{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T19:05:07Z","timestamp":1792004707530,"version":"4.4.0"},"reference-count":271,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T00:00:00Z","timestamp":1770076800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Pharmaceutics"],"abstract":"<jats:p>This review highlights the rapidly evolving role of artificial intelligence (AI) in transforming lung cancer care, with a specific focus on its integrated applications across diagnosis, biomarker discovery, and drug development. The novelty of this work lies in its holistic examination of how AI bridges these traditionally separate domains, from radiology and pathology to genomics and clinical trials, to create a more cohesive and personalized oncology pipeline. We detail how AI algorithms significantly enhance early detection by improving the accuracy and efficiency of pulmonary nodule characterization on computed tomography scans and enable precise cancer subtyping via computational pathology. In biomarker discovery, AI-driven analysis of radiomic features and genomic data facilitates the non-invasive prediction of tumor genotype, PD-L1 expression, and immunotherapy response, moving beyond invasive tissue biopsies. Furthermore, AI is accelerating the drug development lifecycle by identifying novel therapeutic targets and optimizing patient selection for clinical trials. The review also explores AI\u2019s critical role in personalizing treatment regimens, including predicting outcomes for radiotherapy and immunotherapy, thereby tailoring therapy to individual patient profiles. We critically address the challenges of clinical translation, including model interpretability, data standardization, and ethical considerations, which are pivotal for real-world implementation. Finally, we contend that the future of lung cancer management hinges on robust, multi-institutional validation of AI tools and the development of trustworthy, explainable systems.<\/jats:p>","DOI":"10.3390\/pharmaceutics18020201","type":"journal-article","created":{"date-parts":[[2026,2,3]],"date-time":"2026-02-03T16:46:28Z","timestamp":1770137188000},"page":"201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1491-7687","authenticated-orcid":false,"given":"Srikanth","family":"Basety","sequence":"first","affiliation":[{"name":"Tris Pharma, Monmouth Junction, NJ 08852, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Renuka","family":"Gudepu","sequence":"additional","affiliation":[{"name":"Department of Microbiology, Pingle Government College for Women (A), Warangal 506370, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0969-6142","authenticated-orcid":false,"given":"Aditya","family":"Velidandi","sequence":"additional","affiliation":[{"name":"Department of Biotechnology, Vaagdevi Degree and P.G. College, Warangal 506001, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Y., and Yu, Y. (2025). Global, Regional, and National Burden of Ovarian Cancer Attributable to High Body Mass Index, 1990\u20132021: Insights from the Global Burden of Disease Study 2021. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1568716"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1097\/CM9.0000000000003268","article-title":"Global Burden of Lung Cancer in 2022 and Projected Burden in 2050","volume":"137","author":"Guo","year":"2024","journal-title":"Chin. Med. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1583","DOI":"10.1097\/CM9.0000000000002529","article-title":"Global Burden and Trends of Lung Cancer Incidence and Mortality","volume":"136","author":"Li","year":"2023","journal-title":"Chin. Med. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1007\/s10147-021-02108-2","article-title":"Mapping of Global, Regional and National Incidence, Mortality and Mortality-to-Incidence Ratio of Lung Cancer in 2020 and 2050","volume":"27","author":"Sharma","year":"2022","journal-title":"Int. J. Clin. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"785","DOI":"10.3390\/epidemiologia5040053","article-title":"Global, Regional, and National Burden of Tracheal, Bronchus, and Lung Cancer in 2022: Evidence from the GLOBOCAN Study","volume":"5","author":"Sharma","year":"2024","journal-title":"Epidemiologia"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, S. (2023). Adenocarcinoma. Diagnostic Imaging of Lung Cancers, Springer Nature.","DOI":"10.1007\/978-981-99-6815-2"},{"key":"ref_7","first-page":"11","article-title":"Geographical Distribution Global Incidence and Mortality of Lung Cancer and Its Relationship with the Human Development Index (HDI); an Ecology Study in 2018","volume":"6","author":"Goodarzi","year":"2019","journal-title":"World Cancer Res. J."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Ji, X., Chen, J., Ye, J., Xu, S., Lin, B., and Hou, K. (2023). Epidemiological Analysis of Global and Regional Lung Cancer Mortality: Based on 30-Year Data Analysis of Global Burden Disease Database. Healthcare, 11.","DOI":"10.3390\/healthcare11222920"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Chen, X., Mo, S., and Yi, B. (2022). The Spatiotemporal Dynamics of Lung Cancer: 30-Year Trends of Epidemiology across 204 Countries and Territories. BMC Public Health, 22.","DOI":"10.1186\/s12889-022-13281-y"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e43651","DOI":"10.2196\/43651","article-title":"Projections of Lung Cancer Incidence by 2035 in 40 Countries Worldwide: Population-Based Study","volume":"9","author":"Luo","year":"2023","journal-title":"JMIR Public Health Surveill."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bhargav, A.L., and Ashokkumar, C. (2024, January 4). AI-Driven Insights: A Survey on Innovative Approach for Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods: Lung Cancer Prediction Utilizing Machine Learning and Deep Learning Methods. Proceedings of the 2024 3rd International Conference on Automation, Computing and Renewable Systems (ICACRS), Pudukkottai, India.","DOI":"10.1109\/ICACRS62842.2024.10841728"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xiang, Y., Liu, X., Wang, Y., Zheng, D., Meng, Q., Jiang, L., Yang, S., Zhang, S., Zhang, X., and Liu, Y. (2024). Mechanisms of Resistance to Targeted Therapy and Immunotherapy in Non-Small Cell Lung Cancer: Promising Strategies to Overcoming Challenges. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1366260"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"113824","DOI":"10.1016\/j.colsurfb.2024.113824","article-title":"Machine Learning-Based Exosome Profiling of Multi-Receptor SERS Sensors for Differentiating Adenocarcinoma in Situ from Early-Stage Invasive Adenocarcinoma","volume":"236","author":"Lu","year":"2024","journal-title":"Colloids Surf. B Biointerfaces"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"124934","DOI":"10.1016\/j.talanta.2023.124934","article-title":"Ultrasensitive Immunosensor for Multiplex Detection of Cancer Biomarkers Carcinoembryonic Antigen (CEA) and Yamaguchi Sarcoma Viral Oncogene Homolog 1 (YES1) Based on Eco-Friendly Synthesized Gold Nanoparticles","volume":"266","author":"Kiio","year":"2024","journal-title":"Talanta"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e70019","DOI":"10.1002\/cai2.70019","article-title":"Artificial Intelligence-Based Methods: The Path Forward in Achieving Equity in Lung Cancer Screening and Evaluation","volume":"4","author":"Kuperberg","year":"2025","journal-title":"Cancer Innov."},{"key":"ref_16","first-page":"1552","article-title":"Artificial Intelligence and Its Future Potential in Lung Cancer Screening","volume":"19","author":"Mathew","year":"2020","journal-title":"EXCLI J."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1779","DOI":"10.2147\/JMDH.S410301","article-title":"Machine Learning and AI in Cancer Prognosis, Prediction, and Treatment Selection: A Critical Approach","volume":"16","author":"Zhang","year":"2023","journal-title":"J. Multidiscip. Healthc."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Gandhi, Z., Gurram, P., Amgai, B., Lekkala, S.P., Lokhandwala, A., Manne, S., Mohammed, A., Koshiya, H., Dewaswala, N., and Desai, R. (2023). Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes. Cancers, 15.","DOI":"10.3390\/cancers15215236"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1287\/orsc.2021.1549","article-title":"To Engage or Not to Engage with AI for Critical Judgments: How Professionals Deal with Opacity When Using AI for Medical Diagnosis","volume":"33","author":"Lebovitz","year":"2022","journal-title":"Organ. Sci."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.21037\/tlcr-20-708","article-title":"Radiomics and Artificial Intelligence in Lung Cancer Screening","volume":"10","author":"Binczyk","year":"2021","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.crad.2021.03.021","article-title":"Augmenting Lung Cancer Diagnosis on Chest Radiographs: Positioning Artificial Intelligence to Improve Radiologist Performance","volume":"76","author":"Tam","year":"2021","journal-title":"Clin. Radiol."},{"key":"ref_22","first-page":"1318","article-title":"AI And Deep Learning Models for Early Lung Cancer Detection Using Radiological Data","volume":"5","author":"Anusheebha","year":"2025","journal-title":"J. Inform. Educ. Res."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bouamrane, A., Derdour, M., Bennour, A., Elfadil Eisa, T.A., Emara, A.-H.M., Al-Sarem, M., and Kurdi, N.A. (2024). Toward Robust Lung Cancer Diagnosis: Integrating Multiple CT Datasets, Curriculum Learning, and Explainable AI. Diagnostics, 15.","DOI":"10.3390\/diagnostics15010001"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.acra.2023.03.040","article-title":"Predicting EGFR Mutation Status in Non\u2013Small Cell Lung Cancer Using Artificial Intelligence: A Systematic Review and Meta-Analysis","volume":"31","author":"Nguyen","year":"2024","journal-title":"Acad. Radiol."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wasilewski, T., Kamysz, W., and G\u0119bicki, J. (2024). AI-assisted detection of biomarkers by sensors and biosensors for early diagnosis and monitoring. Biosensors, 14.","DOI":"10.3390\/bios14070356"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e18564","DOI":"10.1111\/jcmm.18564","article-title":"Deciphering Lung Adenocarcinoma Prognosis and Immunotherapy Response through an AI-driven Stemness-related Gene Signature","volume":"28","author":"Ye","year":"2024","journal-title":"J. Cell. Mol. Med."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tun, H.M., Rahman, H.A., Naing, L., and Malik, O.A. (2025). Artificial Intelligence Utilization in Cancer Screening Program across ASEAN: A Scoping Review. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-14026-x"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"100051","DOI":"10.1016\/j.acepjo.2025.100051","article-title":"Artificial Intelligence in Emergency Medicine: A Primer for the Nonexpert","volume":"6","author":"Smith","year":"2025","journal-title":"JACEP Open"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1007\/s13311-023-01384-2","article-title":"Machine Learning in Clinical Trials: A Primer with Applications to Neurology","volume":"20","author":"Miller","year":"2023","journal-title":"Neurotherapeutics"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1016\/j.diii.2023.03.002","article-title":"A Primer on Artificial Intelligence in Pancreatic Imaging","volume":"104","author":"Ahmed","year":"2023","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_31","first-page":"89","article-title":"Artificial Intelligence in Musculoskeletal Applications: A Primer for Radiologists","volume":"31","author":"Tong","year":"2024","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Najjar, R. (2023). Redefining Radiology: A Review of Artificial Intelligence Integration in Medical Imaging. Diagnostics, 13.","DOI":"10.20944\/preprints202306.1124.v1"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Maleki Varnosfaderani, S., and Forouzanfar, M. (2024). The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering, 11.","DOI":"10.3390\/bioengineering11040337"},{"key":"ref_34","first-page":"e49890","article-title":"Generative Artificial Intelligence Terminology: A Primer for Clinicians and Medical Researchers","volume":"15","author":"Melnyk","year":"2023","journal-title":"Cureus"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Ye, J., Yao, L., Shen, J., Janarthanam, R., and Luo, Y. (2020). Predicting Mortality in Critically Ill Patients with Diabetes Using Machine Learning and Clinical Notes. BMC Med. Inform. Decis. Mak., 20.","DOI":"10.1186\/s12911-020-01318-4"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"102690","DOI":"10.1016\/j.inffus.2024.102690","article-title":"Review of Multimodal Machine Learning Approaches in Healthcare","volume":"114","author":"Krones","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"ubae011","DOI":"10.1093\/bjrai\/ubae011","article-title":"Artificial Intelligence-Driven Radiomics: Developing Valuable Radiomics Signatures with the Use of Artificial Intelligence","volume":"1","author":"Vrettos","year":"2024","journal-title":"BJR|Artif. Intell."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1038\/s41746-025-01471-y","article-title":"Convergence of Evolving Artificial Intelligence and Machine Learning Techniques in Precision Oncology","volume":"8","author":"Fountzilas","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1038\/s41746-024-01316-0","article-title":"A Primer on Reinforcement Learning in Medicine for Clinicians","volume":"7","author":"Jayaraman","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"445","DOI":"10.2217\/pme-2022-0014","article-title":"Artificial Intelligence in Healthcare: A Primer for Medical Education in Radiomics","volume":"19","author":"Waldman","year":"2022","journal-title":"Per. Med."},{"key":"ref_41","unstructured":"(2025, November 29). Approaches to Regulating Artificial Intelligence: A Primer. Available online: https:\/\/www.ncsl.org\/technology-and-communication\/approaches-to-regulating-artificial-intelligence-a-primer."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.bbi.2023.11.005","article-title":"Machine Learning and Artificial Intelligence in Neuroscience: A Primer for Researchers","volume":"115","author":"Badrulhisham","year":"2024","journal-title":"Brain Behav. Immun."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1002\/cpt.3746","article-title":"Navigating the Real World: A Scoping Review of Structured Frameworks to Effectively Identify, Evaluate, and Select Real-World Data Sources for Fit-for-Purpose Studies","volume":"118","author":"Zebachi","year":"2025","journal-title":"Clin. Pharmacol. Ther."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1038\/s41586-024-08167-5","article-title":"Automated Real-World Data Integration Improves Cancer Outcome Prediction","volume":"636","author":"Jee","year":"2024","journal-title":"Nature"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"10884","DOI":"10.1038\/s41467-024-55251-5","article-title":"Characterizing Mutation-Treatment Effects Using Clinico-Genomics Data of 78,287 Patients with 20 Types of Cancers","volume":"15","author":"Liu","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Chhabra, R. (2024). Molecular and Modular Intricacies of Precision Oncology. Front. Immunol., 15.","DOI":"10.3389\/fimmu.2024.1476494"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Bandaru, V.K.R., Gondi, D.S., Sathish, K., Kanthi Kumar, K., Ramakrishnaiah, N., and Bhutani, M. (2025). Transforming Patient Records into Actionable Insights with Natural Language Processing in Health Care. International Conference on Innovative Computing and Communication, Springer Nature.","DOI":"10.1007\/978-981-96-6906-6_34"},{"key":"ref_48","first-page":"e59954","article-title":"Unveiling the Influence of AI Predictive Analytics on Patient Outcomes: A Comprehensive Narrative Review","volume":"16","author":"Dixon","year":"2024","journal-title":"Cureus"},{"key":"ref_49","first-page":"303","article-title":"The Future of Multimodal Artificial Intelligence Models for Integrating Imaging and Clinical Metadata: A Narrative Review","volume":"31","author":"Simon","year":"2024","journal-title":"Diagn. Interv. Radiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.annonc.2023.10.125","article-title":"Artificial Intelligence for Predictive Biomarker Discovery in Immuno-Oncology: A Systematic Review","volume":"35","author":"Prelaj","year":"2024","journal-title":"Ann. Oncol."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.gene.2019.04.062","article-title":"Machine Learning Technology in the Application of Genome Analysis: A Systematic Review","volume":"705","author":"Wu","year":"2019","journal-title":"Gene"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"6954","DOI":"10.21037\/jtd-2019-cptn-03","article-title":"Putting Artificial Intelligence (AI) on the Spot: Machine Learning Evaluation of Pulmonary Nodules","volume":"12","author":"Tandon","year":"2020","journal-title":"J. Thorac. Dis."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.diii.2022.11.007","article-title":"Artificial Intelligence: A Critical Review of Applications for Lung Nodule and Lung Cancer","volume":"104","author":"Chassagnon","year":"2023","journal-title":"Diagn. Interv. Imaging"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chamberlin, J., Kocher, M.R., Waltz, J., Snoddy, M., Stringer, N.F.C., Stephenson, J., Sahbaee, P., Sharma, P., Rapaka, S., and Schoepf, U.J. (2021). Automated Detection of Lung Nodules and Coronary Artery Calcium Using Artificial Intelligence on Low-Dose CT Scans for Lung Cancer Screening: Accuracy and Prognostic Value. BMC Med., 19.","DOI":"10.1186\/s12916-021-01928-3"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"108","DOI":"10.1097\/RLI.0000000000000814","article-title":"First Performance Evaluation of an Artificial Intelligence-Based Computer-Aided Detection System for Pulmonary Nodule Evaluation in Dual-Source Photon-Counting Detector CT at Different Low-Dose Levels","volume":"57","author":"Jungblut","year":"2022","journal-title":"Investig. Radiol."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.21037\/qims-21-815","article-title":"The Influence of a Deep Learning Image Reconstruction Algorithm on the Image Quality and Auto-Analysis of Pulmonary Nodules at Ultra-Low Dose Chest CT: A Phantom Study","volume":"12","author":"Yao","year":"2022","journal-title":"Quant. Imaging Med. Surg."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"5818423","DOI":"10.1155\/2022\/5818423","article-title":"Diagnostic Value of Artificial Intelligence Based on CT Image in Benign and Malignant Pulmonary Nodules","volume":"2022","author":"Du","year":"2022","journal-title":"J. Oncol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"102027","DOI":"10.1016\/j.media.2021.102027","article-title":"LNDb Challenge on Automatic Lung Cancer Patient Management","volume":"70","author":"Pedrosa","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"e222904","DOI":"10.1148\/radiol.222904","article-title":"Expanding Role of Advanced Image Analysis in CT-Detected Indeterminate Pulmonary Nodules and Early Lung Cancer Characterization","volume":"309","author":"Prosper","year":"2023","journal-title":"Radiology"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2378","DOI":"10.21037\/tlcr-2020-lcs-06","article-title":"Artificial Intelligence for Detection and Characterization of Pulmonary Nodules in Lung Cancer CT Screening: Ready for Practice?","volume":"10","author":"Schreuder","year":"2021","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s11604-022-01359-x","article-title":"Artificial Intelligence in Lung Cancer: Current Applications and Perspectives","volume":"41","author":"Chassagnon","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"105190","DOI":"10.1016\/j.ijmedinf.2023.105190","article-title":"Clinical Application of Radiological AI for Pulmonary Nodule Evaluation: Replicability and Susceptibility to the Population Shift Caused by the COVID-19 Pandemic","volume":"178","author":"Vasilev","year":"2023","journal-title":"Int. J. Med. Inform."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Nagase, W., Harada, K., Kudo, Y., Matsubayashi, J., Takada, I., Park, J., Murakami, K., Ohira, T., Nagao, T., and Taguri, M. (2025). AI-Driven 3D CT Imaging Prediction Model for Improving Preoperative Detection of Visceral Pleural Invasion in Early-Stage Lung Cancer. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0332956"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"1684","DOI":"10.1016\/j.jtho.2021.07.006","article-title":"Validation of the Novel International Association for the Study of Lung Cancer Grading System for Invasive Pulmonary Adenocarcinoma and Association with Common Driver Mutations","volume":"16","author":"Deng","year":"2021","journal-title":"J. Thorac. Oncol."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1016\/j.jtho.2021.04.008","article-title":"Validation Study of the International Association for the Study of Lung Cancer Histologic Grading System of Invasive Lung Adenocarcinoma","volume":"16","author":"Yoshizawa","year":"2021","journal-title":"J. Thorac. Oncol."},{"key":"ref_66","first-page":"500","article-title":"Computational Pathology: What Is the Way Forward?","volume":"Volume 11603","author":"Tomaszewski","year":"2021","journal-title":"Medical Imaging 2021: Digital Pathology"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"103481","DOI":"10.1016\/j.ebiom.2021.103481","article-title":"A Prognostic and Predictive Computational Pathology Image Signature for Added Benefit of Adjuvant Chemotherapy in Early Stage Non-Small-Cell Lung Cancer","volume":"69","author":"Wang","year":"2021","journal-title":"eBioMedicine"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1038\/s41551-020-00682-w","article-title":"Data-Efficient and Weakly Supervised Computational Pathology on Whole-Slide Images","volume":"5","author":"Lu","year":"2021","journal-title":"Nat. Biomed. Eng."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"16123","DOI":"10.1109\/CVPR52688.2022.01567","article-title":"Scaling Vision Transformers to Gigapixel Images via Hierarchical Self-Supervised Learning","volume":"Volume 2022","author":"Chen","year":"2022","journal-title":"Proceedings of the 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)"},{"key":"ref_70","first-page":"3570","article-title":"RLogist: Fast Observation Strategy on Whole-Slide Images with Deep Reinforcement Learning","volume":"37","author":"Zhao","year":"2023","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Tavolara, T.E., Gurcan, M.N., and Niazi, M.K.K. (2022). Contrastive Multiple Instance Learning: An Unsupervised Framework for Learning Slide-Level Representations of Whole Slide Histopathology Images without Labels. Cancers, 14.","DOI":"10.3390\/cancers14235778"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1174","DOI":"10.1038\/s41591-024-02885-z","article-title":"Demographic Bias in Misdiagnosis by Computational Pathology Models","volume":"30","author":"Vaidya","year":"2024","journal-title":"Nat. Med."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1158\/0008-5472.CAN-21-2843","article-title":"Prostate Cancer Risk Stratification via Nondestructive 3D Pathology with Deep Learning\u2013Assisted Gland Analysis","volume":"82","author":"Xie","year":"2022","journal-title":"Cancer Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"102992","DOI":"10.1016\/j.media.2023.102992","article-title":"FFPE++: Improving the Quality of Formalin-Fixed Paraffin-Embedded Tissue Imaging via Contrastive Unpaired Image-to-Image Translation","volume":"91","author":"Kassab","year":"2024","journal-title":"Med. Image Anal."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"101697","DOI":"10.1016\/j.xcrm.2024.101697","article-title":"Next-Generation Lung Cancer Pathology: Development and Validation of Diagnostic and Prognostic Algorithms","volume":"5","author":"Kludt","year":"2024","journal-title":"Cell Rep. Med."},{"key":"ref_76","unstructured":"Li, J., Hu, J., Sun, Q., Yan, R., Ouyang, M., Guan, T., Han, A., He, C., and He, Y. (2025). Can We Simplify Slide-Level Fine-Tuning of Pathology Foundation Models?. arXiv."},{"key":"ref_77","unstructured":"Shao, D., Chen, R.J., Song, A.H., Runevic, J., Lu, M.Y., Ding, T., and Mahmood, F. (2025, January 13\u201319). Do Multiple Instance Learning Models Transfer?. Proceedings of the Forty-Second International Conference on Machine Learning, Vancouver, BC, Canada."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Wang, H., Zhu, Z., Ma, J., Wang, F., Shi, Y., Luo, B., Wang, J., Cai, Q., Zhang, X., and Chen, Y.-W. (2025). Accurate and Scalable Multimodal Pathology Retrieval via Attentive Vision-Language Alignment. arXiv.","DOI":"10.21203\/rs.3.rs-8222041\/v1"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Cherukuri, S.P., Kaur, A., Goyal, B., Kukunoor, H.R., Sahito, A.F., Sachdeva, P., Yerrapragada, G., Elangovan, P., Shariff, M.N., and Natarajan, T. (2025). Artificial Intelligence-Enhanced Liquid Biopsy and Radiomics in Early-Stage Lung Cancer Detection: A Precision Oncology Paradigm. Cancers, 17.","DOI":"10.3390\/cancers17193165"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Kim, G., Moon, S., and Choi, J.-H. (2022). Deep Learning with Multimodal Integration for Predicting Recurrence in Patients with Non-Small Cell Lung Cancer. Sensors, 22.","DOI":"10.3390\/s22176594"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"100933","DOI":"10.1016\/j.xcrm.2023.100933","article-title":"Integration of Artificial Intelligence in Lung Cancer: Rise of the Machine","volume":"4","author":"Ladbury","year":"2023","journal-title":"Cell Rep. Med."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3642","DOI":"10.21037\/tlcr-24-982","article-title":"Development of an AI Model for Predicting Hypoxia Status and Prognosis in Non-Small Cell Lung Cancer Using Multi-Modal Data","volume":"13","author":"Zhou","year":"2024","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"e18750362335415","DOI":"10.2174\/0118750362335415240909061539","article-title":"Deep Learning and MRI Biomarkers for Precise Lung Cancer Cell Detection and Diagnosis","volume":"17","author":"Kumar","year":"2024","journal-title":"Open Bioinform. J."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"385","DOI":"10.30574\/wjarr.2025.25.1.0066","article-title":"Machine Learning Integration for Early-Stage Cancer Detection Using Multi-Modal Imaging Analysis","volume":"25","author":"Toochukwu","year":"2025","journal-title":"World J. Adv. Res. Rev."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1053\/j.semnuclmed.2025.02.006","article-title":"Impact of [18F]FDG PET\/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role","volume":"55","author":"Safarian","year":"2025","journal-title":"Semin. Nucl. Med."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Abdullakutty, F., Akbari, Y., Al-Maadeed, S., Bouridane, A., Talaat, I.M., and Hamoudi, R. (2024). Histopathology in Focus: A Review on Explainable Multi-Modal Approaches for Breast Cancer Diagnosis. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1450103"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Cusumano, G., D\u2019Arrigo, S., Terminella, A., and Lococo, F. (2024). Artificial Intelligence Applications for Thoracic Surgeons: \u201cThe Phenomenal Cosmic Powers of the Magic Lamp\u201d. J. Clin. Med., 13.","DOI":"10.20944\/preprints202405.0759.v1"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1002\/ird3.70013","article-title":"Performance Review of Meta LLaMa 3.1 in Thoracic Imaging and Diagnostics","volume":"3","author":"Lotfian","year":"2025","journal-title":"iRADIOLOGY"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Dunn, B., Pierobon, M., and Wei, Q. (2023). Automated Classification of Lung Cancer Subtypes Using Deep Learning and CT-Scan Based Radiomic Analysis. Bioengineering, 10.","DOI":"10.3390\/bioengineering10060690"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"So, A.C.P., Cheng, D., Aslani, S., Azimbagirad, M., Yamada, D., Dunn, R., Josephides, E., McDowall, E., Henry, A.-R., and Bille, A. (2025). A Deep Learning Lung Cancer Segmentation Pipeline to Facilitate CT-Based Radiomics. medRxiv.","DOI":"10.2139\/ssrn.5313327"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Wang, C., Ma, J., Shao, J., Zhang, S., Li, J., Yan, J., Zhao, Z., Bai, C., Yu, Y., and Li, W. (2022). Non-Invasive Measurement Using Deep Learning Algorithm Based on Multi-Source Features Fusion to Predict PD-L1 Expression and Survival in NSCLC. Front. Immunol., 13.","DOI":"10.3389\/fimmu.2022.828560"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Mahajan, A., Kania, V., Agarwal, U., Ashtekar, R., Shukla, S., Patil, V.M., Noronha, V., Joshi, A., Menon, N., and Kaushal, R.K. (2024). Deep-Learning-Based Predictive Imaging Biomarker Model for EGFR Mutation Status in Non-Small Cell Lung Cancer from CT Imaging. Cancers, 16.","DOI":"10.3390\/cancers16061130"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"8632","DOI":"10.1200\/JCO.2024.42.16_suppl.8632","article-title":"CheckpointPx: A Predictive Radiology AI Model of Immune Checkpoint Inhibitor (ICI) Benefit in Non-Small Cell Lung Cancer (NSCLC)","volume":"42","author":"Lee","year":"2024","journal-title":"J. Clin. Oncol."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Liu, G., Zhang, X., He, Y., Liang, D., Xie, S., Zhang, N., Geng, N., Zhang, L., Huang, Y., and Liu, F. (2025). AI-Based Non-Invasive Profiling of the Tumor Immune Microenvironment Using Longitudinal CT Radiomics Predicts Immunotherapy Response in Lung Cancer. Front. Immunol., 16.","DOI":"10.3389\/fimmu.2025.1664726"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Bahado-Singh, R., Vlachos, K.T., Aydas, B., Gordevicius, J., Radhakrishna, U., and Vishweswaraiah, S. (2022). Precision Oncology: Artificial Intelligence and DNA Methylation Analysis of Circulating Cell-Free DNA for Lung Cancer Detection. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.790645"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"10090","DOI":"10.1038\/s41467-024-53851-9","article-title":"Deep Generative AI Models Analyzing Circulating Orphan Non-Coding RNAs Enable Detection of Early-Stage Lung Cancer","volume":"15","author":"Karimzadeh","year":"2024","journal-title":"Nat. Commun."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.21037\/tlcr-2024-1196","article-title":"Deep Learning in Histopathology Images for Prediction of Oncogenic Driver Molecular Alterations in Lung Cancer: A Systematic Review and Meta-Analysis","volume":"14","author":"Alzate","year":"2025","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1186\/s12943-025-02369-9","article-title":"Current AI Technologies in Cancer Diagnostics and Treatment","volume":"24","author":"Tiwari","year":"2025","journal-title":"Mol. Cancer"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"107879","DOI":"10.1016\/j.cmpb.2023.107879","article-title":"DeepXplainer: An Interpretable Deep Learning Based Approach for Lung Cancer Detection Using Explainable Artificial Intelligence","volume":"243","author":"Wani","year":"2024","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"AlOsaimi, H.M., Alshilash, A.M., Al-Saif, L.K., Bosbait, J.M., Albeladi, R.S., Almutairi, D.R., Alhazzaa, A.A., Alluqmani, T.A., Al Qahtani, S.M., and Almohammadi, S.A. (2025). AI Models for the Identification of Prognostic and Predictive Biomarkers in Lung Cancer: A Systematic Review and Meta-Analysis. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1424647"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1111\/cyt.13396","article-title":"Exploring the Potential of Multiomics Liquid Biopsy Testing in the Clinical Setting of Lung Cancer","volume":"35","author":"Gottardo","year":"2024","journal-title":"Cytopathology"},{"key":"ref_102","doi-asserted-by":"crossref","unstructured":"Esposito Abate, R., Frezzetti, D., Maiello, M.R., Gallo, M., Camerlingo, R., De Luca, A., De Cecio, R., Morabito, A., and Normanno, N. (2020). Next Generation Sequencing-Based Profiling of Cell Free DNA in Patients with Advanced Non-Small Cell Lung Cancer: Advantages and Pitfalls. Cancers, 12.","DOI":"10.3390\/cancers12123804"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/s41698-021-00225-9","article-title":"Genetic Mutation and Biological Pathway Prediction Based on Whole Slide Images in Breast Carcinoma Using Deep Learning","volume":"5","author":"Qu","year":"2021","journal-title":"NPJ Precis. Oncol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"1188","DOI":"10.1016\/j.jtho.2021.03.023","article-title":"Invasive Mucinous Adenocarcinomas with Spatially Separate Lung Lesions: Analysis of Clonal Relationship by Comparative Molecular Profiling","volume":"16","author":"Yang","year":"2021","journal-title":"J. Thorac. Oncol."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"e2131892","DOI":"10.1001\/jamanetworkopen.2021.31892","article-title":"Association of Clinicopathologic and Molecular Tumor Features with Recurrence in Resected Early-Stage Epidermal Growth Factor Receptor\u2013Positive Non\u2013Small Cell Lung Cancer","volume":"4","author":"Saw","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"7081","DOI":"10.1038\/s41467-021-27341-1","article-title":"The Histologic Phenotype of Lung Cancers Is Associated with Transcriptomic Features Rather than Genomic Characteristics","volume":"12","author":"Tang","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1080\/14789450.2021.1984886","article-title":"Laser Capture Proteomics: Spatial Tissue Molecular Profiling from the Bench to Personalized Medicine","volume":"18","author":"Liotta","year":"2021","journal-title":"Expert Rev. Proteom."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1038\/s41416-022-01776-9","article-title":"Integrating Circulating-Free DNA (CfDNA) Analysis into Clinical Practice: Opportunities and Challenges","volume":"127","author":"Makarem","year":"2022","journal-title":"Br. J. Cancer"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"17588359221112696","DOI":"10.1177\/17588359221112696","article-title":"The Economic Value of Liquid Biopsy for Genomic Profiling in Advanced Non-Small Cell Lung Cancer","volume":"14","author":"Ezeife","year":"2022","journal-title":"Ther. Adv. Med. Oncol."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"899","DOI":"10.4143\/crt.2024.1184","article-title":"ALK Inhibition in a Patient with Inflammatory Myofibroblastic Tumor Harboring CARS1-ALK Fusion","volume":"57","author":"Choi","year":"2025","journal-title":"Cancer Res. Treat."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1038\/s41586-023-06303-1","article-title":"Therapy-Induced APOBEC3A Drives Evolution of Persistent Cancer Cells","volume":"620","author":"Isozaki","year":"2023","journal-title":"Nature"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"7402","DOI":"10.1158\/1538-7445.AM2024-7402","article-title":"Abstract 7402: Foundation AI Models Predict Molecular Measurements of Tumor Purity","volume":"84","author":"Gerardin","year":"2024","journal-title":"Cancer Res."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"115609","DOI":"10.1016\/j.ejca.2025.115609","article-title":"The Correlation of Liquid Biopsy Genomic Data to Radiomics in Colon, Pancreatic, Lung and Prostatic Cancer Patients","volume":"226","author":"Italiano","year":"2025","journal-title":"Eur. J. Cancer"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"734","DOI":"10.20892\/j.issn.2095-3941.2020.0397","article-title":"A Novel Recurrence-Associated Metabolic Prognostic Model for Risk Stratification and Therapeutic Response Prediction in Patients with Stage I Lung Adenocarcinoma","volume":"18","author":"Liu","year":"2021","journal-title":"Cancer Biol. Med."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Cellina, M., C\u00e8, M., Irmici, G., Ascenti, V., Khenkina, N., Toto-Brocchi, M., Martinenghi, C., Papa, S., and Carrafiello, G. (2022). Artificial Intelligence in Lung Cancer Imaging: Unfolding the Future. Diagnostics, 12.","DOI":"10.3390\/diagnostics12112644"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"8549","DOI":"10.1200\/JCO.2022.40.16_suppl.8549","article-title":"The Use of Artificial Intelligence with Uncertainty Estimation to Predict Lung Cancer Relapse from Histopathology","volume":"40","author":"Dolezal","year":"2022","journal-title":"J. Clin. Oncol."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"1575","DOI":"10.21037\/tlcr-23-5","article-title":"Review of the Use of Radiomics to Assess the Risk of Recurrence in Early-Stage Non-Small Cell Lung Cancer","volume":"12","author":"Libling","year":"2023","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"2890","DOI":"10.1158\/1078-0432.CCR-20-4769","article-title":"Clinical Impact of Presurgery Circulating Tumor DNA after Total Neoadjuvant Treatment in Locally Advanced Rectal Cancer: A Biomarker Study from the GEMCAD 1402 Trial","volume":"27","author":"Vidal","year":"2021","journal-title":"Clin. Cancer Res."},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Mansur, A., Vrionis, A., Charles, J.P., Hancel, K., Panagides, J.C., Moloudi, F., Iqbal, S., and Daye, D. (2023). The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities. Cancers, 15.","DOI":"10.3390\/cancers15112928"},{"key":"ref_120","doi-asserted-by":"crossref","unstructured":"To, K.K.W., Fong, W., and Cho, W.C.S. (2021). Immunotherapy in Treating EGFR-Mutant Lung Cancer: Current Challenges and New Strategies. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.635007"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"e42667","DOI":"10.1097\/MD.0000000000042667","article-title":"Predicting Thyroid Cancer Recurrence Using Supervised CatBoost: A SHAP-Based Explainable AI Approach","volume":"104","author":"Hanani","year":"2025","journal-title":"Medicine"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1007\/s13193-024-02079-6","article-title":"A Holistic Approach to Implementing Artificial Intelligence in Lung Cancer","volume":"16","author":"HaghighiKian","year":"2025","journal-title":"Indian J. Surg. Oncol."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Xia, C., Liu, M., Li, X., Zhang, H., Li, X., Wu, D., Ren, D., Hua, Y., Dong, M., and Liu, H. (2021). Prediction Model for Lung Cancer in High-Risk Nodules Being Considered for Resection: Development and Validation in a Chinese Population. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.700179"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"100640","DOI":"10.1016\/j.jbo.2024.100640","article-title":"AI Diagnostics in Bone Oncology for Predicting Bone Metastasis in Lung Cancer Patients Using DenseNet-264 Deep Learning Model and Radiomics","volume":"48","author":"Zeng","year":"2024","journal-title":"J. Bone Oncol."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Nibid, L., Greco, C., Cordelli, E., Sabarese, G., Fiore, M., Liu, C.Z., Ippolito, E., Sicilia, R., Miele, M., and Tortora, M. (2023). Deep Pathomics: A New Image-Based Tool for Predicting Response to Treatment in Stage III Non-Small Cell Lung Cancer. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0294259"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"10732748251351754","DOI":"10.1177\/10732748251351754","article-title":"Peritumoral Radiomic Features on CT for Differential Diagnosis in Small-Cell Lung Cancer: Potential for Surgical Decision-Making","volume":"32","author":"Lin","year":"2025","journal-title":"Cancer Control"},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"624","DOI":"10.21037\/tcr-22-2593","article-title":"Advances in the Prediction of Spread through Air Spaces with Imaging in Lung Cancer: A Narrative Review","volume":"12","author":"Wang","year":"2023","journal-title":"Transl. Cancer Res."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"2387","DOI":"10.21037\/jtd-21-1825","article-title":"A Guide for Managing Patients with Stage I NSCLC: Deciding between Lobectomy, Segmentectomy, Wedge, SBRT and Ablation\u2014Part 3: Systematic Review of Evidence Regarding Surgery in Compromised Patients or Specific Tumors","volume":"14","author":"Bade","year":"2022","journal-title":"J. Thorac. Dis."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"1358","DOI":"10.1007\/s00595-022-02464-0","article-title":"Surgical Strategies for Patients with Second Primary Non-Small Cell Lung Cancer Lesions 2 Cm or Less in Diameter","volume":"52","author":"Wang","year":"2022","journal-title":"Surg. Today"},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"471","DOI":"10.1186\/s13019-024-02922-5","article-title":"Surgical Intervention for Lung Cancer in Patients Aged 75 and above: Potential Associations with Increased Mortality Rates\u2014A Single-Center Observational Study","volume":"19","author":"Kaprin","year":"2024","journal-title":"J. Cardiothorac. Surg."},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"e231","DOI":"10.1016\/j.cllc.2021.12.012","article-title":"Association of Operability with Post-Treatment Mortality in Early-Stage Non-Small Cell Lung Cancer","volume":"23","author":"Stokes","year":"2022","journal-title":"Clin. Lung Cancer"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1338","DOI":"10.1093\/jnci\/djac108","article-title":"Digital Health Applications in Oncology: An Opportunity to Seize","volume":"114","author":"Parikh","year":"2022","journal-title":"JNCI J. Natl. Cancer Inst."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"103314","DOI":"10.1016\/j.drudis.2022.06.014","article-title":"Wearable Smart Devices in Cancer Diagnosis and Remote Clinical Trial Monitoring: Transforming the Healthcare Applications","volume":"27","author":"Beg","year":"2022","journal-title":"Drug Discov. Today"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Scarpa, J., Hussain, I., Cheng, A., Villena-Vargas, J., and Boyer, R. (2025). Next Generation Performance Status: Digital Health Technologies across the Lung Cancer Continuum. Front. Digit. Health, 7.","DOI":"10.3389\/fdgth.2025.1558180"},{"key":"ref_135","doi-asserted-by":"crossref","unstructured":"Das, S., Dey, M.K., Devireddy, R., and Gartia, M.R. (2023). Biomarkers in Cancer Detection, Diagnosis, and Prognosis. Sensors, 24.","DOI":"10.3390\/s24010037"},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s42234-025-00173-w","article-title":"Revolutionizing Breast Cancer Immunotherapy by Integrating AI and Nanotechnology Approaches: Review of Current Applications and Future Directions","volume":"11","author":"Bendani","year":"2025","journal-title":"Bioelectron. Med."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1186\/s13063-021-05489-x","article-title":"The Role of Machine Learning in Clinical Research: Transforming the Future of Evidence Generation","volume":"22","author":"Weissler","year":"2021","journal-title":"Trials"},{"key":"ref_138","unstructured":"(2025, November 30). American Thoracic Society|ATS 2025 International Conference. Available online: https:\/\/site.thoracic.org\/events\/ats-2025-international-conference."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"94705","DOI":"10.1109\/ACCESS.2023.3309711","article-title":"Biomedical Image Analysis for Colon and Lung Cancer Detection Using Tuna Swarm Algorithm with Deep Learning Model","volume":"11","author":"Obayya","year":"2023","journal-title":"IEEE Access"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"106544","DOI":"10.1016\/j.compbiomed.2023.106544","article-title":"An Explainable AI-Driven Biomarker Discovery Framework for Non-Small Cell Lung Cancer Classification","volume":"153","author":"Dwivedi","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"114486","DOI":"10.1016\/j.biopha.2023.114486","article-title":"Discovery of Drug Targets and Therapeutic Agents Based on Drug Repositioning to Treat Lung Adenocarcinoma","volume":"161","author":"Graves","year":"2023","journal-title":"Biomed. Pharmacother."},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1007\/s12032-022-01924-4","article-title":"Machine Learning Driven Drug Repurposing Strategy for Identification of Potential RET Inhibitors against Non-Small Cell Lung Cancer","volume":"40","author":"Ramesh","year":"2022","journal-title":"Med. Oncol."},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"4689","DOI":"10.1007\/s00210-024-03625-3","article-title":"Harnessing Network Pharmacology in Drug Discovery: An Integrated Approach","volume":"398","author":"Joshi","year":"2025","journal-title":"Naunyn-Schmiedeberg\u2019s Arch. Pharmacol."},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Hwang, S.-H., Lee, S., Park, J.Y., Jeon, J.S., Cho, Y.-J., and Kim, S. (2021). Potential of Drug Efficacy Evaluation in Lung and Kidney Cancer Models Using Organ-on-a-Chip Technology. Micromachines, 12.","DOI":"10.3390\/mi12020215"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"9667","DOI":"10.7150\/thno.62676","article-title":"Tumor Microenvironment-Based Screening Repurposes Drugs Targeting Cancer Stem Cells and Cancer-Associated Fibroblasts","volume":"11","author":"Lee","year":"2021","journal-title":"Theranostics"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1186\/s12935-023-03176-2","article-title":"Artificial Intelligence-Driven New Drug Discovery Targeting Serine\/Threonine Kinase 33 for Cancer Treatment","volume":"23","author":"Tran","year":"2023","journal-title":"Cancer Cell Int."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"107356","DOI":"10.1016\/j.compbiomed.2023.107356","article-title":"Artificial Intelligence in Cancer Diagnosis and Therapy: Current Status and Future Perspective","volume":"165","author":"Sufyan","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_148","doi-asserted-by":"crossref","unstructured":"Huang, D., Li, Z., Jiang, T., Yang, C., and Li, N. (2024). Artificial Intelligence in Lung Cancer: Current Applications, Future Perspectives, and Challenges. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1486310"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"e3","DOI":"10.1017\/erm.2021.5","article-title":"Biological Bases of Cancer Immunotherapy","volume":"23","author":"Pinto","year":"2021","journal-title":"Expert Rev. Mol. Med."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.semcancer.2022.08.002","article-title":"Artificial Intelligence-Based Prediction of Clinical Outcome in Immunotherapy and Targeted Therapy of Lung Cancer","volume":"86","author":"Yin","year":"2022","journal-title":"Semin. Cancer Biol."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"pgad133","DOI":"10.1093\/pnasnexus\/pgad133","article-title":"Bayesian Network Enables Interpretable and State-of-the-Art Prediction of Immunotherapy Responses in Cancer Patients","volume":"2","author":"Hozumi","year":"2023","journal-title":"PNAS Nexus"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Ayasa, Y., Alajrami, D., Idkedek, M., Tahayneh, K., and Akar, F.A. (2025). The Impact of Artificial Intelligence on Lung Cancer Diagnosis and Personalized Treatment. Int. J. Mol. Sci., 26.","DOI":"10.3390\/ijms26178472"},{"key":"ref_153","doi-asserted-by":"crossref","unstructured":"Abbaker, N., Minervini, F., Guttadauro, A., Solli, P., Cioffi, U., and Scarci, M. (2024). The Future of Artificial Intelligence in Thoracic Surgery for Non-Small Cell Lung Cancer Treatment a Narrative Review. Front. Oncol., 14.","DOI":"10.3389\/fonc.2024.1347464"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"10732748221077351","DOI":"10.1177\/10732748221077351","article-title":"Precision Medicine in Lung Cancer Theranostics: Paving the Way from Traditional Technology to Advance Era","volume":"29","author":"Guan","year":"2022","journal-title":"Cancer Control"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"454","DOI":"10.1186\/s12935-021-02165-7","article-title":"The Current Issues and Future Perspective of Artificial Intelligence for Developing New Treatment Strategy in Non-Small Cell Lung Cancer: Harmonization of Molecular Cancer Biology and Artificial Intelligence","volume":"21","author":"Tanaka","year":"2021","journal-title":"Cancer Cell Int."},{"key":"ref_156","doi-asserted-by":"crossref","unstructured":"Tankyevych, O., Trousset, F., Latappy, C., Berraho, M., Dutilh, J., Tasu, J.P., Lamour, C., and Cheze Le Rest, C. (2022). Development of Radiomic-Based Model to Predict Clinical Outcomes in Non-Small Cell Lung Cancer Patients Treated with Immunotherapy. Cancers, 14.","DOI":"10.3390\/cancers14235931"},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.ejca.2022.04.011","article-title":"Artificial Intelligence\u2013Powered Programmed Death Ligand 1 Analyser Reduces Interobserver Variation in Tumour Proportion Score for Non\u2013Small Cell Lung Cancer with Better Prediction of Immunotherapy Response","volume":"170","author":"Choi","year":"2022","journal-title":"Eur. J. Cancer"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1038\/s41698-025-00939-0","article-title":"Computationally Integrating Radiology and Pathology Image Features for Predicting Treatment Benefit and Outcome in Lung Cancer","volume":"9","author":"Vaidya","year":"2025","journal-title":"NPJ Precis. Oncol."},{"key":"ref_159","doi-asserted-by":"crossref","unstructured":"Prelaj, A., Galli, E.G., Miskovic, V., Pesenti, M., Viscardi, G., Pedica, B., Mazzeo, L., Bottiglieri, A., Provenzano, L., and Spagnoletti, A. (2023). Real-World Data to Build Explainable Trustworthy Artificial Intelligence Models for Prediction of Immunotherapy Efficacy in NSCLC Patients. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.1078822"},{"key":"ref_160","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.csbj.2023.12.001","article-title":"Inflammatory Response Signature Score Model for Predicting Immunotherapy Response and Pan-Cancer Prognosis","volume":"23","author":"Chen","year":"2024","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"100002","DOI":"10.1016\/j.immuno.2021.100002","article-title":"Integrating Single Cell Sequencing with a Spatial Quantitative Systems Pharmacology Model SpQSP for Personalized Prediction of Triple-Negative Breast Cancer Immunotherapy Response","volume":"1\u20132","author":"Zhang","year":"2021","journal-title":"ImmunoInformatics"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"6987","DOI":"10.1002\/mp.15260","article-title":"Automatic Segmentation of Organs-at-risks of Nasopharynx Cancer and Lung Cancer by Cross-layer Attention Fusion Network with TELD-Loss","volume":"48","author":"Liu","year":"2021","journal-title":"Med. Phys."},{"key":"ref_163","doi-asserted-by":"crossref","first-page":"23058","DOI":"10.1038\/s41598-025-08116-w","article-title":"AI-Driven Genetic Algorithm-Optimized Lung Segmentation for Precision in Early Lung Cancer Diagnosis","volume":"15","author":"Said","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_164","doi-asserted-by":"crossref","unstructured":"Jiang, S., Xue, Y., Li, M., Yang, C., Zhang, D., Wang, Q., Wang, J., Chen, J., You, J., and Yuan, Z. (2022). Artificial Intelligence-Based Automated Treatment Planning of Postmastectomy Volumetric Modulated Arc Radiotherapy. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.871871"},{"key":"ref_165","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1007\/s00066-023-02126-1","article-title":"Novel In-House Knowledge-Based Automated Planning System for Lung Cancer Treated with Intensity-Modulated Radiotherapy","volume":"200","author":"Shao","year":"2024","journal-title":"Strahlenther. Onkol."},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"102137","DOI":"10.1016\/j.artmed.2021.102137","article-title":"Deep Reinforcement Learning for Fractionated Radiotherapy in Non-Small Cell Lung Carcinoma","volume":"119","author":"Tortora","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref_167","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1002\/acm2.13461","article-title":"A Novel CRT-IMRT-combined (Co-CRIM) Planning Technique for Peripheral Lung Stereotactic Body Radiotherapy in Pinnacle Treatment Planning System","volume":"22","author":"Duan","year":"2021","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Fjellanger, K., Hysing, L.B., Heijmen, B.J.M., Pettersen, H.E.S., Sandvik, I.M., Sulen, T.H., Breedveld, S., and Rossi, L. (2021). Enhancing Radiotherapy for Locally Advanced Non-Small Cell Lung Cancer Patients with ICE, a Novel System for Automated Multi-Criterial Treatment Planning Including Beam Angle Optimization. Cancers, 13.","DOI":"10.3390\/cancers13225683"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Tang, F.-H., Fong, Y.-W., Yung, S.-H., Wong, C.-K., Tu, C.-L., and Chan, M.-T. (2023). Radiomics-Clinical AI Model with Probability Weighted Strategy for Prognosis Prediction in Non-Small Cell Lung Cancer. Biomedicines, 11.","DOI":"10.20944\/preprints202306.0353.v1"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"e70340","DOI":"10.1002\/acm2.70340","article-title":"Prediction of Lung Dose-volume Parameters of the Patients with Esophageal Cancer Undergoing Radiotherapy Based on Artificial Neural Network","volume":"26","author":"Li","year":"2025","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Ma, C., Wang, X., Nie, K., Xiong, Z., Xu, K., Yue, N., and Zhang, Y. (2025). Recent Technical Advancements and Clinical Applications of MR-Guided Radiotherapy in Lung Cancer Treatment. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1622060"},{"key":"ref_172","unstructured":"Gao, R., Diallo, M., Liu, H., Magliari, A., Sackett, J., Verbakel, W., Meyers, S., Mcbeth, R., Zarepisheh, M., and Arberet, S. (2025). Automating High Quality RT Planning at Scale. arXiv."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1038\/s41467-024-55259-x","article-title":"Intricacies of Human\u2013AI Interaction in Dynamic Decision-Making for Precision Oncology","volume":"16","author":"Niraula","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1097\/CCO.0000000000001199","article-title":"Artificial Intelligence in Lung Cancer: From Diagnosis to Therapy","volume":"38","author":"Wang","year":"2025","journal-title":"Curr. Opin. Oncol."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"155002","DOI":"10.1088\/1361-6560\/adf02c","article-title":"Automated Radiotherapy Treatment Planning Guided by GPT-4Vision","volume":"70","author":"Liu","year":"2025","journal-title":"Phys. Med. Biol."},{"key":"ref_176","doi-asserted-by":"crossref","first-page":"6592","DOI":"10.1200\/JCO.2021.39.15_suppl.6592","article-title":"Phenotyping of Clinical Trial Eligibility Text from Cancer Studies into Computable Criteria in Electronic Health Records","volume":"39","author":"Mai","year":"2021","journal-title":"J. Clin. Oncol."},{"key":"ref_177","doi-asserted-by":"crossref","first-page":"25387","DOI":"10.1038\/s41598-024-77447-x","article-title":"CriteriaMapper: Establishing the Automatic Identification of Clinical Trial Cohorts from Electronic Health Records by Matching Normalized Eligibility Criteria and Patient Clinical Characteristics","volume":"14","author":"Lee","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"7397","DOI":"10.1007\/s00330-024-10804-6","article-title":"Standalone Deep Learning versus Experts for Diagnosis Lung Cancer on Chest Computed Tomography: A Systematic Review","volume":"34","author":"Wang","year":"2024","journal-title":"Eur. Radiol."},{"key":"ref_179","doi-asserted-by":"crossref","first-page":"1067","DOI":"10.1158\/1538-7445.AM2023-1067","article-title":"Abstract 1067: MatchMiner: An Open-Source AI Precision Medicine Trial Matching Platform","volume":"83","author":"Klein","year":"2023","journal-title":"Cancer Res."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"S864","DOI":"10.1016\/j.jtho.2021.08.066","article-title":"OA10.03 How LungMATCH, A Personalized Treatment Navigation and Clinical Trial Matching Service, Affects the Treatment Journey","volume":"16","author":"Saez","year":"2021","journal-title":"J. Thorac. Oncol."},{"key":"ref_181","doi-asserted-by":"crossref","unstructured":"Cesario, A., Simone, I., Paris, I., Boldrini, L., Orlandi, A., Franceschini, G., Lococo, F., Bria, E., Magno, S., and Mul\u00e8, A. (2021). Development of a Digital Research Assistant for the Management of Patients\u2019 Enrollment in Oncology Clinical Trials within a Research Hospital. J. Pers. Med., 11.","DOI":"10.3390\/jpm11040244"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1038\/s41379-022-01119-2","article-title":"Association of Artificial Intelligence-Powered and Manual Quantification of Programmed Death-Ligand 1 (PD-L1) Expression with Outcomes in Patients Treated with Nivolumab \u00b1 Ipilimumab","volume":"35","author":"Baxi","year":"2022","journal-title":"Mod. Pathol."},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"8535","DOI":"10.1200\/JCO.2025.43.16_suppl.8535","article-title":"A Large Validation Study of AI-Powered PD-L1 Analyzer Compared to Pathologists\u2019 Assessment of PD-L1 Expression in Lung Cancer","volume":"43","author":"Zenke","year":"2025","journal-title":"J. Clin. Oncol."},{"key":"ref_184","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1200\/JCO.2021.39.15_suppl.106","article-title":"Artificial Intelligence (AI)\u2013Powered Pathologic Response (PathR) Assessment of Resection Specimens after Neoadjuvant Atezolizumab in Patients with Non-Small Cell Lung Cancer: Results from the LCMC3 Study","volume":"39","author":"Dacic","year":"2021","journal-title":"J. Clin. Oncol."},{"key":"ref_185","doi-asserted-by":"crossref","first-page":"8536","DOI":"10.1200\/JCO.2025.43.16_suppl.8536","article-title":"Artificial Intelligence-Powered Spatial Analysis of Tumor Microenvironment in Non-Small Cell Lung Cancer Patients Who Acquired Resistance after EGFR Tyrosine Kinase Inhibitors","volume":"43","author":"Bang","year":"2025","journal-title":"J. Clin. Oncol."},{"key":"ref_186","doi-asserted-by":"crossref","first-page":"3653","DOI":"10.1158\/1538-7445.AM2025-3653","article-title":"Abstract 3653: Comparison between Rule-Based, AI-Powered and Manual Clinical Trial Matching Using OncoSolver, a Newly-Developed Clinical Decision Support Tool","volume":"85","author":"Crimini","year":"2025","journal-title":"Cancer Res."},{"key":"ref_187","doi-asserted-by":"crossref","first-page":"107536","DOI":"10.1016\/j.lungcan.2024.107536","article-title":"Survival Benefit in EGFR-Wild and ALK Negative NSCLC Patients Who Participate in Clinical Trials Compared to Standard-of-Care: Propensity-Matched Analysis","volume":"190","author":"Jung","year":"2024","journal-title":"Lung Cancer"},{"key":"ref_188","doi-asserted-by":"crossref","first-page":"167680","DOI":"10.1016\/j.bbadis.2025.167680","article-title":"An In-Depth Review of AI-Powered Advancements in Cancer Drug Discovery","volume":"1871","author":"Le","year":"2025","journal-title":"Biochim. Biophys. Acta-Mol. Basis Dis."},{"key":"ref_189","first-page":"e79199","article-title":"Artificial Intelligence and Early Detection of Breast, Lung, and Colon Cancer: A Narrative Review","volume":"17","author":"Debellotte","year":"2025","journal-title":"Cureus"},{"key":"ref_190","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1038\/s41746-024-01073-0","article-title":"Digital Twins for Health: A Scoping Review","volume":"7","author":"Katsoulakis","year":"2024","journal-title":"NPJ Digit. Med."},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"bbaf237","DOI":"10.1093\/bib\/bbaf237","article-title":"Advances and Critical Aspects in Cancer Treatment Development Using Digital Twins","volume":"26","author":"Bouriga","year":"2025","journal-title":"Brief. Bioinform."},{"key":"ref_192","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1186\/s40537-025-01280-w","article-title":"Digital Twins in Healthcare: A Review of AI-Powered Practical Applications across Health Domains","volume":"12","author":"Elgammal","year":"2025","journal-title":"J. Big Data"},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"100042","DOI":"10.1016\/j.engmed.2024.100042","article-title":"The Status Quo and Future Prospects of Digital Twins for Healthcare","volume":"1","author":"Li","year":"2024","journal-title":"EngMedicine"},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"100340","DOI":"10.1016\/j.hcc.2025.100340","article-title":"Digital Twins in Healthcare IoT: A Systematic Review","volume":"5","author":"Kabir","year":"2025","journal-title":"High-Confid. Comput."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"108804","DOI":"10.1016\/j.cmpb.2025.108804","article-title":"A Systematic Review of AI as a Digital Twin for Prostate Cancer Care","volume":"268","author":"John","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1186\/s12967-025-06371-z","article-title":"From Virtual to Reality: Innovative Practices of Digital Twins in Tumor Therapy","volume":"23","author":"Shen","year":"2025","journal-title":"J. Transl. Med."},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"25006","DOI":"10.1038\/s41598-024-75876-2","article-title":"Automating Cancer Diagnosis Using Advanced Deep Learning Techniques for Multi-Cancer Image Classification","volume":"14","author":"Kumar","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1038\/s41746-022-00690-x","article-title":"Moving towards Vertically Integrated Artificial Intelligence Development","volume":"5","author":"Zhang","year":"2022","journal-title":"NPJ Digit. Med."},{"key":"ref_199","unstructured":"Han, C., Pan, X., Yan, L., Lin, H., Li, B., Yao, S., Lv, S., Shi, Z., Mai, J., and Lin, J. (2022). WSSS4LUAD: Grand Challenge on Weakly-Supervised Tissue Semantic Segmentation for Lung Adenocarcinoma. arXiv."},{"key":"ref_200","doi-asserted-by":"crossref","unstructured":"Jan\u00dfen, C., Boskamp, T., Le\u2019Clerc Arrastia, J., Otero Baguer, D., Hauberg-Lotte, L., Kriegsmann, M., Kriegsmann, K., Steinbu\u00df, G., Casadonte, R., and Kriegsmann, J. (2022). Multimodal Lung Cancer Subtyping Using Deep Learning Neural Networks on Whole Slide Tissue Images and MALDI MSI. Cancers, 14.","DOI":"10.3390\/cancers14246181"},{"key":"ref_201","doi-asserted-by":"crossref","first-page":"63","DOI":"10.37547\/ijmsphr\/Volume06Issue05-04","article-title":"Deep Learning Meets Early Diagnosis: A Hybrid CNN-DNN Framework for Lung Cancer Prediction and Clinical Translation","volume":"6","author":"Tusher","year":"2025","journal-title":"Int. J. Med. Sci. Public Health Res."},{"key":"ref_202","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.21037\/tlcr-20-591","article-title":"A Narrative Review of Digital Pathology and Artificial Intelligence: Focusing on Lung Cancer","volume":"9","author":"Sakamoto","year":"2020","journal-title":"Transl. Lung Cancer Res."},{"key":"ref_203","doi-asserted-by":"crossref","unstructured":"Xing, W., Sun, H., Yan, C., Zhao, C., Wang, D., Li, M., and Ma, J. (2021). A Prediction Model Based on DNA Methylation Biomarkers and Radiological Characteristics for Identifying Malignant from Benign Pulmonary Nodules. BMC Cancer, 21.","DOI":"10.1186\/s12885-021-08002-4"},{"key":"ref_204","doi-asserted-by":"crossref","unstructured":"Ye, M., Tong, L., Zheng, X., Wang, H., Zhou, H., Zhu, X., Zhou, C., Zhao, P., Wang, Y., and Wang, Q. (2022). A Classifier for Improving Early Lung Cancer Diagnosis Incorporating Artificial Intelligence and Liquid Biopsy. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.853801"},{"key":"ref_205","doi-asserted-by":"crossref","unstructured":"Chen, X., Xu, H., Qi, Q., Sun, C., Jin, J., Zhao, H., Wang, X., Weng, W., Wang, S., and Sui, X. (2022). AI-Based Chest CT Semantic Segmentation Algorithm Enables Semi-Automated Lung Cancer Surgery Planning by Recognizing Anatomical Variants of Pulmonary Vessels. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.1021084"},{"key":"ref_206","doi-asserted-by":"crossref","first-page":"15330338221136724","DOI":"10.1177\/15330338221136724","article-title":"Construction and Validation of Early Warning Model of Lung Cancer Based on Machine Learning: A Retrospective Study","volume":"21","author":"Ye","year":"2022","journal-title":"Technol. Cancer Res. Treat."},{"key":"ref_207","doi-asserted-by":"crossref","first-page":"124181","DOI":"10.1016\/j.saa.2024.124181","article-title":"SERS-Based AI Diagnosis of Lung and Gastric Cancer via Exhaled Breath","volume":"314","author":"Xie","year":"2024","journal-title":"Spectrochim. Acta Part A Mol. Biomol. Spectrosc."},{"key":"ref_208","first-page":"40","article-title":"Classification of Benign and Malignancy in Lung Cancer Using Capsule Networks with Dynamic Routing Algorithm on Computed Tomography Images","volume":"4","author":"Bushara","year":"2023","journal-title":"J. Artif. Intell. Technol."},{"key":"ref_209","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1038\/s41591-021-01620-2","article-title":"Artificial Intelligence for Diagnosis and Gleason Grading of Prostate Cancer: The PANDA Challenge","volume":"28","author":"Bulten","year":"2022","journal-title":"Nat. Med."},{"key":"ref_210","doi-asserted-by":"crossref","first-page":"7136","DOI":"10.1038\/s41598-024-57067-1","article-title":"Comparing Deep Learning and Pathologist Quantification of Cell-Level PD-L1 Expression in Non-Small Cell Lung Cancer Whole-Slide Images","volume":"14","author":"Spronck","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_211","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1007\/s00261-022-03419-2","article-title":"Deep Learning-Based Artificial Intelligence for Prostate Cancer Detection at Biparametric MRI","volume":"47","author":"Mehralivand","year":"2022","journal-title":"Abdom. Radiol."},{"key":"ref_212","doi-asserted-by":"crossref","first-page":"100327","DOI":"10.1016\/j.modpat.2023.100327","article-title":"Artificial Intelligence\u2013Based Tool for Tumor Detection and Quantitative Tissue Analysis in Colorectal Specimens","volume":"36","author":"Griem","year":"2023","journal-title":"Mod. Pathol."},{"key":"ref_213","first-page":"1","article-title":"Challenges and Promotional Strategies for the Clinical Translation of Fractal Theory in Medical Diagnosis","volume":"1","author":"Wang","year":"2025","journal-title":"Sci. Technol. Eng. Chem. Environ. Prot."},{"key":"ref_214","doi-asserted-by":"crossref","first-page":"102410","DOI":"10.1016\/j.ctrv.2022.102410","article-title":"Artificial Intelligence for Prediction of Treatment Outcomes in Breast Cancer: Systematic Review of Design, Reporting Standards, and Bias","volume":"108","author":"Corti","year":"2022","journal-title":"Cancer Treat. Rev."},{"key":"ref_215","doi-asserted-by":"crossref","unstructured":"Zheng, J., Lin, X., and Li, M. (2026). Artificial intelligence-based magnetic resonance imaging for preoperative staging of patients with endometrial cancer: A systematic review and meta-analysis. Front. Oncol., 15.","DOI":"10.3389\/fonc.2025.1673060"},{"key":"ref_216","doi-asserted-by":"crossref","first-page":"109370","DOI":"10.1016\/j.compeleceng.2024.109370","article-title":"A Review of Explainable Artificial Intelligence in Healthcare","volume":"118","author":"Sadeghi","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"ref_217","doi-asserted-by":"crossref","unstructured":"Saarela, M., and Podgorelec, V. (2024). Recent Applications of Explainable AI (XAI): A Systematic Literature Review. Appl. Sci., 14.","DOI":"10.3390\/app14198884"},{"key":"ref_218","doi-asserted-by":"crossref","first-page":"106668","DOI":"10.1016\/j.compbiomed.2023.106668","article-title":"Survey of Explainable Artificial Intelligence Techniques for Biomedical Imaging with Deep Neural Networks","volume":"156","author":"Nazir","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_219","doi-asserted-by":"crossref","first-page":"101587","DOI":"10.1016\/j.imu.2024.101587","article-title":"A Survey of Explainable Artificial Intelligence in Healthcare: Concepts, Applications, and Challenges","volume":"51","author":"Mienye","year":"2024","journal-title":"Inform. Med. Unlocked"},{"key":"ref_220","doi-asserted-by":"crossref","unstructured":"Mahmood, S., Hasan, R., Hussain, S., and Adhikari, R. (2025). An Interpretable and Generalizable Machine Learning Model for Predicting Asthma Outcomes: Integrating AutoML and Explainable AI Techniques. World, 6.","DOI":"10.3390\/world6010015"},{"key":"ref_221","doi-asserted-by":"crossref","first-page":"e70018","DOI":"10.1002\/widm.70018","article-title":"Unveiling Explainable AI in Healthcare: Current Trends, Challenges, and Future Directions","volume":"15","author":"Noor","year":"2025","journal-title":"WIREs Data Min. Knowl. Discov."},{"key":"ref_222","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s44230-023-00038-y","article-title":"Survey on Explainable AI: From Approaches, Limitations and Applications Aspects","volume":"3","author":"Yang","year":"2023","journal-title":"Hum.-Centric Intell. Syst."},{"key":"ref_223","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1186\/s13000-025-01686-3","article-title":"Fostering Trust and Interpretability: Integrating Explainable AI (XAI) with Machine Learning for Enhanced Disease Prediction and Decision Transparency","volume":"20","author":"Agrawal","year":"2025","journal-title":"Diagn. Pathol."},{"key":"ref_224","doi-asserted-by":"crossref","first-page":"115","DOI":"10.51594\/csitrj.v3i3.1559","article-title":"Ethical AI: Addressing Bias in Machine Learning Models and Software Applications","volume":"3","author":"Oyekunle","year":"2022","journal-title":"Comput. Sci. IT Res. J."},{"key":"ref_225","doi-asserted-by":"crossref","unstructured":"Chamouni, G., Lococo, F., Sassorossi, C., Atuhaire, N., \u00c1d\u00e1ny, R., and Varga, O. (2025). Ethical and Legal Concerns in Artificial Intelligence Applications for the Diagnosis and Treatment of Lung Cancer: A Scoping Review. Front. Public Heal., 13.","DOI":"10.3389\/fpubh.2025.1663298"},{"key":"ref_226","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1007\/s43681-022-00218-9","article-title":"Ethics and Diversity in Artificial Intelligence Policies, Strategies and Initiatives","volume":"3","author":"Roche","year":"2023","journal-title":"AI Ethics"},{"key":"ref_227","doi-asserted-by":"crossref","unstructured":"Zhang, J., and Zhang, Z. (2023). Ethics and Governance of Trustworthy Medical Artificial Intelligence. BMC Med. Inform. Decis. Mak., 23.","DOI":"10.1186\/s12911-023-02103-9"},{"key":"ref_228","doi-asserted-by":"crossref","first-page":"1085","DOI":"10.1007\/s43681-023-00289-2","article-title":"Auditing Large Language Models: A Three-Layered Approach","volume":"4","author":"Schuett","year":"2024","journal-title":"AI Ethics"},{"key":"ref_229","doi-asserted-by":"crossref","first-page":"17","DOI":"10.6914\/dsvg.010102","article-title":"Intelligent Social Welfare: How AI Optimizes Social Assistance, Elderly Care, and Healthcare Systems","volume":"1","author":"Sun","year":"2025","journal-title":"Digit. Soc. Virtual Gov."},{"key":"ref_230","doi-asserted-by":"crossref","unstructured":"Mayrhofer, M.T., Lavitrano, M., and Kozlakidis, Z. (2025). Insights and Outlook from the First Ethical, Legal, and Social Implication Symposium of the BBMRI-ERIC Academy at International Agency for Research on Cancer\/World Health Organization. Biopreserv. Biobank., 19475535251370637.","DOI":"10.1177\/19475535251370637"},{"key":"ref_231","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.1177\/09697330231201901","article-title":"Cyberethics in Nursing Education: Ethical Implications of Artificial Intelligence","volume":"31","author":"Hwang","year":"2024","journal-title":"Nurs. Ethics"},{"key":"ref_232","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1007\/s11948-023-00443-3","article-title":"Reflections on Putting AI Ethics into Practice: How Three AI Ethics Approaches Conceptualize Theory and Practice","volume":"29","author":"Bleher","year":"2023","journal-title":"Sci. Eng. Ethics"},{"key":"ref_233","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1038\/s44387-025-00044-4","article-title":"The AI Revolution: How Multimodal Intelligence Will Reshape the Oncology Ecosystem","volume":"1","author":"Dellamonica","year":"2025","journal-title":"NPJ Artif. Intell."},{"key":"ref_234","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1080\/23808993.2024.2393089","article-title":"Drug Discovery in the Context of Precision Medicine and Artificial Intelligence","volume":"9","author":"Hasanzad","year":"2024","journal-title":"Expert Rev. Precis. Med. Drug Dev."},{"key":"ref_235","doi-asserted-by":"crossref","unstructured":"Grenier, P.A., Brun, A.L., and Mellot, F. (2022). The Potential Role of Artificial Intelligence in Lung Cancer Screening Using Low-Dose Computed Tomography. Diagnostics, 12.","DOI":"10.3390\/diagnostics12102435"},{"key":"ref_236","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1038\/s41416-022-02026-8","article-title":"Updated Cost-Effectiveness Analysis of Lung Cancer Screening for Australia, Capturing Differences in the Health Economic Impact of NELSON and NLST Outcomes","volume":"128","author":"Weber","year":"2023","journal-title":"Br. J. Cancer"},{"key":"ref_237","doi-asserted-by":"crossref","unstructured":"Wale, A., Shaw, H., Ayres, T., Okolie, C., Morgan, H., Everitt, J., Little, K., Edwards, R.T., Davies, J., and Lewis, R. (2023). A Rapid Review Exploring the Effectiveness of Artificial Intelligence for Cancer Diagnosis. medRxiv.","DOI":"10.1101\/2023.11.09.23298257"},{"key":"ref_238","doi-asserted-by":"crossref","first-page":"104929","DOI":"10.1016\/j.critrevonc.2025.104929","article-title":"Artificial Intelligence in NSCLC Management for Revolutionizing Diagnosis, Prognosis, and Treatment Optimization: A Systematic Review","volume":"216","author":"Bonci","year":"2025","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref_239","doi-asserted-by":"crossref","unstructured":"Zhu, J., and Tian, L. (2024). Cost-Effectiveness of Kang Ai Injection plus Chemotherapy vs. Shenqi Fuzheng Injection plus Chemotherapy in the First-Line Treatment of Advanced Non-Small Cell Lung Cancer. Front. Med., 11.","DOI":"10.3389\/fmed.2024.1363484"},{"key":"ref_240","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1007\/s40273-023-01292-5","article-title":"Health Economic Evidence and Modeling Challenges for Liquid Biopsy Assays in Cancer Management: A Systematic Literature Review","volume":"41","author":"Fagery","year":"2023","journal-title":"Pharmacoeconomics"},{"key":"ref_241","doi-asserted-by":"crossref","first-page":"e51514","DOI":"10.2196\/51514","article-title":"Suitability of the Current Health Technology Assessment of Innovative Artificial Intelligence-Based Medical Devices: Scoping Literature Review","volume":"26","author":"Farah","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_242","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.trecan.2024.12.001","article-title":"Artificial Intelligence-Based Biomarkers for Treatment Decisions in Oncology","volume":"11","author":"Ligero","year":"2025","journal-title":"Trends Cancer"},{"key":"ref_243","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1038\/s41392-024-01823-2","article-title":"Tumor Biomarkers for Diagnosis, Prognosis and Targeted Therapy","volume":"9","author":"Zhou","year":"2024","journal-title":"Signal Transduct. Target. Ther."},{"key":"ref_244","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1038\/s41746-025-01992-6","article-title":"Unlocking the Potential: Multimodal AI in Biotechnology and Digital Medicine\u2014Economic Impact and Ethical Challenges","volume":"8","author":"Bhushan","year":"2025","journal-title":"NPJ Digit. Med."},{"key":"ref_245","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1007\/s12672-025-02064-7","article-title":"AI-Driven Biomarker Discovery: Enhancing Precision in Cancer Diagnosis and Prognosis","volume":"16","author":"Alum","year":"2025","journal-title":"Discov. Oncol."},{"key":"ref_246","doi-asserted-by":"crossref","first-page":"10536","DOI":"10.1200\/JCO.2024.42.16_suppl.10536","article-title":"AI-Based Approach to Enable Proactive Identification of Early Lung Cancer: A Retrospective Population Health Study and Economic Model","volume":"42","author":"Ricketts","year":"2024","journal-title":"J. Clin. Oncol."},{"key":"ref_247","doi-asserted-by":"crossref","unstructured":"Huang, F., Lin, X., Hong, Y., Li, Y., Li, Y., Chen, W.-T., and Chen, W. (2025). The Feasibility and Cost-Effectiveness of Implementing Mobile Low-Dose Computed Tomography with an AI-Based Diagnostic System in Underserved Populations. BMC Cancer, 25.","DOI":"10.1186\/s12885-025-13710-2"},{"key":"ref_248","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1007\/s43621-024-00575-x","article-title":"Computational Intelligence Techniques for Achieving Sustainable Development Goals in Female Cancer Care","volume":"5","author":"Bukke","year":"2024","journal-title":"Discov. Sustain."},{"key":"ref_249","unstructured":"(2025, November 30). Oxford Insights Government AI Readiness Index\u2014Oxford Insights. Available online: https:\/\/oxfordinsights.com\/ai-readiness\/ai-readiness-index\/."},{"key":"ref_250","unstructured":"(2025, November 30). AI for Good Global Summit 2024. Available online: https:\/\/aiforgood.itu.int\/summit24\/."},{"key":"ref_251","doi-asserted-by":"crossref","first-page":"105442","DOI":"10.1016\/j.jprot.2025.105442","article-title":"Comprehensive Biobanking Strategy with Clinical Impact at the European Cancer Moonshot Lund Center","volume":"316","author":"Oskolas","year":"2025","journal-title":"J. Proteom."},{"key":"ref_252","unstructured":"White House Office of Science (2025, November 30). The Biden Cancer Moonshot: Ending Cancer As We Know It, Available online: https:\/\/bidenwhitehouse.archives.gov\/ostp\/news-updates\/2025\/01\/15\/the-biden-cancer-moonshot-ending-cancer-as-we-know-it\/."},{"key":"ref_253","doi-asserted-by":"crossref","first-page":"103497","DOI":"10.1016\/j.media.2025.103497","article-title":"From Challenges and Pitfalls to Recommendations and Opportunities: Implementing Federated Learning in Healthcare","volume":"101","author":"Li","year":"2025","journal-title":"Med. Image Anal."},{"key":"ref_254","doi-asserted-by":"crossref","first-page":"21501319241245847","DOI":"10.1177\/21501319241245847","article-title":"Exploring the Impact of Artificial Intelligence on Global Health and Enhancing Healthcare in Developing Nations","volume":"15","author":"Zuhair","year":"2024","journal-title":"J. Prim. Care Community Health"},{"key":"ref_255","unstructured":"MICCAI (2025, November 30). MICCAI Registered Challenges. Available online: https:\/\/miccai.org\/index.php\/special-interest-groups\/challenges\/miccai-registered-challenges."},{"key":"ref_256","doi-asserted-by":"crossref","first-page":"6274","DOI":"10.1038\/s41467-025-60466-1","article-title":"Towards Fair Decentralized Benchmarking of Healthcare AI Algorithms with the Federated Tumor Segmentation (FeTS) Challenge","volume":"16","author":"Zenk","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_257","unstructured":"(2025, November 30). Open Problems: Cracking Cell Complexity with Collective Intelligence. Available online: https:\/\/www.helmholtz-munich.de\/en\/ehc\/ehc-news\/news-detail\/open-problems-cracking-cell-complexity-with-collective-intelligence."},{"key":"ref_258","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1177\/0846537120941434","article-title":"Artificial Intelligence in Lung Cancer: Bridging the Gap Between Computational Power and Clinical Decision-Making","volume":"72","author":"Christie","year":"2021","journal-title":"Can. Assoc. Radiol. J."},{"key":"ref_259","doi-asserted-by":"crossref","first-page":"1477","DOI":"10.1038\/s41467-024-55068-2","article-title":"Multi-omics analyses reveal biological and clinical insights in recurrent stage I non-small cell lung cancer","volume":"16","author":"Wang","year":"2025","journal-title":"Nat. Commun."},{"key":"ref_260","doi-asserted-by":"crossref","unstructured":"Shariaty, F., Pavlov, V.A., Fedyashina, S.V., and Serebrennikov, N.A. (2024, January 20). Integrating Deep Learning and Explainable AI for Non-Invasive Prediction of EGFR and KRAS Mutations in NSCLC: A Novel Radiogenomic Approach. Proceedings of the 2024 V International Conference on Neural Networks and Neurotechnologies (NeuroNT), St. Petersburg, Russia.","DOI":"10.1109\/NeuroNT62606.2024.10585441"},{"key":"ref_261","doi-asserted-by":"crossref","first-page":"100817","DOI":"10.1016\/j.crmeth.2024.100817","article-title":"Cross-Attention Enables Deep Learning on Limited Omics-Imaging-Clinical Data of 130 Lung Cancer Patients","volume":"4","author":"Verma","year":"2024","journal-title":"Cell Rep. Methods"},{"key":"ref_262","doi-asserted-by":"crossref","unstructured":"Park, M.-K., Lim, J.-M., Jeong, J., Jang, Y., Lee, J.-W., Lee, J.-C., Kim, H., Koh, E., Hwang, S.-J., and Kim, H.-G. (2022). Deep-Learning Algorithm and Concomitant Biomarker Identification for NSCLC Prediction Using Multi-Omics Data Integration. Biomolecules, 12.","DOI":"10.3390\/biom12121839"},{"key":"ref_263","doi-asserted-by":"crossref","unstructured":"Lococo, F., Boldrini, L., Diepriye, C.-D., Evangelista, J., Nero, C., Flamini, S., Minucci, A., De Paolis, E., Vita, E., and Cesario, A. (2023). Lung Cancer Multi-Omics Digital Human Avatars for Integrating Precision Medicine into Clinical Practice: The LANTERN Study. BMC Cancer, 23.","DOI":"10.1186\/s12885-023-11606-7"},{"key":"ref_264","doi-asserted-by":"crossref","unstructured":"Jamil, D., Rocha, A., and Ferras, C. (2025). AI-Driven Computational Models for Lung Cancer Diagnosis: A Systematic Review and Meta-Analysis. Preprints.","DOI":"10.20944\/preprints202503.1981.v1"},{"key":"ref_265","doi-asserted-by":"crossref","unstructured":"Jacob, E., Arango-Argoty, G., Bikiel, D., Sun, G., Kipkogei, E., and Smith, K. (2024). AI-Based Predictive Biomarker Discovery via Contrastive Learning Retrospectively Improves Clinical Trial Outcome. Preprints.","DOI":"10.21203\/rs.3.rs-3938140\/v1"},{"key":"ref_266","doi-asserted-by":"crossref","first-page":"110325","DOI":"10.1016\/j.radonc.2024.110325","article-title":"Accuracy of machine learning in preoperative identi-fication of genetic mutation status in lung cancer: A systematic review and meta-analysis","volume":"196","author":"Chen","year":"2024","journal-title":"Radiother. Oncol."},{"key":"ref_267","doi-asserted-by":"crossref","unstructured":"Liu, R., Wang, K., Wang, S., and Zhang, Q. (2023, January 5). Biomarker Discovery Using Multimodal Data with the Potential Application in Lung Tumor Diagnosis. Proceedings of the 2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Istanbul, Turkiye.","DOI":"10.1109\/BIBM58861.2023.10385354"},{"key":"ref_268","doi-asserted-by":"crossref","unstructured":"\u00c7al\u0131\u015fkan, M., and Tazaki, K. (2023). AI\/ML Advances in Non-Small Cell Lung Cancer Biomarker Discovery. Front. Oncol., 13.","DOI":"10.3389\/fonc.2023.1260374"},{"key":"ref_269","doi-asserted-by":"crossref","first-page":"102","DOI":"10.53771\/ijlsra.2025.9.1.0048","article-title":"Computational Immunogenomics: Leveraging AI to Uncover Novel Biomarkers for Disease Diagnosis and Therapy","volume":"9","author":"Ogochukwu","year":"2025","journal-title":"Int. J. Life Sci. Res. Arch."},{"key":"ref_270","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.csbj.2025.09.036","article-title":"Quantum Neural Networks Meet Federated Learning for DNA Mutation Prediction","volume":"30","author":"Messinis","year":"2025","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_271","doi-asserted-by":"crossref","first-page":"331","DOI":"10.3934\/bioeng.2023020","article-title":"Transforming Lung Cancer Care: Synergizing Artificial Intelligence and Clinical Expertise for Precision Diagnosis and Treatment","volume":"10","author":"Sun","year":"2023","journal-title":"AIMS Bioeng."}],"container-title":["Pharmaceutics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4923\/18\/2\/201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T05:21:35Z","timestamp":1770960095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4923\/18\/2\/201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,3]]},"references-count":271,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["pharmaceutics18020201"],"URL":"https:\/\/doi.org\/10.3390\/pharmaceutics18020201","relation":{},"ISSN":["1999-4923"],"issn-type":[{"value":"1999-4923","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,3]]}}}