{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T22:21:00Z","timestamp":1793830860419,"version":"4.6.1"},"reference-count":157,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:00Z","timestamp":1730419200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,8,15]],"date-time":"2024-08-15T00:00:00Z","timestamp":1723680000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100009414","name":"Veneto Oncology Institute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009414","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003196","name":"Ministero della Salute","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003196","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003979","name":"Istituto Oncologico Romagnolo","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003979","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003500","name":"University of Padua","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100003500","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100030937","name":"Department of Surgery, University of Michigan","doi-asserted-by":"publisher","award":["DOR 2023"],"award-info":[{"award-number":["DOR 2023"]}],"id":[{"id":"10.13039\/100030937","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Critical Reviews in Oncology\/Hematology"],"published-print":{"date-parts":[[2024,11]]},"DOI":"10.1016\/j.critrevonc.2024.104479","type":"journal-article","created":{"date-parts":[[2024,8,14]],"date-time":"2024-08-14T20:01:10Z","timestamp":1723665670000},"page":"104479","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":39,"special_numbering":"C","title":["Clinical applications of radiomics and deep learning in breast and lung cancer: A narrative literature review on current evidence and future perspectives"],"prefix":"10.1016","volume":"203","author":[{"given":"Alessandra","family":"Ferro","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michele","family":"Bottosso","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3967-9861","authenticated-orcid":false,"given":"Maria Vittoria","family":"Dieci","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elena","family":"Scagliori","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federica","family":"Miglietta","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vittoria","family":"Aldegheri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Laura","family":"Bonanno","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesca","family":"Caumo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valentina","family":"Guarneri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gaia","family":"Griguolo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Giulia","family":"Pasello","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.critrevonc.2024.104479_bib1","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.1001\/jamaoncol.2016.2631","article-title":"The potential of radiomic-based phenotyping in precision medicine: a review","volume":"2","author":"Aerts","year":"2016","journal-title":"JAMA Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib2","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1148\/radiol.2015151169","article-title":"Radiomics: images are more than pictures, they are data","volume":"278","author":"Gillies","year":"2016","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib3","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib4","article-title":"Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach","volume":"5","author":"Aerts","year":"2014","journal-title":"Nat. Commun."},{"key":"10.1016\/j.critrevonc.2024.104479_bib5","doi-asserted-by":"crossref","first-page":"2673","DOI":"10.1007\/s00259-019-04414-4","article-title":"AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics","volume":"46","author":"Castiglioni","year":"2019","journal-title":"Eur. J. Nucl. Med Mol. Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib6","doi-asserted-by":"crossref","first-page":"1303","DOI":"10.7150\/thno.30309","article-title":"The applications of radiomics in precision diagnosis and treatment of oncology: opportunities and challenges","volume":"9","author":"Liu","year":"2019","journal-title":"Theranostics"},{"key":"10.1016\/j.critrevonc.2024.104479_bib7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13244-020-00887-2","article-title":"Radiomics in medical imaging\u2014\u201chow-to\u201d guide and critical reflection","volume":"11","author":"van Timmeren","year":"2020","journal-title":"Insights Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib8","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1148\/radiol.2020191145","article-title":"The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping","volume":"295","author":"Zwanenburg","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib9","doi-asserted-by":"crossref","first-page":"159","DOI":"10.3348\/kjr.2019.0630","article-title":"Radiomics in lung cancer from basic to advanced: current status and future directions","volume":"21","author":"Lee","year":"2020","journal-title":"Korean J. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib10","doi-asserted-by":"crossref","first-page":"497","DOI":"10.2214\/AJR.18.20623","article-title":"Radiomics in pulmonary lesion imaging. AJR","volume":"212","author":"Hassani","year":"2019","journal-title":"Am. J. Roentgenol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib11","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1038\/nrclinonc.2016.162","article-title":"Imaging biomarker roadmap for cancer studies","volume":"14","author":"O\u2019Connor","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib12","doi-asserted-by":"crossref","first-page":"e185","DOI":"10.1002\/mp.13678","article-title":"Machine and deep learning methods for radiomics","volume":"47","author":"Avanzo","year":"2020","journal-title":"Med Phys."},{"key":"10.1016\/j.critrevonc.2024.104479_bib13","article-title":"Deep learning with radiomics for disease diagnosis and treatment: challenges and potential","volume":"12","author":"Zhang","year":"2022","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib14","doi-asserted-by":"crossref","first-page":"3961","DOI":"10.1007\/s00259-021-05242-1","article-title":"Structural and functional radiomics for lung cancer","volume":"48","author":"Wu","year":"2021","journal-title":"Eur. J. Nucl. Med Mol. Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib15","article-title":"A review on advances in 18F-FDG PET\/CT radiomics standardisation and application in lung disease management","volume":"13","author":"Anan","year":"2022","journal-title":"Insights Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib16","doi-asserted-by":"crossref","first-page":"7","DOI":"10.3322\/caac.21442","article-title":"Cancer statistics, 2018","volume":"68","author":"Siegel","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"10.1016\/j.critrevonc.2024.104479_bib17","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1080\/14737140.2021.1852935","article-title":"Radiomics features as predictive and prognostic biomarkers in NSCLC","volume":"21","author":"Bortolotto","year":"2021","journal-title":"Expert Rev. Anticancer Ther."},{"key":"10.1016\/j.critrevonc.2024.104479_bib18","doi-asserted-by":"crossref","DOI":"10.3390\/jpm11070602","article-title":"Radiomics in lung diseases imaging: state-of-the-art for clinicians","volume":"11","author":"Frix","year":"2021","journal-title":"J. Pers. Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib19","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"10.1016\/j.critrevonc.2024.104479_bib20","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-70940-z","article-title":"MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability","volume":"10","author":"Granzier","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.critrevonc.2024.104479_bib21","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1056\/NEJMoa1102873","article-title":"Reduced lung-cancer mortality with low-dose computed tomographic screening","volume":"365","author":"DR","year":"2011","journal-title":"N. Engl. J. Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib22","first-page":"2165","article-title":"Lung-Cancer Screening and the NELSON Trial","volume":"382","author":"Autier","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.critrevonc.2024.104479_bib23","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1002\/ijc.33578","article-title":"Personalising lung cancer screening: An overview of risk-stratification opportunities and challenges","volume":"149","author":"ten Haaf","year":"2021","journal-title":"Int J. Cancer"},{"key":"10.1016\/j.critrevonc.2024.104479_bib24","doi-asserted-by":"crossref","first-page":"704","DOI":"10.7326\/M20-1868","article-title":"Deep Learning Using Chest Radiographs to Identify High-Risk Smokers for Lung Cancer Screening Computed Tomography: Development and Validation of a Prediction Model","volume":"173","author":"Lu","year":"2020","journal-title":"Ann. Intern Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib25","article-title":"ACR Lung-RADS v2022: assessment categories and management recommendations","author":"Christensen","year":"2023","journal-title":"J. Am. Coll. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib26","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1007\/s12194-019-00552-4","article-title":"AI-based computer-aided diagnosis (AI-CAD): the latest review to read first","volume":"13","author":"Fujita","year":"2020","journal-title":"Radio. Phys. Technol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib27","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.21037\/qims-19-883","article-title":"Automatic detection of pulmonary nodules on CT images with YOLOv3: development and evaluation using simulated and patient data","volume":"10","author":"Liu","year":"2020","journal-title":"Quant. Imaging Med Surg."},{"key":"10.1016\/j.critrevonc.2024.104479_bib28","doi-asserted-by":"crossref","first-page":"3748","DOI":"10.1109\/TMI.2021.3097665","article-title":"Lung Nodule Malignancy Prediction in Sequential CT Scans: Summary of ISBI 2018 Challenge","volume":"40","author":"Balagurunathan","year":"2021","journal-title":"IEEE Trans. Med Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib29","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1148\/radiol.14132187","article-title":"Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas","volume":"273","author":"Chae","year":"2014","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib30","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-44562-z","article-title":"Quantitative Imaging features Improve Discrimination of Malignancy in Pulmonary nodules","volume":"9","author":"Balagurunathan","year":"2019","journal-title":"Sci. Rep."},{"key":"10.1016\/j.critrevonc.2024.104479_bib31","article-title":"Discovery radiomics for pathologically-proven computed tomography lung cancer prediction","author":"Kumar","year":"2017","journal-title":"Lect. Notes Comput. Sci. (Incl. Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinforma. )"},{"key":"10.1016\/j.critrevonc.2024.104479_bib32","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1158\/1078-0432.CCR-15-3102","article-title":"Radiological Image Traits Predictive of Cancer Status in Pulmonary Nodules","volume":"23","author":"Liu","year":"2017","journal-title":"Clin. Cancer Res"},{"key":"10.1016\/j.critrevonc.2024.104479_bib33","doi-asserted-by":"crossref","first-page":"e353","DOI":"10.1016\/S2589-7500(19)30159-1","article-title":"Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method","volume":"1","author":"Huang","year":"2019","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.critrevonc.2024.104479_bib34","doi-asserted-by":"crossref","first-page":"6913","DOI":"10.1007\/s00330-020-07071-6","article-title":"Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning","volume":"30","author":"Huang","year":"2020","journal-title":"Eur. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib35","doi-asserted-by":"crossref","first-page":"1983","DOI":"10.1007\/s00330-021-08268-z","article-title":"Development and validation of a preoperative CT-based radiomic nomogram to predict pathology invasiveness in patients with a solitary pulmonary nodule: a machine learning approach, multicenter, diagnostic study","volume":"32","author":"Huang","year":"2022","journal-title":"Eur. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib36","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1002\/mrm.10496","article-title":"Textural analysis of contrast-enhanced MR images of the breast","volume":"50","author":"Gibbs","year":"2003","journal-title":"Magn. Reson Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib37","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.acra.2009.08.012","article-title":"Characterization of breast cancer types by texture analysis of magnetic resonance images","volume":"17","author":"Holli","year":"2010","journal-title":"Acad. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib38","doi-asserted-by":"crossref","first-page":"842","DOI":"10.1016\/j.acra.2009.01.029","article-title":"Prediction of malignant breast lesions from MRI features: a comparison of artificial neural network and logistic regression techniques","volume":"16","author":"McLaren","year":"2009","journal-title":"Acad. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib39","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/j.acra.2008.06.005","article-title":"Quantitative analysis of lesion morphology and texture features for diagnostic prediction in breast MRI","volume":"15","author":"Nie","year":"2008","journal-title":"Acad. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib40","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.mri.2013.12.002","article-title":"Computer-aided diagnosis of breast DCE-MRI using pharmacokinetic model and 3-D morphology analysis","volume":"32","author":"Wang","year":"2014","journal-title":"Magn. Reson Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib41","doi-asserted-by":"crossref","first-page":"1205","DOI":"10.1002\/jmri.25873","article-title":"Support vector machine for breast cancer classification using diffusion-weighted MRI histogram features: Preliminary study","volume":"47","author":"Vidi\u0107","year":"2018","journal-title":"J. Magn. Reson Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib42","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1002\/jmri.25606","article-title":"Prediction of malignancy by a radiomic signature from contrast agent-free diffusion MRI in suspicious breast lesions found on screening mammography","volume":"46","author":"Bickelhaupt","year":"2017","journal-title":"J. Magn. Reson Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib43","article-title":"Radiomics methodology for breast cancer diagnosis using multiparametric magnetic resonance imaging","volume":"7","author":"Hu","year":"2020","journal-title":"J. Med Imaging (Bellingham)"},{"key":"10.1016\/j.critrevonc.2024.104479_bib44","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s10549-020-05533-5","article-title":"Multiparametric radiomics methods for breast cancer tissue characterization using radiological imaging","volume":"180","author":"Parekh","year":"2020","journal-title":"Breast Cancer Res Treat."},{"key":"10.1016\/j.critrevonc.2024.104479_bib45","doi-asserted-by":"crossref","unstructured":"Tsarouchi M.I., Vlachopoulos G.F., Karahaliou A.N., Costaridou L.I. Diffusion Weighted Magnetic Resonance Imaging Texture Biomarkers for Breast Cancer Diagnosis, 2020, p. 301\u2013305. https:\/\/doi.org\/10.1007\/978-3-030-31635-8_36.","DOI":"10.1007\/978-3-030-31635-8_36"},{"key":"10.1016\/j.critrevonc.2024.104479_bib46","doi-asserted-by":"crossref","first-page":"5162","DOI":"10.1002\/mp.12453","article-title":"A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets","volume":"44","author":"Antropova","year":"2017","journal-title":"Med Phys."},{"key":"10.1016\/j.critrevonc.2024.104479_bib47","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1097\/RLI.0000000000000544","article-title":"Artificial Intelligence\u2013Based Classification of Breast Lesions Imaged With a Multiparametric Breast MRI Protocol With Ultrafast DCE-MRI, T2, and DWI","volume":"54","author":"Dalmi\u015f","year":"2019","journal-title":"Invest Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib48","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1148\/radiol.2020200292","article-title":"Artificial intelligence applied to breast MRI for improved diagnosis","volume":"298","author":"Jiang","year":"2021","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib49","doi-asserted-by":"crossref","first-page":"5866","DOI":"10.1007\/s00330-021-07787-z","article-title":"An A.I. classifier derived from 4D radiomics of dynamic contrast-enhanced breast MRI data: potential to avoid unnecessary breast biopsies","volume":"31","author":"P\u00f6tsch","year":"2021","journal-title":"Eur. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib50","article-title":"Radiomics based on digital mammography helps to identify mammographic masses suspicious for cancer","volume":"12","author":"Wang","year":"2022","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib51","article-title":"Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis","volume":"12","author":"Li","year":"2022","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib52","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.annonc.2022.12.009","article-title":"Oncogene-addicted metastatic non-small-cell lung cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up","volume":"34","author":"Hendriks","year":"2023","journal-title":"Ann. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib53","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.pathol.2020.10.007","article-title":"Programmed death ligand-1 (PD-L1) as a predictive marker for immunotherapy in solid tumours: a guide to immunohistochemistry implementation and interpretation","volume":"53","author":"Paver","year":"2021","journal-title":"Pathology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib54","article-title":"Exploratory Study of a CT Radiomics Model for the Classification of Small Cell Lung Cancer and Non-small-Cell Lung Cancer","volume":"10","author":"Liu","year":"2020","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib55","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.1007\/s00330-017-5221-1","article-title":"Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer","volume":"28","author":"Zhu","year":"2018","journal-title":"Eur. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib56","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2016.00071","article-title":"Exploratory Study to Identify Radiomics Classifiers for Lung Cancer Histology","volume":"6","author":"Wu","year":"2016","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib57","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/j.cmpb.2018.02.015","article-title":"de. Radiomics-based features for pattern recognition of lung cancer histopathology and metastases","volume":"159","author":"Ferreira Junior","year":"2018","journal-title":"Comput. Methods Prog. Biomed."},{"key":"10.1016\/j.critrevonc.2024.104479_bib58","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1148\/radiol.2017161845","article-title":"Non-Small Cell Lung Cancer Radiogenomics Map Identifies Relationships between Molecular and Imaging Phenotypes with Prognostic Implications","volume":"286","author":"Zhou","year":"2018","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib59","doi-asserted-by":"crossref","DOI":"10.3390\/cancers14071657","article-title":"Radiomics\/Radiogenomics in Lung Cancer: Basic Principles and Initial Clinical Results","volume":"14","author":"Anagnostopoulos","year":"2022","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.critrevonc.2024.104479_bib60","doi-asserted-by":"crossref","DOI":"10.1183\/13993003.00986-2018","article-title":"Predicting EGFR mutation status in lung adenocarcinoma on computed tomography image using deep learning","volume":"53","author":"Wang","year":"2019","journal-title":"Eur. Respir. J."},{"key":"10.1016\/j.critrevonc.2024.104479_bib61","doi-asserted-by":"crossref","first-page":"3922","DOI":"10.1158\/0008-5472.CAN-17-0122","article-title":"Somatic mutations drive distinct imaging phenotypes in lung cancer","volume":"77","author":"Rios Velazquez","year":"2017","journal-title":"Cancer Res"},{"key":"10.1016\/j.critrevonc.2024.104479_bib62","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.tranon.2017.10.012","article-title":"Quantitative biomarkers for prediction of epidermal growth factor receptor mutation in non-small cell lung cancer","volume":"11","author":"Zhang","year":"2018","journal-title":"Transl. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib63","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2019.01485","article-title":"The Potential of Radiomics Nomogram in Non-invasively Prediction of Epidermal Growth Factor Receptor Mutation Status and Subtypes in Lung Adenocarcinoma","volume":"9","author":"Zhao","year":"2020","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib64","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000001753","article-title":"Decoding Tumor Phenotypes for ALK, ROS1, and RET Fusions in Lung Adenocarcinoma Using a Radiomics Approach","volume":"94","author":"Yoon","year":"2015","journal-title":"Medicine"},{"key":"10.1016\/j.critrevonc.2024.104479_bib65","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2021.109710","article-title":"CT radiomics-based prediction of anaplastic lymphoma kinase and epidermal growth factor receptor mutations in lung adenocarcinoma","volume":"139","author":"Choe","year":"2021","journal-title":"Eur. J. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib66","doi-asserted-by":"crossref","first-page":"798","DOI":"10.1016\/j.annonc.2020.03.287","article-title":"Outcomes with durvalumab by tumour PD-L1 expression in unresectable, stage III non-small-cell lung cancer in the PACIFIC trial","volume":"31","author":"Paz-Ares","year":"2020","journal-title":"Ann. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib67","doi-asserted-by":"crossref","first-page":"E199","DOI":"10.1093\/oncolo\/oyab047","article-title":"Tumor Immune-Infiltrate Landscape After Chemo-Radiotherapy in a Case Series of Patients with Non-small Cell Lung Cancer: Pretreatment Predictors and Correlation With Outcome","volume":"27","author":"Pavan","year":"2022","journal-title":"Oncologist"},{"key":"10.1016\/j.critrevonc.2024.104479_bib68","first-page":"449","article-title":"Radiomics study for predicting the expression of PD-L1 in non-small cell lung cancer based on CT images and clinicopathologic features","volume":"28","author":"Sun","year":"2020","journal-title":"J. Xray Sci. Technol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib69","doi-asserted-by":"crossref","DOI":"10.3390\/cancers14194823","article-title":"Radiogenomic System for Non-Invasive Identification of Multiple Actionable Mutations and PD-L1 Expression in Non-Small Cell Lung Cancer Based on CT Images","volume":"14","author":"Shao","year":"2022","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.critrevonc.2024.104479_bib70","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1186\/s12885-020-07557-y","article-title":"Prediction of the clinicopathological subtypes of breast cancer using a fisher discriminant analysis model based on radiomic features of diffusion-weighted MRI","volume":"20","author":"Ni","year":"2020","journal-title":"BMC Cancer"},{"key":"10.1016\/j.critrevonc.2024.104479_bib71","doi-asserted-by":"crossref","first-page":"550","DOI":"10.1007\/s11307-021-01695-w","article-title":"Intratumoral and Peritumoral Analysis of Mammography, Tomosynthesis, and Multiparametric MRI for Predicting Ki-67 Level in Breast Cancer: a Radiomics-Based Study","volume":"24","author":"Jiang","year":"2022","journal-title":"Mol. Imaging Biol."},{"issue":"Suppl 1","key":"10.1016\/j.critrevonc.2024.104479_bib72","doi-asserted-by":"crossref","first-page":"S116","DOI":"10.1016\/j.acra.2021.02.001","article-title":"Radioproteomics in Breast Cancer: Prediction of Ki-67 Expression With MRI-based Radiomic Models","volume":"29","author":"Kayadibi","year":"2022","journal-title":"Acad. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib73","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s11547-019-01100-1","article-title":"Invasive ductal breast cancer: preoperative predict Ki-67 index based on radiomics of ADC maps","volume":"125","author":"Zhang","year":"2020","journal-title":"Radio. Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib74","doi-asserted-by":"crossref","first-page":"e44","DOI":"10.1016\/j.acra.2020.02.006","article-title":"Preoperative Prediction of Ki-67 Status in Breast Cancer with Multiparametric MRI Using Transfer Learning","volume":"28","author":"Liu","year":"2021","journal-title":"Acad. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib75","doi-asserted-by":"crossref","first-page":"10869","DOI":"10.1073\/pnas.191367098","article-title":"Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications","volume":"98","author":"S\u00f8rlie","year":"2001","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"10.1016\/j.critrevonc.2024.104479_bib76","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2021.109996","article-title":"Radiomic differentiation of breast cancer molecular subtypes using pre-operative breast imaging - A systematic review and meta-analysis","volume":"144","author":"Davey","year":"2021","journal-title":"Eur. J. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib77","doi-asserted-by":"crossref","first-page":"2336","DOI":"10.1056\/NEJMoa2108873","article-title":"21-Gene Assay to Inform Chemotherapy Benefit in Node-Positive Breast Cancer","volume":"385","author":"Kalinsky","year":"2021","journal-title":"N. Engl. J. Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib78","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1056\/NEJMoa1804710","article-title":"Adjuvant Chemotherapy Guided by a 21-Gene Expression Assay in Breast Cancer","volume":"379","author":"Sparano","year":"2018","journal-title":"N. Engl. J. Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib79","doi-asserted-by":"crossref","DOI":"10.1093\/bjsopen\/zrab081","article-title":"Is radiomic MRI a feasible alternative to OncotypeDX\u00ae recurrence score testing? A systematic review and meta-analysis","volume":"5","author":"Davey","year":"2021","journal-title":"BJS Open"},{"key":"10.1016\/j.critrevonc.2024.104479_bib80","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1148\/radiol.2016152110","article-title":"MR Imaging Radiomics Signatures for Predicting the Risk of Breast Cancer Recurrence as Given by Research Versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays","volume":"281","author":"Li","year":"2016","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib81","doi-asserted-by":"crossref","first-page":"S807","DOI":"10.21037\/jtd.2018.03.126","article-title":"A new approach to predict lymph node metastasis in solid lung adenocarcinoma: a radiomics nomogram","volume":"10","author":"Yang","year":"2018","journal-title":"J. Thorac. Dis."},{"key":"10.1016\/j.critrevonc.2024.104479_bib82","doi-asserted-by":"crossref","first-page":"109","DOI":"10.2214\/AJR.17.19074","article-title":"Radiomics Approach to Prediction of Occult Mediastinal Lymph Node Metastasis of Lung Adenocarcinoma. AJR","volume":"211","author":"Zhong","year":"2018","journal-title":"Am. J. Roentgenol."},{"issue":"8","key":"10.1016\/j.critrevonc.2024.104479_bib83","doi-asserted-by":"crossref","first-page":"6030","DOI":"10.1007\/s00330-020-07624-9","article-title":"A PET\/CT nomogram incorporating SUVmax and CT radiomics for preoperative nodal staging in non-small cell lung cancer","volume":"31","author":"Xie","year":"2021","journal-title":"Eur. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib84","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1007\/s00330-014-3420-6","article-title":"Quantitative CT texture and shape analysis: can it differentiate benign and malignant mediastinal lymph nodes in patients with primary lung cancer?","volume":"25","author":"Bayanati","year":"2015","journal-title":"Eur. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib85","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1177\/0284185115598808","article-title":"CT texture analysis can help differentiate between malignant and benign lymph nodes in the mediastinum in patients suspected for lung cancer","volume":"57","author":"Andersen","year":"2016","journal-title":"Acta Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib86","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.jtho.2016.11.2226","article-title":"Radiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC","volume":"12","author":"Coroller","year":"2017","journal-title":"J. Thorac. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib87","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2020.558428","article-title":"Preoperatively Estimating the Malignant Potential of Mediastinal Lymph Nodes: A Pilot Study Toward Establishing a Robust Radiomics Model Based on Contrast-Enhanced CT Imaging","volume":"10","author":"Dong","year":"2021","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib88","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1097\/RCT.0000000000001264","article-title":"Integrative Predictive Models of Computed Tomography Texture Parameters and Hematological Parameters for Lymph Node Metastasis in Lung Adenocarcinomas","volume":"46","author":"Chen","year":"2022","journal-title":"J. Comput. Assist Tomogr."},{"key":"10.1016\/j.critrevonc.2024.104479_bib89","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1148\/radiol.2016151829","article-title":"Early-Stage Non-Small Cell Lung Cancer: Quantitative Imaging Characteristics of (18)F Fluorodeoxyglucose PET\/CT Allow Prediction of Distant Metastasis","volume":"281","author":"Wu","year":"2016","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib90","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.radonc.2015.02.015","article-title":"CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma","volume":"114","author":"Coroller","year":"2015","journal-title":"Radio. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib91","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0206108","article-title":"Peritumoral radiomics features predict distant metastasis in locally advanced NSCLC","volume":"13","author":"Dou","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.critrevonc.2024.104479_bib92","doi-asserted-by":"crossref","first-page":"4375","DOI":"10.21037\/tcr-21-702","article-title":"Development and validation a radiomics nomogram for diagnosing occult brain metastases in patients with stage IV lung adenocarcinoma","volume":"10","author":"Cong","year":"2021","journal-title":"Transl. Cancer Res"},{"key":"10.1016\/j.critrevonc.2024.104479_bib93","doi-asserted-by":"crossref","first-page":"3657","DOI":"10.1200\/JCO.2006.07.4062","article-title":"Surgical complications associated with sentinel lymph node dissection (SLND) plus axillary lymph node dissection compared with SLND alone in the American College of Surgeons Oncology Group Trial Z0011","volume":"25","author":"Lucci","year":"2007","journal-title":"J. Clin. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib94","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1016\/S1470-2045(07)70278-4","article-title":"Technical outcomes of sentinel-lymph-node resection and conventional axillary-lymph-node dissection in patients with clinically node-negative breast cancer: results from the NSABP B-32 randomised phase III trial","volume":"8","author":"Krag","year":"2007","journal-title":"Lancet Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib95","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s00330-017-5005-7","article-title":"Preoperative prediction of sentinel lymph node metastasis in breast cancer based on radiomics of T2-weighted fat-suppression and diffusion-weighted MRI","volume":"28","author":"Dong","year":"2018","journal-title":"Eur. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib96","article-title":"Radiomics Analysis of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for the Prediction of Sentinel Lymph Node Metastasis in Breast Cancer","volume":"9","author":"Liu","year":"2019","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib97","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1002\/jmri.26224","article-title":"Preoperative prediction of sentinel lymph node metastasis in breast cancer by radiomic signatures from dynamic contrast-enhanced MRI","volume":"49","author":"Liu","year":"2019","journal-title":"J. Magn. Reson Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib98","article-title":"MRI-Based Radiomics Nomogram: Prediction of Axillary Non-Sentinel Lymph Node Metastasis in Patients With Sentinel Lymph Node-Positive Breast Cancer","volume":"12","author":"Qiu","year":"2022","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib99","article-title":"Development and internal validation of a preoperative prediction model for sentinel lymph node status in breast cancer: combining radiomics signature and clinical factors","volume":"11","author":"Wang","year":"2021","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib100","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s42492-022-00104-5","article-title":"Preoperative prediction of lymph node metastasis using deep learning-based features","volume":"5","author":"Cattell","year":"2022","journal-title":"Vis. Comput. Ind. Biomed. Art."},{"key":"10.1016\/j.critrevonc.2024.104479_bib101","article-title":"Prediction of metastasis in the axillary lymph nodes of patients with breast cancer: a radiomics method based on contrast-enhanced computed tomography","volume":"11","author":"Yang","year":"2021","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib102","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2021.757111","article-title":"Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer","volume":"11","author":"Zhu","year":"2021","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib103","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s40644-020-00342-x","article-title":"Pharmacokinetic parameters and radiomics model based on dynamic contrast enhanced MRI for the preoperative prediction of sentinel lymph node metastasis in breast cancer","volume":"20","author":"Liu","year":"2020","journal-title":"Cancer Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib104","article-title":"The Diagnostic Performance of Machine Learning-Based Radiomics of DCE-MRI in Predicting Axillary Lymph Node Metastasis in Breast Cancer: A Meta-Analysis","volume":"12","author":"Zhang","year":"2022","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib105","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1038\/s41416-020-01122-x","article-title":"Deep learning in cancer pathology: a new generation of clinical biomarkers","volume":"124","author":"Echle","year":"2021","journal-title":"Br. J. Cancer"},{"key":"10.1016\/j.critrevonc.2024.104479_bib106","doi-asserted-by":"crossref","first-page":"2151","DOI":"10.1158\/1078-0432.CCR-19-2942","article-title":"Identification of Non-Small Cell Lung Cancer Sensitive to Systemic Cancer Therapies Using Radiomics","volume":"26","author":"Dercle","year":"2020","journal-title":"Clin. Cancer Res"},{"key":"10.1016\/j.critrevonc.2024.104479_bib107","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-020-19116-x","article-title":"Non-invasive decision support for NSCLC treatment using PET\/CT radiomics","volume":"11","author":"Mu","year":"2020","journal-title":"Nat. Commun."},{"key":"10.1016\/j.critrevonc.2024.104479_bib108","article-title":"CT-Based Radiomic Signature as a Prognostic Factor in Stage IV ALK-Positive Non-small-cell Lung Cancer Treated With TKI Crizotinib: A Proof-of-Concept Study","volume":"10","author":"Li","year":"2020","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib109","doi-asserted-by":"crossref","first-page":"1386","DOI":"10.1007\/s11547-023-01702-w","article-title":"A CT-based radiomics nomogram for predicting the progression-free survival in small cell lung cancer: a multicenter cohort study","volume":"128","author":"Zheng","year":"2023","journal-title":"Radio. Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib110","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/s00259-021-05509-7","article-title":"Current status and quality of radiomic studies for predicting immunotherapy response and outcome in patients with non-small cell lung cancer: a systematic review and meta-analysis","volume":"49","author":"Chen","year":"2021","journal-title":"Eur. J. Nucl. Med Mol. Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib111","doi-asserted-by":"crossref","DOI":"10.1148\/ryai.2019190063","article-title":"Radiomics of 18F Fluorodeoxyglucose PET\/CT Images Predicts Severe Immune-related Adverse Events in Patients with NSCLC","volume":"2","author":"Mu","year":"2020","journal-title":"Radio. Artif. Intell."},{"key":"10.1016\/j.critrevonc.2024.104479_bib112","doi-asserted-by":"crossref","DOI":"10.1016\/j.ctrv.2021.102222","article-title":"Neoadjuvant approach as a platform for treatment personalization: focus on HER2-positive and triple-negative breast cancer","volume":"98","author":"Miglietta","year":"2021","journal-title":"Cancer Treat. Rev."},{"key":"10.1016\/j.critrevonc.2024.104479_bib113","unstructured":"NCCN Clinical Practice Guidelines in Oncology - Breast Cancer. Version 12022 2022. https:\/\/www.nccn.org\/professionals\/physician_gls\/pdf\/breast.pdf (accessed November 24, 2021)."},{"key":"10.1016\/j.critrevonc.2024.104479_bib114","doi-asserted-by":"crossref","first-page":"932","DOI":"10.1245\/s10434-010-1390-8","article-title":"Impact of Progression During Neoadjuvant Chemotherapy on Surgical Management of Breast Cancer","volume":"18","author":"Caudle","year":"2011","journal-title":"Ann. Surg. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib115","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1016\/S0140-6736(13)62422-8","article-title":"Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis","volume":"384","author":"Cortazar","year":"2014","journal-title":"Lancet"},{"key":"10.1016\/j.critrevonc.2024.104479_bib116","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1007\/s10549-018-4990-9","article-title":"Multivariate machine learning models for prediction of pathologic response to neoadjuvant therapy in breast cancer using MRI features: a study using an independent validation set","volume":"173","author":"Cain","year":"2019","journal-title":"Breast Cancer Res Treat."},{"key":"10.1016\/j.critrevonc.2024.104479_bib117","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/s13058-017-0846-1","article-title":"Intratumoral and peritumoral radiomics for the pretreatment prediction of pathological complete response to neoadjuvant chemotherapy based on breast DCE-MRI","volume":"19","author":"Braman","year":"2017","journal-title":"Breast Cancer Res."},{"key":"10.1016\/j.critrevonc.2024.104479_bib118","first-page":"669","article-title":"Radiomics of contrast-enhanced spectral mammography for prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer","volume":"31","author":"Zhang","year":"2023","journal-title":"J. Xray Sci. Technol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib119","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejrad.2022.110247","article-title":"Machine learning with magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis","volume":"150","author":"Liang","year":"2022","journal-title":"Eur. J. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib120","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1177\/000313481908500630","article-title":"Meta-Analysis of Quantitative Dynamic Contrast-Enhanced MRI for the Assessment of Neoadjuvant Chemotherapy in Breast Cancer","volume":"85","author":"Jun","year":"2019","journal-title":"Am. Surg."},{"key":"10.1016\/j.critrevonc.2024.104479_bib121","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.tranon.2015.11.016","article-title":"Early Prediction and Evaluation of Breast Cancer Response to Neoadjuvant Chemotherapy Using Quantitative DCE-MRI","volume":"9","author":"Tudorica","year":"2016","journal-title":"Transl. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib122","doi-asserted-by":"crossref","first-page":"90","DOI":"10.18383\/j.tom.2018.00046","article-title":"Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps","volume":"5","author":"Machireddy","year":"2019","journal-title":"Tomography"},{"key":"10.1016\/j.critrevonc.2024.104479_bib123","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1097\/RLI.0000000000000518","article-title":"Impact of machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients","volume":"54","author":"Tahmassebi","year":"2019","journal-title":"Invest Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib124","doi-asserted-by":"crossref","first-page":"23","DOI":"10.18383\/j.tom.2016.00241","article-title":"DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response","volume":"3","author":"Thibault","year":"2017","journal-title":"Tomography"},{"key":"10.1016\/j.critrevonc.2024.104479_bib125","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1148\/radiol.2020192764","article-title":"Preoperative CT-based Deep Learning Model for Predicting Disease-Free Survival in Patients with Lung Adenocarcinomas","volume":"296","author":"Kim","year":"2020","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib126","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002711","article-title":"Deep learning for lung cancer prognostication: A retrospective multi-cohort radiomics study","volume":"15","author":"Hosny","year":"2018","journal-title":"PLoS Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib127","article-title":"Development of an Immune-Pathology Informed Radiomics Model for Non-Small Cell Lung Cancer","volume":"8","author":"Tang","year":"2018","journal-title":"Sci. Rep."},{"key":"10.1016\/j.critrevonc.2024.104479_bib128","doi-asserted-by":"crossref","DOI":"10.1186\/s12890-023-02422-7","article-title":"Prognostic predictors of radical resection of stage I-IIIB non-small cell lung cancer: the role of preoperative CT texture features, conventional imaging features, and clinical features in a retrospectively analyzed","volume":"23","author":"Zheng","year":"2023","journal-title":"BMC Pulm. Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib129","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13143616","article-title":"Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer","volume":"13","author":"Le","year":"2021","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.critrevonc.2024.104479_bib130","doi-asserted-by":"crossref","DOI":"10.3390\/cancers14153798","article-title":"Radiomics-Based Deep Learning Prediction of Overall Survival in Non-Small-Cell Lung Cancer Using Contrast-Enhanced Computed Tomography","volume":"14","author":"Hou","year":"2022","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.critrevonc.2024.104479_bib131","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0192859","article-title":"18F-fluorodeoxyglucose positron-emission tomography (FDG-PET)-Radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) - A prospective externally validated study","volume":"13","author":"Carvalho","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.critrevonc.2024.104479_bib132","doi-asserted-by":"crossref","first-page":"6916","DOI":"10.1007\/s00330-021-07816-x","article-title":"Texture analysis using machine learning\u2013based 3-T magnetic resonance imaging for predicting recurrence in breast cancer patients treated with neoadjuvant chemotherapy","volume":"31","author":"Eun","year":"2021","journal-title":"Eur. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib133","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1148\/radiol.2016160261","article-title":"Breast Cancer Heterogeneity: MR Imaging Texture Analysis and Survival Outcomes","volume":"282","author":"Kim","year":"2017","journal-title":"Radiology"},{"key":"10.1016\/j.critrevonc.2024.104479_bib134","doi-asserted-by":"crossref","first-page":"2715","DOI":"10.21037\/gs-21-537","article-title":"A pre-operative MRI-based brain metastasis risk-prediction model for triple-negative breast cancer","volume":"10","author":"Cheng","year":"2021","journal-title":"Gland Surg."},{"key":"10.1016\/j.critrevonc.2024.104479_bib135","doi-asserted-by":"crossref","first-page":"3750","DOI":"10.1038\/s41598-020-60822-9","article-title":"MRI Radiomic Features: Association with Disease-Free Survival in Patients with Triple-Negative Breast Cancer","volume":"10","author":"Kim","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.critrevonc.2024.104479_bib136","doi-asserted-by":"crossref","first-page":"5781","DOI":"10.1038\/s41598-020-62516-8","article-title":"Heterogeneity at the invasion front of triple negative breast cancer cells","volume":"10","author":"Aw Yong","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.critrevonc.2024.104479_bib137","doi-asserted-by":"crossref","first-page":"4705","DOI":"10.1158\/1078-0432.CCR-17-3783","article-title":"Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer","volume":"24","author":"Park","year":"2018","journal-title":"Clin. Cancer Res."},{"key":"10.1016\/j.critrevonc.2024.104479_bib138","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13112522","article-title":"Radiomics in Oncology, Part 1: Technical Principles and Gastrointestinal Application in CT and MRI","volume":"13","author":"Caruso","year":"2021","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.critrevonc.2024.104479_bib139","doi-asserted-by":"crossref","DOI":"10.3390\/cancers13112681","article-title":"Radiomics in Oncology, Part 2: Thoracic, Genito-Urinary, Breast, Neurological, Hematologic and Musculoskeletal Applications","volume":"13","author":"Caruso","year":"2021","journal-title":"Cancers (Basel)"},{"key":"10.1016\/j.critrevonc.2024.104479_bib140","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1080\/23808993.2016.1164013","article-title":"Radiomics: a new application from established techniques","volume":"1","author":"Parekh","year":"2016","journal-title":"Expert Rev. Precis Med Drug Dev."},{"key":"10.1016\/j.critrevonc.2024.104479_bib141","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med Image Anal."},{"key":"10.1016\/j.critrevonc.2024.104479_bib142","doi-asserted-by":"crossref","first-page":"1239","DOI":"10.1016\/j.jacr.2019.05.047","article-title":"Strengths, weaknesses, opportunities, and threats analysis of artificial intelligence and machine learning applications in radiology","volume":"16","author":"Mart\u00edn Noguerol","year":"2019","journal-title":"J. Am. Coll. Radio."},{"key":"10.1016\/j.critrevonc.2024.104479_bib143","article-title":"Deep learning with radiomics for disease diagnosis and treatment: challenges and potential","volume":"12","author":"Zhang","year":"2022","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib144","doi-asserted-by":"crossref","DOI":"10.3390\/jcm11061740","article-title":"The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria","volume":"11","author":"Castello","year":"2022","journal-title":"J. Clin. Med"},{"key":"10.1016\/j.critrevonc.2024.104479_bib145","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1593\/tlo.13862","article-title":"Quantitative imaging network: data sharing and competitive algorithmvalidation leveraging the cancer imaging archive","volume":"7","author":"Kalpathy-Cramer","year":"2014","journal-title":"Transl. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib146","doi-asserted-by":"crossref","DOI":"10.1136\/bmj.g7594","article-title":"Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement","volume":"350","author":"Collins","year":"2015","journal-title":"BMJ"},{"key":"10.1016\/j.critrevonc.2024.104479_bib147","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1038\/nrclinonc.2017.141","article-title":"Radiomics: the bridge between medical imaging and personalized medicine","volume":"14","author":"Lambin","year":"2017","journal-title":"Nat. Rev. Clin. Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib148","doi-asserted-by":"crossref","first-page":"118","DOI":"10.18383\/j.tom.2019.00031","article-title":"Standardization in Quantitative Imaging: A Multicenter Comparison of Radiomic Features from Different Software Packages on Digital Reference Objects and Patient Data Sets","volume":"6","author":"McNitt-Gray","year":"2020","journal-title":"Tomography"},{"key":"10.1016\/j.critrevonc.2024.104479_bib149","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1186\/s13244-023-01415-8","article-title":"CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII","volume":"14","author":"Kocak","year":"2023","journal-title":"Insights Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib150","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1007\/s10278-021-00576-6","article-title":"Design and development of a medical image databank for assisting studies in radiomics","volume":"35","author":"Kundu","year":"2022","journal-title":"J. Digit Imaging"},{"key":"10.1016\/j.critrevonc.2024.104479_bib151","article-title":"Preliminary exploration of response the course of radiotherapy for stage III non-small cell lung cancer based on longitudinal CT radiomics features","volume":"9","author":"Zhang","year":"2021","journal-title":"Eur. J. Radio. Open"},{"key":"10.1016\/j.critrevonc.2024.104479_bib152","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-021-22989-1","article-title":"Integration of machine learning and genome-scale metabolic modeling identifies multi-omics biomarkers for radiation resistance","volume":"12","author":"Lewis","year":"2021","journal-title":"Nat. Commun."},{"key":"10.1016\/j.critrevonc.2024.104479_bib153","doi-asserted-by":"crossref","DOI":"10.1001\/jamasurg.2020.5601","article-title":"A Genomic-Pathologic Annotated Risk Model to Predict Recurrence in Early-Stage Lung Adenocarcinoma","volume":"156","author":"Jones","year":"2021","journal-title":"JAMA Surg."},{"key":"10.1016\/j.critrevonc.2024.104479_bib154","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.ymeth.2020.11.005","article-title":"Systematic review of radiomic biomarkers for predicting immune checkpoint inhibitor treatment outcomes","volume":"188","author":"Zhang","year":"2021","journal-title":"Methods"},{"key":"10.1016\/j.critrevonc.2024.104479_bib155","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0231227","article-title":"Deciphering the tumor microenvironment through radiomics in non-small cell lung cancer: Correlation with immune profiles","volume":"15","author":"Yoon","year":"2020","journal-title":"PLoS One"},{"key":"10.1016\/j.critrevonc.2024.104479_bib156","article-title":"Value of CT-Based Radiomics in Predicating the Efficacy of Anti-HER2 Therapy for Patients With Liver Metastases From Breast Cancer","volume":"12","author":"He","year":"2022","journal-title":"Front Oncol."},{"key":"10.1016\/j.critrevonc.2024.104479_bib157","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1148\/radiol.2018180946","article-title":"Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type","volume":"290","author":"Kniep","year":"2019","journal-title":"Radiology"}],"container-title":["Critical Reviews in Oncology\/Hematology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1040842824002221?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1040842824002221?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,9,12]],"date-time":"2024-09-12T20:23:45Z","timestamp":1726172625000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1040842824002221"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11]]},"references-count":157,"alternative-id":["S1040842824002221"],"URL":"https:\/\/doi.org\/10.1016\/j.critrevonc.2024.104479","relation":{},"ISSN":["1040-8428"],"issn-type":[{"value":"1040-8428","type":"print"}],"subject":[],"published":{"date-parts":[[2024,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Clinical applications of radiomics and deep learning in breast and lung cancer: A narrative literature review on current evidence and future perspectives","name":"articletitle","label":"Article Title"},{"value":"Critical Reviews in Oncology\/Hematology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.critrevonc.2024.104479","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"104479"}}