{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T03:25:19Z","timestamp":1788751519714,"version":"build-2803163510"},"reference-count":69,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T00:00:00Z","timestamp":1709856000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Science and Higher Education of the Russian Federation","award":["FENW-2023-0019"],"award-info":[{"award-number":["FENW-2023-0019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>The role of machine learning (a part of artificial intelligence\u2014AI) in the diagnosis and treatment of various types of oncology is steadily increasing. It is expected that the use of AI in oncology will speed up both diagnostic and treatment planning processes. This review describes recent applications of machine learning in oncology, including medical image analysis, treatment planning, patient survival prognosis, and the synthesis of drugs at the point of care. The fast and reliable analysis of medical images is of great importance in the case of fast-flowing forms of cancer. The introduction of ML for the analysis of constantly growing volumes of big data makes it possible to improve the quality of prescribed treatment and patient care. Thus, ML is expected to become an essential technology for medical specialists. The ML model has already improved prognostic prediction for patients compared to traditional staging algorithms. The direct synthesis of the necessary medical substances (small molecule mixtures) at the point of care could also seriously benefit from the application of ML. We further review the main trends in the use of artificial intelligence-based technologies in modern oncology. This review demonstrates the future prospects of using ML tools to make progress in cancer research, as well as in other areas of medicine. Despite growing interest in the use of modern computer technologies in medical practice, a number of unresolved ethical and legal problems remain. In this review, we also discuss the most relevant issues among them.<\/jats:p>","DOI":"10.3390\/cancers16061100","type":"journal-article","created":{"date-parts":[[2024,3,8]],"date-time":"2024-03-08T11:47:30Z","timestamp":1709898450000},"page":"1100","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Machine Learning Meets Cancer"],"prefix":"10.3390","volume":"16","author":[{"given":"Elena V.","family":"Varlamova","sequence":"first","affiliation":[{"name":"The Smart Materials Research Institute, Southern Federal University, 178\/24 Sladkova Str., 344090 Rostov-on-Don, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6167-7942","authenticated-orcid":false,"given":"Maria A.","family":"Butakova","sequence":"additional","affiliation":[{"name":"The Smart Materials Research Institute, Southern Federal University, 178\/24 Sladkova Str., 344090 Rostov-on-Don, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vlada V.","family":"Semyonova","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, HSE University, 20 Myasnitskaya Str., 101000 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sergey A.","family":"Soldatov","sequence":"additional","affiliation":[{"name":"The Smart Materials Research Institute, Southern Federal University, 178\/24 Sladkova Str., 344090 Rostov-on-Don, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Artem V.","family":"Poltavskiy","sequence":"additional","affiliation":[{"name":"The Smart Materials Research Institute, Southern Federal University, 178\/24 Sladkova Str., 344090 Rostov-on-Don, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Oleg I.","family":"Kit","sequence":"additional","affiliation":[{"name":"National Medical Research Centre for Oncology, 63 14 Liniya Str., 344037 Rostov-on-Don, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alexander V.","family":"Soldatov","sequence":"additional","affiliation":[{"name":"The Smart Materials Research Institute, Southern Federal University, 178\/24 Sladkova Str., 344090 Rostov-on-Don, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.ymeth.2020.06.003","article-title":"Radiomics in neuro-oncology: Basics, workflow, and applications","volume":"188","author":"Lohmann","year":"2021","journal-title":"Methods"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sleeman, W.C., Nalluri, J., Syed, K., Ghosh, P., Krawczyk, B., Hagan, M., Palta, J., and Kapoor, R. (2020). A Machine Learning method for relabeling arbitrary DICOM structure sets to TG-263 defined labels. J. Biomed. Inform., 109.","DOI":"10.1016\/j.jbi.2020.103527"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.semcancer.2018.07.001","article-title":"Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning","volume":"52","author":"Klauschen","year":"2018","journal-title":"Semin. Cancer Biol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.radonc.2020.09.054","article-title":"From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community","volume":"153","author":"Kazmierska","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1053\/j.semnuclmed.2020.08.003","article-title":"Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives","volume":"51","author":"Seifert","year":"2021","journal-title":"Semin. Nucl. Med."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1016\/j.radonc.2019.11.019","article-title":"Distributed learning on 20,000+ lung cancer patients\u2014The Personal Health Train","volume":"144","author":"Deist","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.canlet.2016.05.033","article-title":"Big Data and machine learning in radiation oncology: State of the art and future prospects","volume":"382","author":"Bibault","year":"2016","journal-title":"Cancer Lett."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"104139","DOI":"10.1016\/j.ijmedinf.2020.104139","article-title":"Machine learning application for incident prostate adenocarcinomas automatic registration in a French regional cancer registry","volume":"139","author":"Fabacher","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Soldatov, S.A., Pashkov, D., Guda, S., Karnaukhov, N., Guda, A., and Soldatov, A. (2022). Deep Learning Classification of Colorectal Lesions Based on Whole Slide Images. Algorithms, 15.","DOI":"10.3390\/a15110398"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"182","DOI":"10.1016\/j.ygyno.2020.10.004","article-title":"Natural language processing with machine learning to predict outcomes after ovarian cancer surgery","volume":"160","author":"Barber","year":"2021","journal-title":"Gynecol. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Bitencourt, A.G.V., Gibbs, P., Rossi Saccarelli, C., Daimiel, I., Lo Gullo, R., Fox, M.J., Thakur, S., Pinker, K., Morris, E.A., and Morrow, M. (2020). MRI-based machine learning radiomics can predict HER2 expression level and pathologic response after neoadjuvant therapy in HER2 overexpressing breast cancer. EBioMedicine, 61.","DOI":"10.1016\/j.ebiom.2020.103042"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1038\/s41598-020-80570-0","article-title":"Breath biopsy of breast cancer using sensor array signals and machine learning analysis","volume":"11","author":"Yang","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100907","DOI":"10.1016\/j.tranon.2020.100907","article-title":"Early lung cancer diagnostic biomarker discovery by machine learning methods","volume":"14","author":"Xie","year":"2021","journal-title":"Transl. Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"121444","DOI":"10.1016\/j.talanta.2020.121444","article-title":"Electrochemical and optical detection and machine learning applied to images of genosensors for diagnosis of prostate cancer with the biomarker PCA3","volume":"222","author":"Rodrigues","year":"2021","journal-title":"Talanta"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2249","DOI":"10.1182\/blood.2020005488","article-title":"Machine learning demonstrates that somatic mutations imprint invariant morphologic features in myelodysplastic syndromes","volume":"136","author":"Nagata","year":"2020","journal-title":"Blood"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"208","DOI":"10.1111\/bjh.14297","article-title":"CRISPR -Cas9 technology and its application in haematological disorders","volume":"175","author":"Zhang","year":"2016","journal-title":"Br. J. Haematol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1858","DOI":"10.1016\/j.celrep.2016.01.059","article-title":"Therapeutic Response to Non-genotoxic Activation of p53 by Nutlin3a Is Driven by PUMA-Mediated Apoptosis in Lymphoma Cells","volume":"14","author":"Valente","year":"2016","journal-title":"Cell Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"479","DOI":"10.1038\/nature.2016.20988","article-title":"CRISPR gene-editing tested in a person for the first time","volume":"539","author":"Cyranoski","year":"2016","journal-title":"Nature"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e20","DOI":"10.1093\/nar\/gkaa1158","article-title":"CRISPRidentify: Identification of CRISPR arrays using machine learning approach","volume":"49","author":"Mitrofanov","year":"2021","journal-title":"Nucleic Acids Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.celrep.2018.03.046","article-title":"Machine Learning Detects Pan-cancer Ras Pathway Activation in The Cancer Genome Atlas","volume":"23","author":"Way","year":"2018","journal-title":"Cell Rep."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_22","first-page":"563","article-title":"Deep ensemble learning for skin lesions classification with convolutional neural network","volume":"10","author":"Pratiwi","year":"2021","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.semcancer.2020.08.006","article-title":"Deep computational pathology in breast cancer","volume":"72","author":"Duggento","year":"2021","journal-title":"Semin. Cancer Biol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.media.2016.06.037","article-title":"Image analysis and machine learning in digital pathology: Challenges and opportunities","volume":"33","author":"Madabhushi","year":"2016","journal-title":"Med. Image Anal."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Wang, S., Yang, D.M., Rong, R., Zhan, X., Fujimoto, J., Liu, H., Minna, J., Wistuba, I.I., Xie, Y., and Xiao, G. (2019). Artificial Intelligence in Lung Cancer Pathology Image Analysis. Cancers, 11.","DOI":"10.3390\/cancers11111673"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.media.2019.03.014","article-title":"Fast and accurate tumor segmentation of histology images using persistent homology and deep convolutional features","volume":"55","author":"Qaiser","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"11579","DOI":"10.1038\/s41598-021-90444-8","article-title":"A generalized deep learning framework for whole-slide image segmentation and analysis","volume":"11","author":"Khened","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4294","DOI":"10.1038\/s41467-020-18147-8","article-title":"Clinically applicable histopathological diagnosis system for gastric cancer detection using deep learning","volume":"11","author":"Song","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.ejmp.2020.07.028","article-title":"Machine learning in quantitative PET: A review of attenuation correction and low-count image reconstruction methods","volume":"76","author":"Wang","year":"2020","journal-title":"Phys. Medica"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1038\/s41598-020-80068-9","article-title":"Automated segmentation of endometrial cancer on MR images using deep learning","volume":"11","author":"Hodneland","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.jacc.2015.10.090","article-title":"U.S. Hospital Use of Echocardiography","volume":"67","author":"Papolos","year":"2016","journal-title":"J. Am. Coll. Cardiol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1038\/s41746-020-00376-2","article-title":"Deep learning-enabled medical computer vision","volume":"4","author":"Esteva","year":"2021","journal-title":"NPJ Digit. Med."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.5858\/arpa.2017-0496-CP","article-title":"US Food and Drug Administration Approval of Whole Slide Imaging for Primary Diagnosis: A Key Milestone Is Reached and New Questions Are Raised","volume":"142","author":"Evans","year":"2018","journal-title":"Arch. Pathol. Lab. Med."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101813","DOI":"10.1016\/j.media.2020.101813","article-title":"Deep neural network models for computational histopathology: A survey","volume":"67","author":"Srinidhi","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"102172","DOI":"10.1016\/j.nicl.2020.102172","article-title":"Distinguishing between paediatric brain tumour types using multi-parametric magnetic resonance imaging and machine learning: A multi-site study","volume":"25","author":"Grist","year":"2020","journal-title":"Neuroimage Clin."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.crad.2019.07.001","article-title":"Machine learning and glioma imaging biomarkers","volume":"75","author":"Booth","year":"2020","journal-title":"Clin. Radiol."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1016\/j.procs.2020.04.064","article-title":"Breast Cancer Prediction using varying Parameters of Machine Learning Models","volume":"171","author":"Gupta","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.jaad.2020.04.069","article-title":"Machine learning for prediction of cutaneous adverse events in patients receiving anti\u2013PD-1 immunotherapy","volume":"84","author":"Lewinson","year":"2021","journal-title":"J. Am. Acad. Dermatol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.oraloncology.2019.03.011","article-title":"Machine learning to predict occult nodal metastasis in early oral squamous cell carcinoma","volume":"92","author":"Bur","year":"2019","journal-title":"Oral Oncol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.breast.2019.11.009","article-title":"Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy","volume":"49","author":"Morris","year":"2020","journal-title":"Breast"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1016\/j.lungcan.2020.07.011","article-title":"Machine learning-based histological classification that predicts recurrence of peripheral lung squamous cell carcinoma","volume":"147","author":"Koike","year":"2020","journal-title":"Lung Cancer"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"100906","DOI":"10.1016\/j.tranon.2020.100906","article-title":"Prediction of post-radiotherapy locoregional progression in HPV-associated oropharyngeal squamous cell carcinoma using machine-learning analysis of baseline PET\/CT radiomics","volume":"14","author":"Haider","year":"2021","journal-title":"Transl. Oncol."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"101822","DOI":"10.1016\/j.artmed.2020.101822","article-title":"Automated machine learning: Review of the state-of-the-art and opportunities for healthcare","volume":"104","author":"Waring","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.1016\/j.csbj.2020.08.019","article-title":"Artificial intelligence (AI) and big data in cancer and precision oncology","volume":"18","author":"Dlamini","year":"2020","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.semcancer.2019.12.011","article-title":"Machine and deep learning approaches for cancer drug repurposing","volume":"68","author":"Issa","year":"2021","journal-title":"Semin. Cancer Biol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"104932","DOI":"10.1016\/j.phrs.2020.104932","article-title":"Beyond the limitation of targeted therapy: Improve the application of targeted drugs combining genomic data with machine learning","volume":"159","author":"Miao","year":"2020","journal-title":"Pharmacol. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1016\/j.brachy.2020.03.004","article-title":"Conventional vs machine learning\u2013based treatment planning in prostate brachytherapy: Results of a Phase I randomized controlled trial","volume":"19","author":"Nicolae","year":"2020","journal-title":"Brachytherapy"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.radonc.2020.04.013","article-title":"Dosimetric predictors of patient-reported toxicity after prostate stereotactic body radiotherapy: Analysis of full range of the dose\u2013volume histogram using ensemble machine learning","volume":"148","author":"Pan","year":"2020","journal-title":"Radiother. Oncol."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yang, Z., Olszewski, D., He, C., Pintea, G., Lian, J., Chou, T., Chen, R.C., and Shtylla, B. (2021). Machine learning and statistical prediction of patient quality-of-life after prostate radiation therapy. Comput. Biol. Med., 129.","DOI":"10.1016\/j.compbiomed.2020.104127"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ejca.2020.09.015","article-title":"Machine learning reveals a PD-L1\u2013independent prediction of response to immunotherapy of non-small cell lung cancer by gene expression context","volume":"140","author":"Wiesweg","year":"2020","journal-title":"Eur. J. Cancer"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.canlet.2020.03.032","article-title":"Machine Learning in oncology: A clinical appraisal","volume":"481","author":"Cuocolo","year":"2020","journal-title":"Cancer Lett."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Nazari, M., Shiri, I., and Zaidi, H. (2021). Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients. Comput. Biol. Med., 129.","DOI":"10.1016\/j.compbiomed.2020.104135"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e1147","DOI":"10.1016\/j.wneu.2020.11.113","article-title":"Meningioma Consistency Can Be Defined by Combining the Radiomic Features of Magnetic Resonance Imaging and Ultrasound Elastography. A Pilot Study Using Machine Learning Classifiers","volume":"146","author":"Cepeda","year":"2021","journal-title":"World Neurosurg."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"158.e19","DOI":"10.1016\/j.crad.2020.08.038","article-title":"Radiomics-based machine-learning method for prediction of distant metastasis from soft-tissue sarcomas","volume":"76","author":"Tian","year":"2021","journal-title":"Clin. Radiol."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ymeth.2020.07.003","article-title":"Reinventing radiation therapy with machine learning and imaging bio-markers (radiomics): State-of-the-art, challenges and perspectives","volume":"188","author":"Dercle","year":"2021","journal-title":"Methods"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1016\/j.adro.2020.07.007","article-title":"Prediction of Survival and Recurrence Patterns by Machine Learning in Gastric Cancer Cases Undergoing Radiation Therapy and Chemotherapy","volume":"5","author":"Akcay","year":"2020","journal-title":"Adv. Radiat. Oncol."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"108755","DOI":"10.1016\/j.ejrad.2019.108755","article-title":"Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma","volume":"122","author":"Wang","year":"2020","journal-title":"Eur. J. Radiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.ejmp.2020.01.009","article-title":"Overlooked pitfalls in multi-class machine learning classification in radiation oncology and how to avoid them","volume":"70","author":"Chatterjee","year":"2020","journal-title":"Phys. Medica"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.mri.2020.09.017","article-title":"Machine learning-based radiomic, clinical and semantic feature analysis for predicting overall survival and MGMT promoter methylation status in patients with glioblastoma","volume":"74","author":"Lu","year":"2020","journal-title":"Magn. Reason. Imaging"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.canlet.2017.06.004","article-title":"Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma","volume":"403","author":"Zhang","year":"2017","journal-title":"Cancer Lett."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/j.wneu.2019.01.046","article-title":"Machine Learning in Neuro-Oncology: Can Data Analysis From 5346 Patients Change Decision-Making Paradigms?","volume":"124","author":"Sarkiss","year":"2019","journal-title":"World Neurosurg."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1016\/j.phro.2020.10.008","article-title":"Automatic evaluation of contours in radiotherapy planning utilising conformity indices and machine learning","volume":"16","author":"Terparia","year":"2020","journal-title":"Phys. Imaging Radiat. Oncol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"104313","DOI":"10.1016\/j.ijmedinf.2020.104313","article-title":"Comparison of nomogram with machine learning techniques for prediction of overall survival in patients with tongue cancer","volume":"145","author":"Alabi","year":"2021","journal-title":"Int. J. Med. Inform."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"e842","DOI":"10.1016\/j.wneu.2018.07.276","article-title":"Development of Machine Learning Algorithms for Prediction of 5-Year Spinal Chordoma Survival","volume":"119","author":"Karhade","year":"2018","journal-title":"World Neurosurg."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"104140","DOI":"10.1016\/j.ijmedinf.2020.104140","article-title":"Predicting in-hospital mortality of patients with febrile neutropenia using machine learning models","volume":"139","author":"Du","year":"2020","journal-title":"Int. J. Med. Inform."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"744","DOI":"10.1016\/j.ygyno.2020.09.047","article-title":"Using machine learning to create prognostic systems for endometrial cancer","volume":"159","author":"Praiss","year":"2020","journal-title":"Gynecol. Oncol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"993","DOI":"10.1200\/CCI.20.00059","article-title":"Improving Cancer Data Interoperability: The Promise of the Minimal Common Oncology Data Elements (mCODE) Initiative","volume":"4","author":"Osterman","year":"2020","journal-title":"JCO Clin. Cancer Inform."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1846","DOI":"10.1093\/annonc\/mdv181","article-title":"Patient-reported outcomes in routine cancer clinical practice: A scoping review of use, impact on health outcomes, and implementation factors","volume":"26","author":"Howell","year":"2015","journal-title":"Ann. Oncol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4138","DOI":"10.1002\/cam4.3935","article-title":"Artificial intelligence in oncology: Path to implementation","volume":"10","author":"Chua","year":"2021","journal-title":"Cancer Med."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/16\/6\/1100\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:11:12Z","timestamp":1760105472000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/16\/6\/1100"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,8]]},"references-count":69,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2024,3]]}},"alternative-id":["cancers16061100"],"URL":"https:\/\/doi.org\/10.3390\/cancers16061100","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,8]]}}}