{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T12:56:25Z","timestamp":1784552185361,"version":"3.55.0"},"reference-count":176,"publisher":"Elsevier BV","issue":"6","license":[{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"am","delay-in-days":386,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T00:00:00Z","timestamp":1698796800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biochimica et Biophysica Acta (BBA) - Reviews on Cancer"],"published-print":{"date-parts":[[2023,11]]},"DOI":"10.1016\/j.bbcan.2023.189026","type":"journal-article","created":{"date-parts":[[2023,11,20]],"date-time":"2023-11-20T23:48:18Z","timestamp":1700524098000},"page":"189026","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":39,"title":["Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers"],"prefix":"10.1016","volume":"1878","author":[{"given":"Pankaj","family":"Garg","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Atish","family":"Mohanty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sravani","family":"Ramisetty","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prakash","family":"Kulkarni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Horne","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Evan","family":"Pisick","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ravi","family":"Salgia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sharad S.","family":"Singhal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.bbcan.2023.189026_bb0005","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1002\/ijc.28990","article-title":"Cancer incidence, survival, and mortality: explaining the concepts","volume":"135","author":"Ellis","year":"2014","journal-title":"Int. J. Cancer"},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0010","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1001\/jamaoncol.2021.6987","article-title":"Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 Cancer groups from 2010 to 2019: a systematic analysis for the global burden of disease study 2019","volume":"8","author":"Kocarnik","year":"2022","journal-title":"JAMA Oncol."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0015","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1093\/annonc\/mdw604","article-title":"Causes of death among cancer patients","volume":"28","author":"Zaorsky","year":"2017","journal-title":"Ann. Oncol."},{"issue":"4","key":"10.1016\/j.bbcan.2023.189026_bb0020","doi-asserted-by":"crossref","first-page":"217","DOI":"10.2991\/jegh.k.191008.001","article-title":"Current Cancer epidemiology","volume":"9","author":"Mattiuzzi","year":"2019","journal-title":"J. Epidemiol. Glob. Health"},{"key":"10.1016\/j.bbcan.2023.189026_bb0025","first-page":"961","article-title":"Innovative approaches for cancer treatment: current perspectives and new challenges","volume":"13","author":"Pucci","year":"2019","journal-title":"Ecancer Med. Sci."},{"issue":"4","key":"10.1016\/j.bbcan.2023.189026_bb0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.bbcan.2023.188929","article-title":"The innate effects of plant secondary metabolites in preclusion of gynecologic cancers: inflammatory response and therapeutic action","volume":"1878","author":"Garg","year":"2023","journal-title":"Biochim Biophys Acta - Reviews on Cancer."},{"key":"10.1016\/j.bbcan.2023.189026_bb0035","doi-asserted-by":"crossref","DOI":"10.1177\/20503121211034366","article-title":"New approaches and procedures for cancer treatment: current perspectives","volume":"9","author":"Debela","year":"2021","journal-title":"SAGE Open Med."},{"issue":"4","key":"10.1016\/j.bbcan.2023.189026_bb0040","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1038\/s41591-022-01738-x","article-title":"Priorities for cancer research in low and middle-income countries: a global perspective","volume":"28","author":"Pramesh","year":"2022","journal-title":"Nat. Med."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0045","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.soncn.2017.02.002","article-title":"Cancer screening and early detection in the 21st century","volume":"33","author":"Loud","year":"2017","journal-title":"Semin. Oncol. Nurs."},{"key":"10.1016\/j.bbcan.2023.189026_bb0050","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1186\/s12885-019-5746-6","article-title":"Evaluating diagnostic strategies for early detection of cancer: the can test framework","volume":"19","author":"Walter","year":"2019","journal-title":"BMC Cancer"},{"key":"10.1016\/j.bbcan.2023.189026_bb0055","article-title":"Soft computing: goals, importance and various problem solving techniques","volume":"1(3):522","author":"Agrawal","year":"2023","journal-title":"J. Integr. Sci. Technol."},{"issue":"6","key":"10.1016\/j.bbcan.2023.189026_bb0060","doi-asserted-by":"crossref","first-page":"1524","DOI":"10.3390\/cancers14061524","article-title":"The role of artificial intelligence in early cancer diagnosis","volume":"14","author":"Hunter","year":"2022","journal-title":"Cancers (Basel)."},{"key":"10.1016\/j.bbcan.2023.189026_bb0065","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1200\/CCI.20.00072","article-title":"Machine learning in oncology: methods, applications, and challenges","volume":"4","author":"Bertsimas","year":"2020","journal-title":"JCO Clinical Cancer Informatics"},{"key":"10.1016\/j.bbcan.2023.189026_bb0070","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s13073-021-00968-x","article-title":"Deep learning in cancer diagnosis, prognosis, and treatment selection","volume":"13","author":"Tran","year":"2021","journal-title":"Genome Med."},{"issue":"9","key":"10.1016\/j.bbcan.2023.189026_bb0075","first-page":"1149","article-title":"Mishra I AI in health sciences","volume":"24","author":"Mishra","year":"2023","journal-title":"Curr. Pharm. Biotechnol."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0080","doi-asserted-by":"crossref","first-page":"94","DOI":"10.7861\/futurehosp.6-2-94","article-title":"The potential for artificial intelligence in healthcare","volume":"6","author":"Davenport","year":"2019","journal-title":"Future Health J."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0085","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1007\/s11030-022-10489-3","article-title":"Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system","volume":"27","author":"Gautam","year":"2023","journal-title":"Mol. Divers."},{"key":"10.1016\/j.bbcan.2023.189026_bb0090","doi-asserted-by":"crossref","first-page":"803","DOI":"10.21037\/tcr.2018.05.02","article-title":"The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review","volume":"7","author":"Vial","year":"2018","journal-title":"Transl. Cancer Res."},{"key":"10.1016\/j.bbcan.2023.189026_bb0095","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1186\/s12911-021-01488-9","article-title":"The role of artificial intelligence in healthcare: a structured literature review","volume":"21","author":"Secinaro","year":"2021","journal-title":"BMC Med. Inform. Decis. Mak."},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0100","doi-asserted-by":"crossref","first-page":"87","DOI":"10.35711\/aimi.v1.i3.87","article-title":"Current trends of artificial intelligence in cancer imaging","volume":"1","author":"Verde","year":"2020","journal-title":"Artif Intell Med Imaging"},{"key":"10.1016\/j.bbcan.2023.189026_bb0105","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1038\/s41467-017-02465-5","article-title":"A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia","volume":"9","author":"Lee","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bbcan.2023.189026_bb0110","first-page":"1","article-title":"Introduction to neural networks in healthcare","author":"Sordo","year":"2002","journal-title":"Open Clinical"},{"key":"10.1016\/j.bbcan.2023.189026_bb0115","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.csbj.2014.11.005","article-title":"Machine learning applications in cancer prognosis and prediction","volume":"13","author":"Kourou","year":"2015","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.bbcan.2023.189026_bb0120","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.ymeth.2016.08.010","article-title":"Perspectives on making big data analytics work for oncology","volume":"111","author":"El Naqa","year":"2016","journal-title":"Methods."},{"key":"10.1016\/j.bbcan.2023.189026_bb0125","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.coisb.2017.07.007","article-title":"Big data in cancer genomics","volume":"4","author":"Maia","year":"2017","journal-title":"Curr. Opini. Syst. Biol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0130","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1007\/s10586-022-03707-y","article-title":"Popular deep learning algorithms for disease prediction: a review","volume":"26","author":"Yu","year":"2023","journal-title":"Clust. Comput."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0135","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s44163-023-00049-5","article-title":"Evaluation of artificial intelligence techniques in disease diagnosis and prediction","volume":"3","author":"Nia","year":"2023","journal-title":"Discov Artif Intell"},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0140","doi-asserted-by":"crossref","first-page":"541","DOI":"10.3390\/healthcare10030541","article-title":"Machine learning based disease diagnosis: a comprehensive review","volume":"10","author":"Ahsan","year":"2022","journal-title":"Healthcare (Basel)."},{"key":"10.1016\/j.bbcan.2023.189026_bb0145","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1186\/s13023-020-01424-6","article-title":"The use of machine learning in rare diseases: a scoping review","volume":"15","author":"Schaefer","year":"2020","journal-title":"Orphanet J. Rare Dis."},{"issue":"24","key":"10.1016\/j.bbcan.2023.189026_bb0150","doi-asserted-by":"crossref","first-page":"13409","DOI":"10.3390\/ijerph182413409","article-title":"Machine learning and its application in skin cancer","volume":"18","author":"Das","year":"2021","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.bbcan.2023.189026_bb0155","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":"10.1016\/j.bbcan.2023.189026_bb0160","doi-asserted-by":"crossref","DOI":"10.3389\/fbioe.2023.982111","article-title":"Methods to improve the accuracy of next-generation sequencing","volume":"11","author":"Cheng","year":"2023","journal-title":"Front. Bioeng. Biotechnol."},{"issue":"11","key":"10.1016\/j.bbcan.2023.189026_bb0165","doi-asserted-by":"crossref","DOI":"10.1101\/cshperspect.a036798","article-title":"Next-generation sequencing technologies","volume":"9","author":"McCombie","year":"2019","journal-title":"Cold Spring Harb. Perspect. Med."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0170","doi-asserted-by":"crossref","first-page":"1630","DOI":"10.3390\/cancers11111630","article-title":"Applications of bioinformatics in cancer","volume":"11","author":"Brenner","year":"2019","journal-title":"Cancers (Basel)"},{"issue":"6654","key":"10.1016\/j.bbcan.2023.189026_bb0175","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1126\/science.adj3312","article-title":"Opfermann artificial intelligence meets medical robotics","volume":"381","author":"Yip","year":"2023","journal-title":"Science."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0180","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1097\/SLA.0000000000002693","article-title":"Artificial intelligence in surgery: promises and perils","volume":"268","author":"Hashimoto","year":"2018","journal-title":"Ann. Surg."},{"issue":"4","key":"10.1016\/j.bbcan.2023.189026_bb0185","doi-asserted-by":"crossref","first-page":"900","DOI":"10.1158\/2159-8290.CD-21-0090","article-title":"Artificial intelligence in cancer research and precision medicine","volume":"1","author":"Bhinder","year":"2021","journal-title":"Cancer Discov."},{"issue":"7","key":"10.1016\/j.bbcan.2023.189026_bb0190","doi-asserted-by":"crossref","first-page":"8459","DOI":"10.1007\/s12652-021-03612-z","article-title":"Artificial intelligence in disease diagnosis: a systematic literature review, synthesizing framework, and future research agenda","volume":"14","author":"Kumar","year":"2023","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"10.1016\/j.bbcan.2023.189026_bb0195","article-title":"Grand challenges for artificial intelligence in molecular medicine","volume":"1","author":"Streib","year":"2021","journal-title":"Front. Mol. Med."},{"key":"10.1016\/j.bbcan.2023.189026_bb0200","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1038\/s43856-022-00199-0","article-title":"Artificial intelligence and machine learning in cancer imaging","volume":"2","author":"Koh","year":"2022","journal-title":"Commun. Med."},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0205","doi-asserted-by":"crossref","first-page":"411","DOI":"10.2174\/1389201023666220610163343","article-title":"Overview of advancement in biosensing technology, including its applications in healthcare","volume":"24","author":"Mishra","year":"2023","journal-title":"Curr. Pharm. Biotechnol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0210","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12935-021-01981-1","article-title":"Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future","volume":"21","author":"Iqbal","year":"2021","journal-title":"Cancer Cell Int."},{"key":"10.1016\/j.bbcan.2023.189026_bb0215","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1186\/s13244-023-01412-x","article-title":"Machine learning combined with radiomics, and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors","volume":"14","author":"Jan","year":"2023","journal-title":"Insights Imaging"},{"issue":"5","key":"10.1016\/j.bbcan.2023.189026_bb0220","doi-asserted-by":"crossref","first-page":"1081","DOI":"10.3390\/math11051081","article-title":"A review on nature-inspired algorithms for Cancer disease prediction and classification","volume":"11","author":"Yaqoob","year":"2023","journal-title":"Mathematics."},{"issue":"11","key":"10.1016\/j.bbcan.2023.189026_bb0225","article-title":"Role of artificial intelligence and machine learning in prediction, diagnosis, and prognosis of Cancer","volume":"14","author":"Gaur","year":"2022","journal-title":"Cureus."},{"issue":"4","key":"10.1016\/j.bbcan.2023.189026_bb0230","article-title":"Quantitative imaging feature pipeline: a web-based tool for utilizing, sharing, and building image-processing pipelines","volume":"7","author":"Mattonen","year":"2020","journal-title":"J Med Imaging (Bellingham)."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0235","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3892\/ijo.2020.5063","article-title":"Artificial intelligence radiogenomics for advancing precision and effectiveness in oncologic care (review)","volume":"57","author":"Trivizakis","year":"2020","journal-title":"Int. J. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0240","series-title":"In Radiomics and Radiogenomics: Technical Basis and Clinical Applications","first-page":"3","author":"Napel","year":"2019"},{"key":"10.1016\/j.bbcan.2023.189026_bb0245","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1148\/rg.2017160130","article-title":"Machine learning for medical imaging","volume":"37","author":"Erickson","year":"2017","journal-title":"Radiographics"},{"key":"10.1016\/j.bbcan.2023.189026_bb0250","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13244-019-0795-6","article-title":"Combining molecular and imaging metrics in cancer: radiogenomics","volume":"11","author":"Lo Gullo","year":"2020","journal-title":"Insights Imaging"},{"issue":"6","key":"10.1016\/j.bbcan.2023.189026_bb0255","doi-asserted-by":"crossref","first-page":"919","DOI":"10.3390\/diagnostics11060919","article-title":"Radiomics and machine learning with multi-parametric breast MRI for improved diagnostic accuracy in breast cancer diagnosis","volume":"11","author":"Daimiel Naranjo","year":"2021","journal-title":"Diagnostics."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0260","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1002\/jmri.22009","article-title":"Computer-aided detection of metastatic brain tumors using automated three-dimensional template matching","volume":"31","author":"Ambrosini","year":"2009","journal-title":"J. Magn. Reson. Imaging"},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0265","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1148\/radiol.2016150063","article-title":"Low-dose CT screening for lung cancer: computer-aided detection of missed lung cancers","volume":"281","author":"Liang","year":"2016","journal-title":"Radiology."},{"issue":"12","key":"10.1016\/j.bbcan.2023.189026_bb0270","doi-asserted-by":"crossref","first-page":"2967","DOI":"10.1016\/S0031-3203(03)00192-4","article-title":"Computer-aided detection, and classification of micro-calcifications in mammograms: a survey","volume":"36","author":"Cheng","year":"2003","journal-title":"Pattern Recogn."},{"issue":"10","key":"10.1016\/j.bbcan.2023.189026_bb0275","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1016\/j.jacr.2010.05.019","article-title":"How widely is computer-aided detection used in screening and diagnostic mammography?","volume":"7","author":"Rao","year":"2010","journal-title":"J. Am. Coll. Radiol."},{"issue":"8","key":"10.1016\/j.bbcan.2023.189026_bb0280","doi-asserted-by":"crossref","first-page":"1364","DOI":"10.1016\/j.ajpath.2021.01.014","article-title":"Tumor microenvironment and the role of artificial intelligence in breast cancer detection and prognosis","volume":"191","author":"Malherbe","year":"2021","journal-title":"Am. J. Pathol."},{"issue":"11","key":"10.1016\/j.bbcan.2023.189026_bb0285","article-title":"Role of artificial intelligence and machine learning in prediction, diagnosis, and prognosis of Cancer","volume":"14","author":"Gaur","year":"2022","journal-title":"Cureus."},{"key":"10.1016\/j.bbcan.2023.189026_bb0290","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1186\/s12935-021-01981-1","article-title":"Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future","volume":"21","author":"Iqbal","year":"2021","journal-title":"Cancer Cell Int."},{"key":"10.1016\/j.bbcan.2023.189026_bb0295","article-title":"Prostate cancer diagnosis using deep learning with 3D multi-parametric MRI","author":"Liu","year":"2017","journal-title":"Medical Imaging"},{"key":"10.1016\/j.bbcan.2023.189026_bb0300","doi-asserted-by":"crossref","first-page":"e303","DOI":"10.1016\/S2589-7500(20)30078-9","article-title":"Deep learning top distinguish pancreatic cancer tissue from non-cancerous pancreatic tissue: a retrospective study with cross racial external validation","volume":"2","author":"Liu","year":"2020","journal-title":"Lancet Digit. Health"},{"issue":"4","key":"10.1016\/j.bbcan.2023.189026_bb0305","article-title":"PROSTATEx challenges for computerized classification of prostate lesions from multi-parametric magnetic resonance images","volume":"5","author":"Armato","year":"2018","journal-title":"J Med Imaging (Bellingham)."},{"key":"10.1016\/j.bbcan.2023.189026_bb0310","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.radonc.2018.10.019","article-title":"Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer","volume":"132","author":"Wang","year":"2019","journal-title":"Radiother. Oncol. J. Eur. Soc. Ther. Radiol. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0315","article-title":"Identification of prognosis biomarkers for high-grade serous ovarian cancer based on stemness","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Genet."},{"key":"10.1016\/j.bbcan.2023.189026_bb0320","doi-asserted-by":"crossref","first-page":"906","DOI":"10.1186\/s12885-021-08647-1","article-title":"Using a machine learning approach to identify key prognostic molecules for esophageal squamous cell carcinoma","volume":"21","author":"Li","year":"2021","journal-title":"BMC Cancer"},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0325","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1016\/j.ophtha.2020.07.045","article-title":"Detection of progressive glaucomatous optic nerve damage on fundus photographs with deep learning","volume":"128","author":"Medeiros","year":"2021","journal-title":"Ophthalmology."},{"key":"10.1016\/j.bbcan.2023.189026_bb0330","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2021.102164","article-title":"Artificial intelligence in gynecologic cancers: current status and future challenges- a systematic review","volume":"120","author":"Akazawa","year":"2021","journal-title":"Artif. Intell. Med."},{"issue":"8","key":"10.1016\/j.bbcan.2023.189026_bb0335","doi-asserted-by":"crossref","first-page":"2577","DOI":"10.1111\/jog.14818","article-title":"Application of artificial intelligence in gynecologic malignancies: a review","volume":"47","author":"Sone","year":"2021","journal-title":"J. Obstet. Gynaecol. Res."},{"issue":"5","key":"10.1016\/j.bbcan.2023.189026_bb0340","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1097\/OGX.0000000000000902","article-title":"Applying artificial intelligence to gynecologic oncology: a review","volume":"76","author":"Mysona","year":"2021","journal-title":"Obstet. Gynecol. Surv."},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0345","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.ygyno.2022.07.024","article-title":"A systematic review on the use of artificial intelligence in gynecologic imaging - background, state of the art, and future directions","volume":"166","author":"Shrestha","year":"2022","journal-title":"Gynecol. Oncol."},{"issue":"22","key":"10.1016\/j.bbcan.2023.189026_bb0350","doi-asserted-by":"crossref","first-page":"5670","DOI":"10.3390\/cancers13225670","article-title":"Machine learning for recurrence prediction of gynecologic cancers using lynch syndrome-related screening markers","volume":"13","author":"Kim","year":"2021","journal-title":"Cancers (Basel)."},{"key":"10.1016\/j.bbcan.2023.189026_bb0355","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102536","article-title":"Gynecological cancer prognosis using machine learning techniques: a systematic review of the last three decades (1990\u20132022)","volume":"139","author":"Sheehy","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bbcan.2023.189026_bb0360","doi-asserted-by":"crossref","first-page":"12823","DOI":"10.2147\/CMAR.S279990","article-title":"Progress of artificial intelligence in gynecological malignant tumors","volume":"2020","author":"Zhou","year":"2020","journal-title":"Cancer Manag. Res."},{"key":"10.1016\/j.bbcan.2023.189026_bb0365","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.breast.2022.08.010","article-title":"Current and future burden of breast cancer: global statistics for 2020 and 2040","volume":"66","author":"Arnold","year":"2022","journal-title":"Breast."},{"issue":"10","key":"10.1016\/j.bbcan.2023.189026_bb0370","article-title":"Artificial intelligence in breast cancer screening and diagnosis","volume":"14","author":"Dileep","year":"2022","journal-title":"Cureus."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0375","doi-asserted-by":"crossref","first-page":"13","DOI":"10.3390\/designs2020013","article-title":"Machine learning with applications in breast cancer diagnosis and prognosis","volume":"2","author":"Yue","year":"2018","journal-title":"Designs."},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0380","doi-asserted-by":"crossref","first-page":"297","DOI":"10.31661\/jbpe.v0i0.2109-1403","article-title":"Prediction of breast cancer using machine learning approaches","volume":"12","author":"Rabiei","year":"2022","journal-title":"J Biomed Phys Eng."},{"key":"10.1016\/j.bbcan.2023.189026_bb0385","first-page":"219","article-title":"Artificial intelligence methods for the diagnosis of breast cancer by image processing: a review","volume":"10","author":"Sadoughi","year":"2018","journal-title":"Breast Cancer (Dove Med Press)."},{"issue":"4","key":"10.1016\/j.bbcan.2023.189026_bb0390","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1118\/1.597177","article-title":"Computerized detection of clustered micro-calcifications in digital mammograms using a shift-invariant artificial neural network","volume":"21","author":"Zhang","year":"1994","journal-title":"Med. Phys."},{"key":"10.1016\/j.bbcan.2023.189026_bb0395","doi-asserted-by":"crossref","first-page":"S123","DOI":"10.1259\/bjr\/37622515","article-title":"Computer aided detection of masses in mammograms as decision support","volume":"79","author":"Karssemeijer","year":"2006","journal-title":"Br. J. Radiol."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0400","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1118\/1.2163390","article-title":"Computerized mass detection for digital breast tomosynthesis directly from the projection images","volume":"33","author":"Reiser","year":"2006","journal-title":"Med. Phys."},{"key":"10.1016\/j.bbcan.2023.189026_bb0405","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1186\/s43055-021-00421-4","article-title":"Comparing the diagnostic efficacy of digital breast tomosynthesis with full-field digital mammography using BI-RADS scoring","volume":"52","author":"Naeim","year":"2021","journal-title":"Egypt. J. Radiol. Nucl. Med."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0410","doi-asserted-by":"crossref","DOI":"10.1118\/1.4837196","article-title":"Computerized detection of breast cancer on automated breast ultrasound imaging of women with dense breasts","volume":"41","author":"Drukker","year":"2013","journal-title":"Med. Phys."},{"issue":"04","key":"10.1016\/j.bbcan.2023.189026_bb0415","first-page":"1","article-title":"Deep learning in breast cancer risk assessment: evaluation of convolutional neural networks on a clinical dataset of full-field digital mammograms. Journal of medical","volume":"4","author":"Li","year":"2017","journal-title":"Imaging."},{"issue":"5","key":"10.1016\/j.bbcan.2023.189026_bb0420","doi-asserted-by":"crossref","first-page":"591","DOI":"10.1007\/s10278-012-9452-z","article-title":"Computerized analysis of mammographic parenchymal patterns on a large clinical dataset of full-field digital mammograms: robustness study with two high-risk datasets","volume":"25","author":"Li","year":"2012","journal-title":"J. Digit. Imaging"},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0425","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1016\/j.acra.2008.08.014","article-title":"Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation","volume":"16","author":"Kontos","year":"2009","journal-title":"Acad. Radiol."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0430","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1097\/00004424-196601000-00032","article-title":"Computer-aided diagnosis in radiology","volume":"1","author":"Lodwick","year":"1966","journal-title":"Investig. Radiol."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0435","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1148\/radiol.2252010845","article-title":"Computerized analysis of digitized mammograms of BRCA1 and BRCA2 gene mutation carriers","volume":"225","author":"Huo","year":"2002","journal-title":"Radiology."},{"key":"10.1016\/j.bbcan.2023.189026_bb0440","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/j.media.2016.07.007","article-title":"Large scale deep learning for computer aided detection of mammographic lesions","volume":"35","author":"Kooi","year":"2017","journal-title":"Med. Image Anal."},{"issue":"5","key":"10.1016\/j.bbcan.2023.189026_bb0445","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1109\/42.538937","article-title":"Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images","volume":"15","author":"Sahiner","year":"1996","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0450","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1002\/(SICI)1097-0142(19970701)80:1<66::AID-CNCR9>3.0.CO;2-D","article-title":"Automated analysis of mammographic densities and breast carcinoma risk","volume":"80","author":"Byng","year":"1997","journal-title":"Cancer."},{"issue":"5","key":"10.1016\/j.bbcan.2023.189026_bb0455","doi-asserted-by":"crossref","first-page":"1076","DOI":"10.1118\/1.1695652","article-title":"Computerized interpretation of breast MRI: investigation of enhancement-variance dynamics","volume":"31","author":"Chen","year":"2004","journal-title":"Med. Phys."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0460","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."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0465","doi-asserted-by":"crossref","first-page":"33860","DOI":"10.1038\/srep33860","article-title":"Defining a radiomic response phenotype: a pilot study using targeted therapy in NSCLC","volume":"6","author":"Aerts","year":"2016","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0470","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1146\/annurev-bioeng-071812-152416","article-title":"Breast image analysis for risk assessment, detection, diagnosis, and treatment of cancer","volume":"15","author":"Giger","year":"2013","journal-title":"Annu. Rev. Biomed. Eng."},{"key":"10.1016\/j.bbcan.2023.189026_bb0475","doi-asserted-by":"crossref","first-page":"9","DOI":"10.20892\/j.issn.2095-3941.2016.0084","article-title":"Epidemiology of ovarian cancer: a review","volume":"14","author":"Reid","year":"2017","journal-title":"Cancer Biol. Med."},{"key":"10.1016\/j.bbcan.2023.189026_bb0480","doi-asserted-by":"crossref","first-page":"284","DOI":"10.3322\/caac.21456","article-title":"DeSantis CE-miller KD, Samimi G, Runowicz CD, Gaudet MM, Jemal a, Siegel RL, ovarian cancer statistics, 2018","volume":"68","author":"Torre","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"10.1016\/j.bbcan.2023.189026_bb0485","doi-asserted-by":"crossref","first-page":"280","DOI":"10.3322\/caac.21559","article-title":"Epithelial ovarian cancer: evolution of management in the era of precision medicine, CA","volume":"69","author":"Lheureux","year":"2019","journal-title":"Cancer J. Clin."},{"issue":"Suppl","key":"10.1016\/j.bbcan.2023.189026_bb0490","first-page":"1","article-title":"Morphological and molecular heterogeneity of epithelial ovarian cancer: Therapeutic implications","volume":"15","author":"Romero","year":"2020","journal-title":"EJC"},{"key":"10.1016\/j.bbcan.2023.189026_bb0495","doi-asserted-by":"crossref","first-page":"258","DOI":"10.1016\/j.ygyno.2012.12.016","article-title":"Global ovarian cancer health disparities","volume":"129","author":"Chornokur","year":"2013","journal-title":"Gynecol. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0500","first-page":"435","article-title":"Histopathological evaluation of ovarian tumors","volume":"2","author":"Singh","year":"2016","journal-title":"Imp. J. Interdiscip. Res."},{"key":"10.1016\/j.bbcan.2023.189026_bb0505","first-page":"15","article-title":"Ovarian cancer screening and early detection in the general population","volume":"4","author":"Rauh-Hain","year":"2011","journal-title":"Rev. Obstet. Gynecol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0510","doi-asserted-by":"crossref","first-page":"2143","DOI":"10.1007\/s00404-022-06578-1","article-title":"A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients","volume":"306","author":"Arezzo","year":"2022","journal-title":"Arch. Gynecol. Obstet."},{"key":"10.1016\/j.bbcan.2023.189026_bb0515","doi-asserted-by":"crossref","first-page":"3970","DOI":"10.1245\/s10434-015-4475-6","article-title":"Artificial intelligence systems as prognostic and predictive tools in ovarian cancer","volume":"22","author":"Enshaei","year":"2015","journal-title":"Ann. Surg. Oncol."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0520","first-page":"170","article-title":"Prognostic analysis of ovarian cancer patients using the cox regression model","volume":"28","author":"Zheng","year":"2009","journal-title":"Ai Zheng"},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0525","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1186\/s13244-023-01412-x","article-title":"Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors","volume":"14","author":"Jan","year":"2023","journal-title":"Insights Imaging"},{"key":"10.1016\/j.bbcan.2023.189026_bb0530","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1038\/s41467-019-08718-9","article-title":"A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic-and molecular-phenotypes of epithelial ovarian cancer","volume":"10","author":"Lu","year":"2019","journal-title":"Nat. Commun."},{"key":"10.1016\/j.bbcan.2023.189026_bb0535","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1007\/s10916-019-1211-y","article-title":"Classification and recognition of ovarian cells based on two-dimensional light scattering technology","volume":"43","author":"Chen","year":"2019","journal-title":"J. Med. Syst."},{"key":"10.1016\/j.bbcan.2023.189026_bb0540","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.radonc.2018.10.019","article-title":"Deep learning provides a new computed tomography-based prognostic biomarker for recurrence prediction in high-grade serous ovarian cancer","volume":"132","author":"Wang","year":"2019","journal-title":"Radiother. Oncol. J. Eur. Soc. Ther."},{"key":"10.1016\/j.bbcan.2023.189026_bb0545","doi-asserted-by":"crossref","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","article-title":"Deep learning techniques for medical image segmentation: achievements and challenges","volume":"32","author":"Hesamian","year":"2017","journal-title":"J. Digit. Imaging"},{"key":"10.1016\/j.bbcan.2023.189026_bb0550","doi-asserted-by":"crossref","first-page":"742","DOI":"10.1016\/j.ultrasmedbio.2015.11.014","article-title":"Ultrasound image discrimination between benign and malignant adnexal masses based on a neural network approach","volume":"42","author":"Aramend\u00eda","year":"2016","journal-title":"Ultrasound Med. Biol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0555","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0190783","article-title":"Automatic labeling of molecular biomarkers of immunohistochemistry images using fully convolutional networks","volume":"13","author":"Sheikhzadeh","year":"2018","journal-title":"PLoS One"},{"key":"10.1016\/j.bbcan.2023.189026_bb0560","doi-asserted-by":"crossref","first-page":"276","DOI":"10.21037\/atm.2017.05.13","article-title":"Benati M Danese E, circulating biomarkers in epithelial ovarian cancer diagnosis: from present to future perspective","volume":"5","author":"Montagnana","year":"2017","journal-title":"Ann. Transl. Med."},{"key":"10.1016\/j.bbcan.2023.189026_bb0565","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2019.e02826","article-title":"Multi-biomarker panel signature as the key to diagnosis of ovarian cancer","volume":"5","author":"Muinao","year":"2019","journal-title":"Heliyon."},{"key":"10.1016\/j.bbcan.2023.189026_bb0570","doi-asserted-by":"crossref","first-page":"467","DOI":"10.1016\/j.ygyno.2013.03.001","article-title":"Differentiating stage 1 epithelial ovarian cancer from benign ovarian tumors using a combination of tumor markers HE4, CA125, and CEA and patient\u2019s age","volume":"129","author":"Kondalsamy-Chennakesavan","year":"2013","journal-title":"Gynecol. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0575","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.ygyno.2018.08.025","article-title":"A multicenter clinical trial validating the performance of HE4, CA125, risk of ovarian malignancy algorithm and risk of malignancy index","volume":"151","author":"Lycke","year":"2018","journal-title":"Gynecol. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0580","doi-asserted-by":"crossref","first-page":"6127","DOI":"10.1007\/s13277-014-1811-6","article-title":"Diagnostic accuracy of serum HE4, CA125 and ROMA in patients with ovarian cancer: a meta-analysis","volume":"35","author":"Wang","year":"2014","journal-title":"Tumor Biol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0585","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.currproblcancer.2018.06.001","article-title":"Comparison of CA125, HE4, and ROMA index for ovarian cancer diagnosis","volume":"43","author":"Zhang","year":"2019","journal-title":"Curr. Probl. Cancer"},{"key":"10.1016\/j.bbcan.2023.189026_bb0590","first-page":"209","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: A Cancer J Clin."},{"issue":"9590","key":"10.1016\/j.bbcan.2023.189026_bb0595","doi-asserted-by":"crossref","first-page":"890","DOI":"10.1016\/S0140-6736(07)61416-0","article-title":"Human papillomavirus and cervical cancer","volume":"370","author":"Schiffman","year":"2007","journal-title":"Lancet."},{"issue":"5","key":"10.1016\/j.bbcan.2023.189026_bb0600","doi-asserted-by":"crossref","DOI":"10.3322\/caac.21628","article-title":"Cervical cancer screening for individuals at average risk: 2020 guideline update from the American Cancer Society","volume":"70","author":"Fontham","year":"2020","journal-title":"CA Cancer J. Clin."},{"issue":"5","key":"10.1016\/j.bbcan.2023.189026_bb0605","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1002\/cncy.21521","article-title":"The pap test and Bethesda 2014","volume":"123","author":"Nayar","year":"2015","journal-title":"Cancer Cytopathol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0610","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101897","article-title":"A shape context fully convolutional neural network for segmentation and classification of cervical nuclei in pap smear images","volume":"107","author":"Hussain","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.bbcan.2023.189026_bb0615","first-page":"105810X","article-title":"CNN based segmentation of nuclei in PAP-smear images with selective pre-processing","volume":"10581","author":"Gautam","year":"2018","journal-title":"Digital Pathol"},{"key":"10.1016\/j.bbcan.2023.189026_bb0620","doi-asserted-by":"crossref","first-page":"1060451","DOI":"10.3389\/fmed.2023.1060451","article-title":"Artificial intelligence in colposcopic examination: a promising tool to assist junior colposcopists","volume":"10","author":"Wu","year":"2023","journal-title":"Front. Med. (Lausanne)."},{"key":"10.1016\/j.bbcan.2023.189026_bb0625","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/j.ygyno.2020.07.099","article-title":"Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: a multicenter, clinical-based, observational study","volume":"159","author":"Bao","year":"2020","journal-title":"Gynecol. Oncol."},{"issue":"6","key":"10.1016\/j.bbcan.2023.189026_bb0630","first-page":"583","article-title":"Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images","volume":"11","author":"Miyagi","year":"2019","journal-title":"Mol Clin Oncol"},{"issue":"12","key":"10.1016\/j.bbcan.2023.189026_bb0635","doi-asserted-by":"crossref","DOI":"10.1128\/JCM.00997-19","article-title":"Machine learning interpretation of extended human papillomavirus genotyping by onclarity in an asian cervical cancer screening population","volume":"57","author":"Wong","year":"2019","journal-title":"J. Clin. Microbiol."},{"issue":"26","key":"10.1016\/j.bbcan.2023.189026_bb0640","doi-asserted-by":"crossref","first-page":"8438","DOI":"10.7150\/thno.37187","article-title":"Point-of-care cervical cancer screening using deep learning-based microholography","volume":"9","author":"Pathania","year":"2019","journal-title":"Theranostics."},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0645","first-page":"3518","article-title":"Application of deep learning to the classification of images from colposcopy","volume":"15","author":"Sato","year":"2018","journal-title":"Oncol. Lett."},{"key":"10.1016\/j.bbcan.2023.189026_bb0650","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105807","article-title":"Cervical cell classification with graph convolutional network","volume":"198","author":"Shi","year":"2021","journal-title":"Comput. Methods Prog. Biomed."},{"issue":"6","key":"10.1016\/j.bbcan.2023.189026_bb0655","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.1109\/JBHI.2017.2705583","article-title":"DeepPap: Deep convolutional networks for cervical cell classification","volume":"21","author":"Zhang","year":"2017","journal-title":"IEEE J BioMed Health Inform."},{"key":"10.1016\/j.bbcan.2023.189026_bb0660","doi-asserted-by":"crossref","first-page":"3194","DOI":"10.1002\/ijc.32514","article-title":"New perspectives on screening and early detection of endometrial cancer","volume":"145","author":"Costas","year":"2019","journal-title":"Int. J. Cancer"},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0665","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.ogc.2012.04.001","article-title":"Endometrial cancer","volume":"39","author":"Leslie","year":"2012","journal-title":"Obstet. Gynecol. Clin. N. Am."},{"issue":"4","key":"10.1016\/j.bbcan.2023.189026_bb0670","doi-asserted-by":"crossref","first-page":"258","DOI":"10.3322\/caac.21561","article-title":"Progress in endometrial cancer","volume":"69","author":"Rebecca","year":"2019","journal-title":"CA Cancer J. Clin."},{"key":"10.1016\/j.bbcan.2023.189026_bb0675","doi-asserted-by":"crossref","DOI":"10.3389\/fonc.2022.852746","article-title":"Machine learning for endometrial cancer prediction and prognostication","volume":"12","author":"Bhardwaj","year":"2022","journal-title":"Front. Oncol."},{"issue":"1","key":"10.1016\/j.bbcan.2023.189026_bb0680","article-title":"Artificial intelligence for prediction of endometrial intraepithelial neoplasia and endometrial cancer risks in pre- and postmenopausal women","volume":"3","author":"Erdemoglu","year":"2023","journal-title":"AJOG Glob Rep."},{"issue":"11","key":"10.1016\/j.bbcan.2023.189026_bb0685","doi-asserted-by":"crossref","first-page":"2623","DOI":"10.3390\/diagnostics12112623","article-title":"Deep learning-based classification of uterine cervical and endometrial Cancer subtypes from whole-slide histopathology images","volume":"12","author":"Song","year":"2022","journal-title":"Diagnostics."},{"key":"10.1016\/j.bbcan.2023.189026_bb0690","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1002\/dc.23649","article-title":"Image analysis and multi-layer perceptron artificial neural networks for the discrimination between benign and malignant endometrial lesions","volume":"45","author":"Makris","year":"2017","journal-title":"Diagn. Cytopathol."},{"issue":"16","key":"10.1016\/j.bbcan.2023.189026_bb0695","doi-asserted-by":"crossref","first-page":"5993","DOI":"10.3390\/ijerph17165993","article-title":"Using deep learning with convolutional neural network approach to identify the invasion depth of endometrial Cancer in myometrium using MR images: a pilot study","volume":"17","author":"Dong","year":"2020","journal-title":"Int. J. Environ. Res. Public Health"},{"key":"10.1016\/j.bbcan.2023.189026_bb0700","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.3389\/fonc.2019.01007","article-title":"Multi- planar MRI-based predictive model for preoperative assessment of lymph node metastasis in endometrial cancer","volume":"9","author":"Xu","year":"2019","journal-title":"Front. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0705","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.puhe.2018.07.012","article-title":"The utility of artificial neural networks and classification and regression trees for the prediction of endometrial cancer in postmenopausal women","volume":"164","author":"Pergialiotis","year":"2018","journal-title":"Public Health"},{"key":"10.1016\/j.bbcan.2023.189026_bb0710","doi-asserted-by":"crossref","first-page":"4985","DOI":"10.1007\/s00330-020-06870-1","article-title":"Deep learning for the determination of myometrial invasion depth and automatic lesion identification in endometrial cancer MR imaging: a preliminary study in a single institution","volume":"30","author":"Chen","year":"2020","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0715","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2020.18327","article-title":"Development and validation of a serum metabolomics signature for endometrial cancer screening in postmenopausal women","volume":"3","author":"Troisi","year":"2020","journal-title":"JAMA Netw. Open"},{"key":"10.1016\/j.bbcan.2023.189026_bb0720","doi-asserted-by":"crossref","first-page":"18093","DOI":"10.1038\/s41598-019-54624-x","article-title":"Prognostic classification of endometrial cancer using a molecular approach based on a twelve-gene NGS panel","volume":"9","author":"Lopez-Reig","year":"2019","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0725","doi-asserted-by":"crossref","first-page":"63","DOI":"10.14740\/wjon1191","article-title":"Epidemiology of prostate cancer","volume":"10","author":"Rawla","year":"2019","journal-title":"World J Oncol."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0730","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.3390\/curroncol30020178","article-title":"Underlying features of prostate cancer-statistics, risk factors, and emerging methods for its diagnosis","volume":"30","author":"Berenguer","year":"2023","journal-title":"Curr. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0735","doi-asserted-by":"crossref","DOI":"10.1177\/17562872221128791","article-title":"A review of artificial intelligence in prostate cancer detection on imaging","volume":"14","author":"Bhattacharya","year":"2022","journal-title":"Ther. Adv. Urol."},{"issue":"13","key":"10.1016\/j.bbcan.2023.189026_bb0740","first-page":"31","article-title":"A systematic review of artificial intelligence in prostate cancer","volume":"22","author":"Van-Booven","year":"2021","journal-title":"Res Rep Urol."},{"issue":"22","key":"10.1016\/j.bbcan.2023.189026_bb0745","doi-asserted-by":"crossref","first-page":"5595","DOI":"10.3390\/cancers14225595","article-title":"Artificial intelligence for clinical diagnosis and treatment of prostate cancer","volume":"14","author":"Rabaan","year":"2022","journal-title":"Cancers."},{"issue":"6","key":"10.1016\/j.bbcan.2023.189026_bb0750","doi-asserted-by":"crossref","first-page":"959","DOI":"10.3390\/diagnostics11060959","article-title":"Artificial intelligence-based algorithms for prostate cancer classification and detection on magnetic resonance imaging: a narrative review","volume":"11","author":"Twilt","year":"2021","journal-title":"Diagnostics."},{"issue":"5","key":"10.1016\/j.bbcan.2023.189026_bb0755","doi-asserted-by":"crossref","first-page":"136","DOI":"10.4329\/wjr.v15.i5.136","article-title":"Future of prostate imaging: artificial intelligence in assessing prostatic magnetic resonance imaging","volume":"15","author":"Chervenkov","year":"2023","journal-title":"World J. Radiol."},{"issue":"6","key":"10.1016\/j.bbcan.2023.189026_bb0760","doi-asserted-by":"crossref","first-page":"21178","DOI":"10.2214\/AJR.19.21178","article-title":"Prostate imaging reporting and data system version 2 for MRI of prostate cancer: can we do better?","volume":"212","author":"Sackett","year":"2019","journal-title":"Am. J. Roentgenol."},{"issue":"7","key":"10.1016\/j.bbcan.2023.189026_bb0765","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1038\/s41585-019-0193-3","article-title":"A new era: artificial intelligence and machine learning in prostate cancer","volume":"16","author":"Goldenberg","year":"2019","journal-title":"Nat. Rev. Urol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0770","doi-asserted-by":"crossref","first-page":"33804","DOI":"10.18632\/oncotarget.26100","article-title":"Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? A multi-center, multi-reader investigation","volume":"9","author":"Gaur","year":"2018","journal-title":"Oncotarget"},{"key":"10.1016\/j.bbcan.2023.189026_bb0775","doi-asserted-by":"crossref","first-page":"555","DOI":"10.1007\/s11547-018-0966-4","article-title":"Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer","volume":"124","author":"Abdollahi","year":"2019","journal-title":"Radiol. Med."},{"key":"10.1016\/j.bbcan.2023.189026_bb0780","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.3390\/s20051539","article-title":"Radiomics driven diffusion weighted imaging sensing strategies for zone-level prostate cancer sensing","volume":"20","author":"Dulhanty","year":"2020","journal-title":"Sensors"},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0785","doi-asserted-by":"crossref","first-page":"1243","DOI":"10.1007\/s00330-019-06417-z","article-title":"Semi-automatic classification of prostate cancer on multi-parametric mr imaging using a multi-channel 3D convolutional neural network","volume":"30","author":"Aldoj","year":"2020","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0790","doi-asserted-by":"crossref","first-page":"726","DOI":"10.1016\/j.ejrad.2016.01.011","article-title":"Assessment of PI-RADS v2 for the detection of prostate cancer","volume":"85","author":"Kasel-Seibert","year":"2016","journal-title":"Eur. J. Radiol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0795","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1038\/s41598-022-06730-6","article-title":"Deep learning for fully automatic detection, segmentation, and Gleason grade estimation of prostate cancer in multiparametric magnetic resonance images","volume":"12","author":"Pellicer-Valero","year":"2022","journal-title":"Sci. Rep."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0800","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1109\/TBME.2020.2993528","article-title":"Deep learning regression for prostate cancer detection and grading in bi-parametric MRI","volume":"68","author":"De-Vente","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"9","key":"10.1016\/j.bbcan.2023.189026_bb0805","doi-asserted-by":"crossref","first-page":"4077","DOI":"10.1002\/mp.14255","article-title":"A deep dive into understanding tumor foci classification using multiparametric MRI based on convolutional neural network","volume":"47","author":"Zong","year":"2020","journal-title":"Med. Phys."},{"key":"10.1016\/j.bbcan.2023.189026_bb0810","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.ebiom.2018.05.010","article-title":"RankProd combined with genetic algorithm optimized artificial neural network establishes a diagnostic and prognostic prediction model that revealed C1QTNF3 as a biomarker for prostate cancer","volume":"32","author":"Hou","year":"2018","journal-title":"EBioMedicine"},{"key":"10.1016\/j.bbcan.2023.189026_bb0815","doi-asserted-by":"crossref","first-page":"778","DOI":"10.3390\/genes10100778","article-title":"DNA methylation markers for pan-cancer prediction by deep learning","volume":"10","author":"Liu","year":"2019","journal-title":"Genes."},{"key":"10.1016\/j.bbcan.2023.189026_bb0820","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1186\/s12859-020-3345-9","article-title":"Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data","volume":"21","author":"Hamzeh","year":"2020","journal-title":"BMC Bioinform."},{"key":"10.1016\/j.bbcan.2023.189026_bb0825","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1200\/JCO.2020.38.6_suppl.279","article-title":"Artificial intelligence for streamlined immunofluorescence-based biomarker discovery in prostate cancer","volume":"38","author":"De-la Calle","year":"2020","journal-title":"JCO"},{"key":"10.1016\/j.bbcan.2023.189026_bb0830","doi-asserted-by":"crossref","first-page":"12823","DOI":"10.2147\/CMAR.S279990","article-title":"Progress of artificial intelligence in gynecological malignant tumors","volume":"12","author":"Zhou","year":"2020","journal-title":"Cancer Manag. Res."},{"issue":"2","key":"10.1016\/j.bbcan.2023.189026_bb0835","doi-asserted-by":"crossref","first-page":"127","DOI":"10.3322\/caac.21552","article-title":"Artificial intelligence in cancer imaging: clinical challenges and applications","volume":"69","author":"Bi","year":"2019","journal-title":"CA Cancer J. Clin."},{"issue":"12","key":"10.1016\/j.bbcan.2023.189026_bb0840","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.3390\/life12121991","article-title":"Artificial intelligence in cancer research: trends, challenges and future directions","volume":"12","author":"Sebastian","year":"2022","journal-title":"Life."},{"issue":"3","key":"10.1016\/j.bbcan.2023.189026_bb0845","doi-asserted-by":"crossref","first-page":"3432","DOI":"10.3390\/curroncol30030260","article-title":"Future of artificial intelligence applications in cancer care: a global cross-sectional survey of researchers","volume":"30","author":"Cabral","year":"2023","journal-title":"Curr. Oncol."},{"issue":"10","key":"10.1016\/j.bbcan.2023.189026_bb0850","doi-asserted-by":"crossref","first-page":"728","DOI":"10.1016\/j.crad.2021.03.009","article-title":"Challenges, and opportunities for artificial intelligence in oncological imaging","volume":"76","author":"Cheung","year":"2021","journal-title":"Clin. Radiol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0855","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.trecan.2021.02.002","article-title":"Quantifying cancer: more than just a numbers game","volume":"7","author":"Salgia","year":"2021","journal-title":"Trends Cancer"},{"key":"10.1016\/j.bbcan.2023.189026_bb0860","article-title":"Predicting survival duration with MRI radiomics of brain metastases from non-small cell lung cancer","volume":"11","author":"Chen","year":"2021","journal-title":"Front. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0865","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/s12038-022-00278-3","article-title":"Leveraging deep learning algorithms for synthetic data generation to design and analyze biological networks","volume":"47","author":"Achuthan","year":"2022","journal-title":"J. Biosci."},{"key":"10.1016\/j.bbcan.2023.189026_bb0870","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.radonc.2022.05.019","article-title":"Machine learning to refine prognostic and predictive nodal burden thresholds for post-operative radiotherapy in completely resected stage III-N2 non-small cell lung cancer","volume":"173","author":"Zarinshenas","year":"2022","journal-title":"Radiother. Oncol."},{"key":"10.1016\/j.bbcan.2023.189026_bb0875","doi-asserted-by":"crossref","first-page":"3853","DOI":"10.21037\/tcr-22-1626","article-title":"Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review","volume":"11","author":"Ladbury","year":"2022","journal-title":"Transl. Cancer Res."},{"key":"10.1016\/j.bbcan.2023.189026_bb0880","article-title":"Integration of artificial intelligence in lung cancer: rise of the machine","volume":"4","author":"Ladbury","year":"2023","journal-title":"Cell Rep Med"}],"container-title":["Biochimica et Biophysica Acta (BBA) - Reviews on Cancer"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0304419X23001750?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0304419X23001750?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T04:35:31Z","timestamp":1719894931000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0304419X23001750"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11]]},"references-count":176,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["S0304419X23001750"],"URL":"https:\/\/doi.org\/10.1016\/j.bbcan.2023.189026","relation":{},"ISSN":["0304-419X"],"issn-type":[{"value":"0304-419X","type":"print"}],"subject":[],"published":{"date-parts":[[2023,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers","name":"articletitle","label":"Article Title"},{"value":"Biochimica et Biophysica Acta (BBA) - Reviews on Cancer","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bbcan.2023.189026","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"189026"}}