{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,2]],"date-time":"2026-11-02T20:28:51Z","timestamp":1793651331686,"version":"4.5.1"},"reference-count":22,"publisher":"Elsevier BV","issue":"9","license":[{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,11,1]],"date-time":"2022-11-01T00:00:00Z","timestamp":1667260800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Gynecology Obstetrics and Human Reproduction"],"published-print":{"date-parts":[[2022,11]]},"DOI":"10.1016\/j.jogoh.2022.102466","type":"journal-article","created":{"date-parts":[[2022,8,27]],"date-time":"2022-08-27T23:05:28Z","timestamp":1661641528000},"page":"102466","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"title":["Prediction of endometrial cancer recurrence by using a novel machine learning algorithm: An Israeli gynecologic oncology group study"],"prefix":"10.1016","volume":"51","author":[{"given":"Ohad","family":"Houri","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yotam","family":"Gil","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ofer","family":"Gemer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Limor","family":"Helpman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zvi","family":"Vaknin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ofer","family":"Lavie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alon Ben","family":"Arie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amnon","family":"Amit","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tally","family":"Levy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmet","family":"Namazov","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Inbar Ben","family":"Shachar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilan","family":"Atlas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ilan","family":"Bruchim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ram","family":"Eitan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"5","key":"10.1016\/j.jogoh.2022.102466_bib0001","first-page":"E359","article-title":"Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012","volume":"136","author":"Jacques","year":"2015","journal-title":"Int. J. Canc."},{"key":"10.1016\/j.jogoh.2022.102466_bib0002","series-title":"SEER cancer statistics review, 1975-2013","author":"Howlader","year":"2016"},{"issue":"5","key":"10.1016\/j.jogoh.2022.102466_bib0003","doi-asserted-by":"crossref","first-page":"1132","DOI":"10.1158\/1055-9965.EPI-04-0871","article-title":"Endometrial cancer incidence trends in Europe: underlying determinants and prospects for prevention","volume":"14","author":"Bray","year":"2005","journal-title":"Cancer Epidemiol Biomarkers Prev."},{"issue":"3","key":"10.1016\/j.jogoh.2022.102466_bib0004","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.ygyno.2006.02.011","article-title":"Follow-up after primary therapy for endometrial cancer: a systematic review","volume":"101","author":"Fung","year":"2006","journal-title":"Gynecol Oncol."},{"key":"10.1016\/j.jogoh.2022.102466_bib0005","series-title":"Consensus Conference on Endometrial Cancer Diagnosis, Treatment and Follow-up","year":"2014"},{"key":"10.1016\/j.jogoh.2022.102466_bib0006","article-title":"Potential new biomarkers for endometrial cancer","author":"Michelle","year":"2019","journal-title":"Cancer Cell Int."},{"issue":"13","key":"10.1016\/j.jogoh.2022.102466_bib0007","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1056\/NEJMp1606181","article-title":"Predicting the Future \u2014 Big Data","volume":"375","author":"Ziad Obermeyer","year":"2016","journal-title":"Machine Learning, and Clinical Medicine N Engl J Med."},{"key":"10.1016\/j.jogoh.2022.102466_bib0008","series-title":"on behalf of the ESMO Guidelines Working Group Endometrial cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up Annals of Oncology","first-page":"vi33","author":"Colombo","year":"2013"},{"issue":"2","key":"10.1016\/j.jogoh.2022.102466_bib0009","doi-asserted-by":"crossref","first-page":"248","DOI":"10.6004\/jnccn.2014.0025","article-title":"Uterine neoplasms","volume":"12","author":"Koh","year":"2014","journal-title":"J. Natl. Compr. Canc. Netw."},{"key":"10.1016\/j.jogoh.2022.102466_bib0010","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learn"},{"key":"10.1016\/j.jogoh.2022.102466_bib0011","unstructured":"Tianqi Chen, Carlos Guestrin. XGBoost: a Scalable Tree Boosting System, KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining."},{"key":"10.1016\/j.jogoh.2022.102466_bib0012","doi-asserted-by":"crossref","first-page":"793","DOI":"10.1038\/bjc.2015.35","article-title":"Just how accurate are the major risk stratification systems for early-stage endometrial cancer?","volume":"112","author":"Bendifallah","year":"2015","journal-title":"Br J Cancer"},{"key":"10.1016\/j.jogoh.2022.102466_bib0013","doi-asserted-by":"crossref","first-page":"4239","DOI":"10.1245\/s10434-014-3864-6","article-title":"A Risk Scoring System to Determine Recurrence in Early-Stage Type 1 Endometrial Cancer: a French Multicentre","volume":"21","author":"Bendifallah","year":"2014","journal-title":"Study Annals of Surgical oncology"},{"key":"10.1016\/j.jogoh.2022.102466_bib0014","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1097\/GCO.0000000000000508","article-title":"Molecular characterization of endometrial cancer and therapeutic implications","author":"Chang","year":"2019","journal-title":"Curr. Opin. Obstet. Gynecol."},{"key":"10.1016\/j.jogoh.2022.102466_bib0015","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1097\/GCO.0000000000000602","article-title":"Molecular characterization of endometrial cancer and therapeutic implications","volume":"32","author":"Winterhoff","year":"2020","journal-title":"Curr. Opin. Obstet. Gynecol."},{"key":"10.1016\/j.jogoh.2022.102466_bib0016","doi-asserted-by":"crossref","first-page":"1351","DOI":"10.1001\/jama.2019.10306","article-title":"Making machine learning models clinically useful","volume":"322","author":"Shah","year":"2019","journal-title":"J Am Med Assoc"},{"key":"10.1016\/j.jogoh.2022.102466_bib0017","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1159\/000012061","article-title":"Artificial neural networks applied to survival prediction in breast cancer","volume":"57","author":"Lundin","year":"1999","journal-title":"Oncology"},{"key":"10.1016\/j.jogoh.2022.102466_bib0018","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijmedinf.2020.104195","article-title":"Using machine learning to predict ovarian cancer","volume":"141","author":"Lu","year":"2020","journal-title":"Int J Med Inform."},{"issue":"3","key":"10.1016\/j.jogoh.2022.102466_bib0019","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":"10.1016\/j.jogoh.2022.102466_bib0020","doi-asserted-by":"crossref","first-page":"7781","DOI":"10.7314\/APJCP.2014.15.18.7781","article-title":"Red cell distribution width as a predictor of prostate cancer progression","volume":"15","author":"Albayrak","year":"2014","journal-title":"Asian Pac J Cancer Prev."},{"key":"10.1016\/j.jogoh.2022.102466_bib0021","first-page":"217","article-title":"Chronic inflammation and cancer","volume":"16","author":"Shacter","year":"2002","journal-title":"Oncology"},{"issue":"4","key":"10.1016\/j.jogoh.2022.102466_bib0022","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1177\/1933719112459218","article-title":"The Clinical Relevance of Rising CA-125 Levels Within the Normal Range in Patients With Uterine Papillary Serous Cancer","author":"Frimer","year":"2013","journal-title":"Reprod Sci."}],"container-title":["Journal of Gynecology Obstetrics and Human Reproduction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468784722001507?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2468784722001507?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T10:01:35Z","timestamp":1762855295000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2468784722001507"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11]]},"references-count":22,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["S2468784722001507"],"URL":"https:\/\/doi.org\/10.1016\/j.jogoh.2022.102466","relation":{},"ISSN":["2468-7847"],"issn-type":[{"value":"2468-7847","type":"print"}],"subject":[],"published":{"date-parts":[[2022,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Prediction of endometrial cancer recurrence by using a novel machine learning algorithm: An Israeli gynecologic oncology group study","name":"articletitle","label":"Article Title"},{"value":"Journal of Gynecology Obstetrics and Human Reproduction","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jogoh.2022.102466","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2022 Published by Elsevier Masson SAS.","name":"copyright","label":"Copyright"}],"article-number":"102466"}}