{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T20:51:33Z","timestamp":1791665493167,"version":"4.3.4"},"reference-count":30,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T00:00:00Z","timestamp":1788566400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100013845","name":"Fondation contre le Cancer","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013845","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005026","name":"Foundation Against Cancer","doi-asserted-by":"publisher","award":["2023-003"],"award-info":[{"award-number":["2023-003"]}],"id":[{"id":"10.13039\/501100005026","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","esmorwd.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["ESMO Real World Data and Digital Oncology"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.esmorw.2026.100792","type":"journal-article","created":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T18:45:48Z","timestamp":1791225948000},"page":"100792","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Using machine learning to estimate time-to-progression in ovarian cancer based on cancer registry and administrative health data"],"prefix":"10.1016","volume":"14","author":[{"given":"A.","family":"Leloup","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"J.","family":"Verbeeck","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"H.","family":"De Schutter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M.","family":"Thibaut","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4876-4600","authenticated-orcid":false,"given":"J.F.","family":"Baurain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4704-4647","authenticated-orcid":false,"given":"S.","family":"Henry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Z.","family":"Hlavata","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7001-9394","authenticated-orcid":false,"given":"T.","family":"Laga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Marquette","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P.","family":"Neven","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"L.","family":"Polastro","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3984-2948","authenticated-orcid":false,"given":"A.","family":"Vanderstichele","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"T.","family":"Van Gorp","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4788-968X","authenticated-orcid":false,"given":"L.","family":"Van Eycken","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8243-697X","authenticated-orcid":false,"given":"F.","family":"Verdoodt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.esmorw.2026.100792_bib1","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 Cancer J Clin"},{"issue":"4","key":"10.1016\/j.esmorw.2026.100792_bib3","first-page":"1121","article-title":"Clinical endpoints in oncology\u2013a primer","volume":"11","author":"Delgado","year":"2021","journal-title":"Am J Cancer Res"},{"issue":"8","key":"10.1016\/j.esmorw.2026.100792_bib4","doi-asserted-by":"crossref","DOI":"10.1093\/jnci\/djv134","article-title":"Challenges and opportunities in measuring cancer recurrence in the United States","volume":"107","author":"Warren","year":"2015","journal-title":"J Natl Cancer Inst"},{"issue":"10","key":"10.1016\/j.esmorw.2026.100792_bib5","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1093\/jnci\/djaa050","article-title":"A systematic review of estimating breast cancer recurrence at the population level with administrative data","volume":"112","author":"Izci","year":"2020","journal-title":"J Natl Cancer Inst"},{"key":"10.1016\/j.esmorw.2026.100792_bib6","article-title":"Recurrence rate in early-stage epithelial ovarian cancer: is there a role for upfront maintenance with PARP inhibitors in stages I and II?","volume":"46","author":"Levine","year":"2023","journal-title":"Gynecol Oncol Rep"},{"issue":"3","key":"10.1016\/j.esmorw.2026.100792_bib7","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.ygyno.2006.06.013","article-title":"Randomized phase III trial of three versus six cycles of adjuvant carboplatin and paclitaxel in early stage epithelial ovarian carcinoma: a Gynecologic Oncology Group study","volume":"102","author":"Bell","year":"2006","journal-title":"Gynecol Oncol"},{"issue":"3","key":"10.1016\/j.esmorw.2026.100792_bib8","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.ygyno.2009.10.073","article-title":"The potential benefit of 6 vs. 3 cycles of chemotherapy in subsets of women with early-stage high-risk epithelial ovarian cancer: an exploratory analysis of a Gynecologic Oncology Group study","volume":"116","author":"Chan","year":"2010","journal-title":"Gynecol Oncol"},{"key":"10.1016\/j.esmorw.2026.100792_bib9","doi-asserted-by":"crossref","DOI":"10.1038\/nrdp.2016.61","article-title":"Ovarian cancer","volume":"2","author":"Matulonis","year":"2016","journal-title":"Nat Rev Dis Primers"},{"issue":"1","key":"10.1016\/j.esmorw.2026.100792_bib10","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s00280-017-3501-8","article-title":"Advances in ovarian cancer therapy","volume":"81","author":"Cortez","year":"2018","journal-title":"Cancer Chemother Pharmacol"},{"key":"10.1016\/j.esmorw.2026.100792_bib11","article-title":"Evolution of breast cancer recurrence risk prediction: a systematic review of statistical and machine learning-based models","volume":"7","author":"El Haji","year":"2023","journal-title":"JCO Clin Cancer Inform"},{"issue":"2","key":"10.1016\/j.esmorw.2026.100792_bib12","article-title":"Incorporating breast cancer recurrence events into population-based cancer registries using medical claims: cohort study","volume":"6","author":"A\u2019Mar","year":"2020","journal-title":"JMIR Cancer"},{"issue":"3","key":"10.1016\/j.esmorw.2026.100792_bib13","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1093\/jnci\/djx200","article-title":"Development, validation, and dissemination of a breast cancer recurrence detection and timing informatics algorithm","volume":"110","author":"Ritzwoller","year":"2018","journal-title":"J Natl Cancer Inst"},{"key":"10.1016\/j.esmorw.2026.100792_bib14","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.canep.2019.01.016","article-title":"A validated algorithm for register-based identification of patients with recurrence of breast cancer-Based on Danish Breast Cancer Group (DBCG) data","volume":"59","author":"Aagaard Rasmussen","year":"2019","journal-title":"Cancer Epidemiol"},{"issue":"12","key":"10.1016\/j.esmorw.2026.100792_bib15","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1093\/jnci\/djs233","article-title":"Administrative data algorithms to identify second breast cancer events following early-stage invasive breast cancer","volume":"104","author":"Chubak","year":"2012","journal-title":"J Natl Cancer Inst"},{"issue":"1","key":"10.1016\/j.esmorw.2026.100792_bib16","first-page":"1208","article-title":"A validation study of administrative claims data to measure ovarian cancer recurrence and secondary debulking surgery","volume":"4","author":"Livaudais-Toman","year":"2016","journal-title":"EGEMS (Wash DC)"},{"key":"10.1016\/j.esmorw.2026.100792_bib17","article-title":"Evaluation of machine learning methods for the retrospective detection of ovarian cancer recurrences from chemotherapy data","volume":"4","author":"Coles","year":"2024","journal-title":"ESMO Real World Data Digit Oncol"},{"key":"10.1016\/j.esmorw.2026.100792_bib18","article-title":"Development of an automatic rule-based algorithm for the detection of ovarian cancer recurrence from electronic health records","volume":"8","author":"Lee","year":"2024","journal-title":"JCO Clin Cancer Inform"},{"key":"10.1016\/j.esmorw.2026.100792_bib19","doi-asserted-by":"crossref","first-page":"1375","DOI":"10.2147\/CMAR.S482837","article-title":"Predicting the recurrence of ovarian cancer based on machine learning","volume":"16","author":"Zhou","year":"2024","journal-title":"Cancer Manag Res"},{"key":"10.1016\/j.esmorw.2026.100792_bib20","series-title":"International Classification of Diseases for Oncology (ICD-O-3)","year":"2013"},{"key":"10.1016\/j.esmorw.2026.100792_bib21","series-title":"International Classification of Diseases for Oncology","year":"2019"},{"key":"10.1016\/j.esmorw.2026.100792_bib22","series-title":"TNM Classification of Malignant Tumours, International Union Against Cancer (UICC)","author":"Sobin","year":"2009"},{"key":"10.1016\/j.esmorw.2026.100792_bib23","series-title":"TNM Classification of Malignant Tumours, International Union Against Cancer (UICC)","author":"Brierley","year":"2017"},{"key":"10.1016\/j.esmorw.2026.100792_bib24","series-title":"TNM Classification of Malignant Tumours, International Union Against Cancer (UICC)","author":"Brierley","year":"2025"},{"issue":"1","key":"10.1016\/j.esmorw.2026.100792_bib25","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1186\/s12864-019-6413-7","article-title":"The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation","volume":"21","author":"Chicco","year":"2020","journal-title":"BMC Genomics"},{"key":"10.1016\/j.esmorw.2026.100792_bib26","doi-asserted-by":"crossref","first-page":"303","DOI":"10.2340\/1651-226X.2024.35232","article-title":"TNM stage in the Nordic Cancer Registries 2004-2016: registration and availability","volume":"63","author":"Engholm","year":"2024","journal-title":"Acta Oncol"},{"issue":"10","key":"10.1016\/j.esmorw.2026.100792_bib27","doi-asserted-by":"crossref","first-page":"833","DOI":"10.1016\/j.annonc.2023.07.011","article-title":"Newly diagnosed and relapsed epithelial ovarian cancer: ESMO Clinical Practice Guideline for diagnosis, treatment and follow-up","volume":"34","author":"Gonz\u00e1lez-Mart\u00edn","year":"2023","journal-title":"Ann Oncol"},{"key":"10.1016\/j.esmorw.2026.100792_bib28","doi-asserted-by":"crossref","first-page":"241","DOI":"10.2147\/CLEP.S396140","article-title":"Identifying recurrences among non-metastatic colorectal cancer patients using National Health Data Registries: validation and optimization of a registry-based algorithm in a modern Danish cohort","volume":"15","author":"Nors","year":"2023","journal-title":"Clin Epidemiol"},{"key":"10.1016\/j.esmorw.2026.100792_bib29","doi-asserted-by":"crossref","first-page":"251","DOI":"10.2147\/CLEP.S396738","article-title":"A validated register-based algorithm to identify patients diagnosed with recurrence of surgically treated stage I lung cancer in Denmark","volume":"15","author":"Rasmussen","year":"2023","journal-title":"Clin Epidemiol"},{"key":"10.1016\/j.esmorw.2026.100792_bib30","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1200\/CCI.18.00113","article-title":"Performance of cancer recurrence algorithms after coding scheme switch from International Classification of Diseases 9th Revision to International Classification of Diseases 10th Revision","volume":"3","author":"Carroll","year":"2019","journal-title":"JCO Clin Cancer Inform"},{"issue":"3","key":"10.1016\/j.esmorw.2026.100792_bib31","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1007\/s10549-022-06812-z","article-title":"Estimating the incidence of breast cancer recurrence using administrative data","volume":"198","author":"Habbous","year":"2023","journal-title":"Breast Cancer Res Treat"}],"container-title":["ESMO Real World Data and Digital Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2949820126001116?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2949820126001116?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,10]],"date-time":"2026-10-10T20:25:23Z","timestamp":1791663923000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2949820126001116"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":30,"alternative-id":["S2949820126001116"],"URL":"https:\/\/doi.org\/10.1016\/j.esmorw.2026.100792","relation":{},"ISSN":["2949-8201"],"issn-type":[{"value":"2949-8201","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Using machine learning to estimate time-to-progression in ovarian cancer based on cancer registry and administrative health data","name":"articletitle","label":"Article Title"},{"value":"ESMO Real World Data and Digital Oncology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.esmorw.2026.100792","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd on behalf of European Society for Medical Oncology.","name":"copyright","label":"Copyright"}],"article-number":"100792"}}