{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T10:21:24Z","timestamp":1775816484941,"version":"3.50.1"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T00:00:00Z","timestamp":1733443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:sec><jats:title>Objectives<\/jats:title><jats:p>Endometrial carcinosarcoma is a rare, aggressive high-grade endometrial cancer, accounting for about 5% of all uterine cancers and 15% of deaths from uterine cancers. The treatment can be complex, and the prognosis is poor. Its increasing incidence underscores the urgent requirement for personalized approaches in managing such challenging diseases.<\/jats:p><\/jats:sec><jats:sec><jats:title>Method<\/jats:title><jats:p>In this work, we designed an explainable machine learning approach to predict recurrence-free survival in patients affected by endometrial carcinosarcoma. For this purpose, we exploited the predictive power of clinical and histopathological data, as well as chemotherapy and surgical information collected for a cohort of 80 patients monitored over time. Among these patients, 32.5% have experienced the appearance of a recurrence.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The designed model was able to well describe the observed sequence of events, providing a reliable ranking of the survival times based on the individual risk scores, and achieving a C-index equals to 70.00% (95% CI, 59.38\u201384.74).<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Accordingly, machine learning methods could support clinicians in discriminating between endometrial carcinosarcoma patients at low-risk or high-risk of recurrence, in a non-invasive and inexpensive way. To the best of our knowledge, this is the first study proposing a preliminary approach addressing this task.<\/jats:p><\/jats:sec>","DOI":"10.3389\/frai.2024.1388188","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T06:52:26Z","timestamp":1733467946000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients"],"prefix":"10.3389","volume":"7","author":[{"given":"Samantha","family":"Bove","sequence":"first","affiliation":[]},{"given":"Francesca","family":"Arezzo","sequence":"additional","affiliation":[]},{"given":"Gennaro","family":"Cormio","sequence":"additional","affiliation":[]},{"given":"Erica","family":"Silvestris","sequence":"additional","affiliation":[]},{"given":"Alessia","family":"Cafforio","sequence":"additional","affiliation":[]},{"given":"Maria Colomba","family":"Comes","sequence":"additional","affiliation":[]},{"given":"Annarita","family":"Fanizzi","sequence":"additional","affiliation":[]},{"given":"Giuseppe","family":"Accogli","sequence":"additional","affiliation":[]},{"given":"Gerardo","family":"Cazzato","sequence":"additional","affiliation":[]},{"given":"Giorgio","family":"De Nunzio","sequence":"additional","affiliation":[]},{"given":"Brigida","family":"Maiorano","sequence":"additional","affiliation":[]},{"given":"Emanuele","family":"Naglieri","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Lupo","sequence":"additional","affiliation":[]},{"given":"Elsa","family":"Vitale","sequence":"additional","affiliation":[]},{"given":"Vera","family":"Loizzi","sequence":"additional","affiliation":[]},{"given":"Raffaella","family":"Massafra","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"888","DOI":"10.6004\/jnccn.2021.0038","article-title":"NCCN guidelines\u00ae insights: uterine neoplasms, version 3.2021","volume":"19","author":"Abu-Rustum","year":"2021","journal-title":"J. Natl. Compr. Cancer Netw."},{"key":"ref2","doi-asserted-by":"publisher","first-page":"102164","DOI":"10.1016\/j.artmed.2021.102164","article-title":"Artificial intelligence in gynecologic cancers: current status and future challenges \u2013 a systematic review","volume":"120","author":"Akazawa","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref3","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1016\/S0140-6736(05)67063-8","article-title":"Endometrial cancer","volume":"366","author":"Amant","year":"2005","journal-title":"Lancet"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"2986","DOI":"10.1109\/TC.2016.2519914","article-title":"Building an intrusion detection system using a filter-based feature selection algorithm","volume":"65","author":"Ambusaidi","year":"2016","journal-title":"IEEE Trans. Comput."},{"key":"ref5","doi-asserted-by":"publisher","first-page":"4633","DOI":"10.2147\/CMAR.S309551","article-title":"Carcinosarcomas of the uterus: prognostic factors and impact of adjuvant treatment","volume":"13","author":"Beckmann","year":"2021","journal-title":"Cancer Manag. Res."},{"key":"ref6","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1136\/ijgc-2022-004073","article-title":"Endometrial carcinosarcoma","volume":"33","author":"Bogani","year":"2023","journal-title":"Int. J. Gynecol. Cancer"},{"key":"ref7","doi-asserted-by":"crossref","DOI":"10.1201\/9781315139470","volume-title":"Classification and regression trees","author":"Breiman","year":"2017"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1136\/ijgc-2020-002230","article-title":"ESGO\/ESTRO\/ESP guidelines for the management of patients with endometrial carcinoma","volume":"31","author":"Concin","year":"2021","journal-title":"Int. J. Gynecol. Cancer"},{"key":"ref9","doi-asserted-by":"publisher","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":"ref10","volume-title":"Towards a rigorous science of interpretable machine learning","author":"Doshi-Velez","year":"2017"},{"key":"ref11","doi-asserted-by":"publisher","first-page":"FSO787","DOI":"10.2144\/fsoa-2021-0074","article-title":"An overview of artificial intelligence in oncology","volume":"8","author":"Farina","year":"2022","journal-title":"Future Sci. OA"},{"key":"ref12","doi-asserted-by":"publisher","first-page":"103808","DOI":"10.1016\/j.critrevonc.2022.103808","article-title":"Machine learning applications in gynecological cancer: a critical review","volume":"179","author":"Fiste","year":"2022","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref13","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1186\/s12885-021-08888-0","article-title":"Nomogram to predict overall survival based on the log odds of positive lymph nodes for patients with endometrial carcinosarcoma after surgery","volume":"21","author":"Gao","year":"2021","journal-title":"BMC Cancer"},{"key":"ref14","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1007\/s00404-023-07149-8","article-title":"The role of L1CAM as predictor of poor prognosis in stage I endometrial cancer: a systematic review and meta-analysis","volume":"309","author":"Giannini","year":"2024","journal-title":"Arch. Gynecol. Obstet."},{"key":"ref15","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1159\/000452277","article-title":"Malignant mixed M\u00fcllerian tumour of the uterus: analysis of 44 cases","volume":"92","author":"Grasso","year":"2017","journal-title":"Oncology"},{"key":"ref16","doi-asserted-by":"publisher","first-page":"aay7120","DOI":"10.1126\/scirobotics.aay7120","article-title":"XAI\u2014Explainable artificial intelligence","volume":"4","author":"Gunning","year":"2019","journal-title":"Sci. Robot."},{"key":"ref17","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1016\/j.mpdhp.2016.06.005","article-title":"Survival analysis","volume":"22","author":"Kartsonaki","year":"2016","journal-title":"Diagn. Histopathol."},{"key":"ref18","first-page":"97","volume-title":"The cox proportional hazards model and its characteristics.Survival analysis. Statistics for Biology and Health","author":"Kleinbaum","year":"2012"},{"key":"ref19","first-page":"106164","article-title":"Surv LIME: A method for explaining machine learning survival models","volume-title":"knowledge-based systems","author":"Kovalev","year":"2020"},{"key":"ref20","doi-asserted-by":"publisher","first-page":"103496","DOI":"10.1016\/j.jbi.2020.103496","article-title":"A practical perspective on the concordance index for the evaluation and selection of prognostic time-to-event models","volume":"108","author":"Longato","year":"2020","journal-title":"J. Biomed. Inform."},{"key":"ref21","doi-asserted-by":"publisher","first-page":"2053","DOI":"10.1056\/NEJMra1514010","article-title":"Endometrial Cancer","volume":"383","author":"Lu","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref22","doi-asserted-by":"publisher","first-page":"4629","DOI":"10.21873\/anticanres.15276","article-title":"A novel prediction model for Colon Cancer recurrence using auto-artificial intelligence","volume":"41","author":"Mazzaki","year":"2021","journal-title":"Anticancer Res."},{"key":"ref23","article-title":"Gradient boosting for survival analysis with applications in oncology","volume-title":"USF Tampa Graduate Theses and Dissertations","author":"Nguyen","year":"2019"},{"key":"ref24","doi-asserted-by":"publisher","first-page":"103369","DOI":"10.1016\/j.critrevonc.2021.103369","article-title":"Uterine carcinosarcoma: An overview","volume":"163","author":"Pezzicoli","year":"2021","journal-title":"Crit. Rev. Oncol. Hematol."},{"key":"ref25","first-page":"1","article-title":"Scikit-survival: a library for time-to-event analysis built on top of scikit-learn","volume":"21","author":"P\u00f6lsterl","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref26","doi-asserted-by":"publisher","first-page":"520","DOI":"10.1002\/ijgo.14033","article-title":"Uterine carcinosarcoma vs endometrial serous and clear cell carcinoma: a systematic review and meta-analysis of survival","volume":"158","author":"Raffone","year":"2022","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref27","doi-asserted-by":"publisher","first-page":"102536","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":"ref28","doi-asserted-by":"publisher","first-page":"7","DOI":"10.3322\/caac.21708","article-title":"Cancer statistics, 2022","volume":"72","author":"Siegel","year":"2022","journal-title":"CA Cancer J. Clin."},{"key":"ref29","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1016\/j.ygyno.2021.05.003","article-title":"Uterine carcinosarcomas: from pathology to practice","volume":"162","author":"Toboni","year":"2021","journal-title":"Gynecol. Oncol."},{"key":"ref30","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1002\/ijgo.13937","article-title":"Prognostic value of the TCGA molecular classification in uterine carcinosarcoma","volume":"158","author":"Travaglino","year":"2022","journal-title":"Int. J. Gynecol. Obstet."},{"key":"ref31","doi-asserted-by":"publisher","first-page":"34","DOI":"10.3390\/cancers14010034","article-title":"Prognostic role of the removed vaginal cuff and its correlation with L1CAM in low-risk endometrial adenocarcinoma","volume":"14","author":"Vizza","year":"2021","journal-title":"Cancers (Basel)"},{"key":"ref32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3214306","article-title":"Machine learning for survival analysis","volume":"51","author":"Wang","year":"2019","journal-title":"ACM Comput. Surv."}],"container-title":["Frontiers in Artificial Intelligence"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1388188\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T06:52:30Z","timestamp":1733467950000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/frai.2024.1388188\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,6]]},"references-count":32,"alternative-id":["10.3389\/frai.2024.1388188"],"URL":"https:\/\/doi.org\/10.3389\/frai.2024.1388188","relation":{},"ISSN":["2624-8212"],"issn-type":[{"value":"2624-8212","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,6]]},"article-number":"1388188"}}