[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124294-en":3,"doc-seo-124294-105":30,"detail-sidebar-cat-0-en-105":95},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124294,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients","Explainable machine learning is developed to predict recurrence-free survival in patients with endometrial carcinosarcoma, a rare and aggressive high-grade endometrial cancer with complex treatment and poor prognosis. Using clinical and histopathological features plus chemotherapy and surgical information from a cohort of 80 patients followed over time, the model supports event sequence description and survival-time ranking based on individual risk scores. Performance reaches a C-index of 70.00% (95% CI 59.38–84.74). The approach aims to help clinicians non-invasively and cost-effectively stratify low- vs high-risk patients for recurrence.","TYPE Original Research PUBLISHED 06 December 2024 DOI 10.3389/frai.2024.1388188  \nOPEN ACCESS  \nEDITED BY  \nTim Hulsen,  \nPhilips (Netherlands), Netherlands  \nREVIEWED BY  \nWendy Wang,  \nUniversity of North Alabama, United States Giacomo Corrado,  \nAgostino Gemelli University Polyclinic (IRCCS), Italy  \n*CORRESPONDENCE  \nMaria Colomba Comes  \n [m.c.comes@oncologico.bari.it](m.c.comes@oncologico.bari.it)[ ](m.c.comes@oncologico.bari.it)Annarita Fanizzi  \n [a.fanizzi@oncologico.bari.it](a.fanizzi@oncologico.bari.it)  \n†These authors have contributed equally to this work  \nRECEIVED 19 March 2024  \nACCEPTED 20 November 2024  \nPUBLISHED 06 December 2024  \nCITATION  \nBove S, Arezzo F, Cormio G, Silvestris E, Cafforio A, Comes MC, Fanizzi A, Accogli G, Cazzato G, De Nunzio G, Maiorano B, Naglieri E, Lupo A, Vitale E, Loizzi V and Massafra R (2024) Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients.  \nFront. Artif. Intell. 7:1388188 .  \ndoi: 10.3389/frai.2024.1388188  \nCOPYRIGHT  \n© 2024 Bove, Arezzo, Cormio, Silvestris, Cafforio, Comes, Fanizzi, Accogli, Cazzato, De Nunzio, Maiorano, Naglieri, Lupo, Vitale, Loizzi and Massafra. This is an open-access article distributed under the terms of the  \nCreative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nExplainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients  \nSamantha Bove 1†, Francesca Arezzo 2,3†, Gennaro Cormio 2,4, Erica Silvestris 2, Alessia Cafforio4, Maria Colomba Comes 1*, Annarita Fanizzi 1*, Giuseppe Accogli 1, Gerardo Cazzato 5, Giorgio De Nunzio 6,7, Brigida Maiorano 8, Emanuele Naglieri 2, Andrea Lupo 1, Elsa Vitale 9, Vera Loizzi 2,10† and  \nRaffaella Massafra 1†  \n1 Laboratorio di Biostatistica e Bioinformatica, Fisica Sanitaria, I. R.C.C.S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy, 2Ginecologia Oncologica, I. R.C.C. S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy,  \n3 Dipartimento di Medicina di Precisione e Rigenerativa e Area Jonica (DiMePRe-J), Università degli Studi di Bari “Aldo Moro”, Bari, Italy, 4 Dipartimento Interdisciplinare di Medicina (DIM), Università degli Studi di Bari “Aldo Moro”, Bari, Italy, 5 Dipartimento dell’Emergenza e dei Trapianti di Organi, Università degli Studi di Bari “Aldo Moro”, Bari, Italy, 6 Laboratory of Biomedical Physics and Environment, Department of Mathematics and Physics \"E. De Giorgi\", Università del Salento, Lecce, Italy, 7Advanced Data Analysis in Medicine (ADAM), Laboratory of Interdisciplinary Research Applied to Medicine (DReAM), Università del Salento, Lecce, Italy, 8Oncologica Medica, Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy, 9 Direzione Scientifica, I. R.C.C. S. Istituto Tumori “Giovanni Paolo II”, Bari, Italy, 10 Dipartimento di Biomedicina Traslazionale e Neuroscienze (DiBraiN), Università degli Studi di Bari\"Aldo Moro\", Bari, Italy  \nObjectives: 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.  \nMethod: 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","cbCaitMW9ykXf35Q","https://ap.wps.com/l/cbCaitMW9ykXf35Q","pdf",1016162,1,9,"English","en",105,"# Introduction\n## Background and clinical challenge\n# Objectives\n# Method\n## Data sources and cohort\n## Explainable machine learning approach\n# Results\n## Survival-time ranking and performance\n# Conclusion","[{\"question\":\"What is the main objective of the explainable machine learning approach described in the study?\",\"answer\":\"To predict recurrence-free survival in endometrial carcinosarcoma patients using an explainable machine learning framework.\"},{\"question\":\"What data are used to build the predictive model?\",\"answer\":\"The model uses clinical and histopathological data along with information about chemotherapy and surgery collected for a cohort of 80 monitored patients.\"},{\"question\":\"How well does the model perform according to the reported results?\",\"answer\":\"It ranks survival times using individual risk scores and achieves a C-index of 70.00% (95% CI 59.38–84.74).\"},{\"question\":\"How can the model support clinical decision-making?\",\"answer\":\"It can help discriminate patients at low risk versus high risk of recurrence in a non-invasive and relatively inexpensive manner.\"}]","Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients | 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