AUTHOR=Bove Samantha , Arezzo Francesca , Cormio Gennaro , Silvestris Erica , Cafforio Alessia , Comes Maria Colomba , Fanizzi Annarita , Accogli Giuseppe , Cazzato Gerardo , De Nunzio Giorgio , Maiorano Brigida , Naglieri Emanuele , Lupo Andrea , Vitale Elsa , Loizzi Vera , Massafra Raffaella 

TITLE=Explainable machine learning for predicting recurrence-free survival in endometrial carcinosarcoma patients

JOURNAL=Frontiers in Artificial Intelligence

VOLUME=Volume 7 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/artificial-intelligence/articles/10.3389/frai.2024.1388188

DOI=10.3389/frai.2024.1388188

ISSN=2624-8212

ABSTRACT=ObjectivesEndometrial 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.MethodIn 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.ResultsThe 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, 59.38–84.74).ConclusionAccordingly, 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.