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.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.