Machine learning methods to predict presence of residual cancer following hysterectomy.

ABSTRACT: Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence following surgery. Current machine learning models use advanced pathology data that is often inaccessible within low-resource settings and are specific to singular cancer types. There is currently a need for machine learning models to predict non-clinically evident residual disease using only clinically available health data. Here we developed and tested multiple machine learning models to assess the risk of residual disease post-hysterectomy based on clinical and operative parameters. Data from 3656 hysterectomy patients from the NSQIP dataset over 1

SUBMITTER: Ganguli R 

PROVIDER: S-EPMC8854708 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature