The application of risk models based on machine learning to predict endometriosis-associated ovarian cancer in patients with endometriosis.

ABSTRACT:

Introduction

There is currently no satisfactory model for predicting malignant transformation of endometriosis. The aim of this study was to construct and evaluate a risk model incorporating noninvasive clinical parameters to predict endometriosis-associated ovarian cancer (EAOC) in patients with endometriosis.

Material and methods

We enrolled 6809 patients with endometriosis confirmed by pathology, and randomly allocated them to training (n = 4766) and testing cohorts (n = 2043). The proportion of patients with EAOC in each cohort was similar. We extracted a total of 94 demographic and clinicopathologic features from the medical records using natural language processing. We used a machine learning method - gradient-boosting decision tree - to construct a predictive model fo

SUBMITTER: Chao X 

PROVIDER: S-EPMC9812095 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature