An assisted diagnostic and prognostic model for endometrial cancer using 36 serological markers and clinical variables from 562 patients.

ABSTRACT: Endometrial carcinoma (EC) has demonstrated a concerning epidemiological trajectory. Current evaluation systems for EC are limited to postoperative analysis, necessitating the development of a preoperative risk stratification model. Researchers aimed to create a machine learning-based approach using multiple serological markers and clinical variables to predict EC characteristics. A dataset of 508 EC patients and 54 patients with endometrial atypical hyperplasia (EAH) was utilized. Seven supervised machine learning classifiers were evaluated, with the Random Forest classifier being the most effective. The use of advanced computational models that integrate diverse biological markers and clinical factors has demonstrated superior predictive accuracy for various outcomes in endometrial cance

SUBMITTER: Chen F 

PROVIDER: S-EPMC12627715 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature