Establishment of Machine Learning Models for Estimating the Collagen Content of Uterine Leiomyomas and Prediction of the Effect of Gonadotropin-Releasing Hormone Analog.

ABSTRACT:

Purpose

To establish machine learning models using magnetic resonance imaging (MRI) data for estimating the collagen content of uterine leiomyomas and predicting the drug therapy effects.

Methods

For surgical patients (90 uterine leiomyomas), tumor signal intensity was quantified by multiple MRI sequences before surgery, and collagen content was quantified by trichrome staining after surgery. These results were used to establish prediction models for estimating collagen content using support vector regression (SVR) and ridge regression (Ridge). For patients who received gonadotropin-releasing hormone analogs (41 uterine leiomyomas), MRI was performed before and after treatment. Correlation between the collagen content estimated by prediction models and tumor reduction rate b

SUBMITTER: Tamehisa T 

PROVIDER: S-EPMC12816974 | biostudies-literature | 2026 Jan-Dec

REPOSITORIES: biostudies-literature