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

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 by GnRHa treatment was investigated.

RESULTS

SVR and ridge models were able to estimate the collagen content with high accuracy [R 2: 0.579 (95% CI: 0.33-0.66) and 0.570 (0.27-0.62) in cross validation and 0.648 (0.31-0.85) and 0.675 (0.34-0.86) in Internal Validation]. Significant negative correlations [R: -0.714 (-0.58 to -0.81) and -0.700 (-0.56 to -0.80)] were shown between the estimated collagen content and the tumor reduction rate.

CONCLUSIONS

Collagen content of uterine leiomyomas can be estimated by machine learning models using MRI data and can predict the effect of drug therapy on tumor reduction.