# Development of a machine learning model for predicting the expression of proteins associated with targeted therapy in endometrial cancer

## TL;DR

This study develops a machine learning model to predict protein expression in endometrial cancer patients using MRI and clinical data, aiming to improve personalized treatment.

## Contribution

A novel machine learning model combining MRI radiomics and clinicopathological features to predict protein expression linked to targeted therapy in endometrial cancer.

## Key findings

- Combination models outperformed radiomic or clinicopathologic models alone in predicting PTEN, PIK3CA, and mTOR expression.
- Calibration and decision curve analyses confirmed the clinical utility and accuracy of the combination models.
- The model shows potential as a tool for personalized adjuvant therapy in endometrial cancer patients.

## Abstract

To develop a machine learning model integrates multi-parametric magnetic resonance imaging (MRI) radiomics features and clinicopathological features to predict the expression status of phosphatase and tension homolog (PTEN), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), and mammalian target of rapamycin (mTOR), which are frequently linked with targeted therapy for endometrial cancer (EC), in order to establish a dependable foundation for personalized adjuvant therapy for EC patients.

we retrospectively recruited 82 EC patients who underwent preoperative MRI and radical resection at two independent hospitals. 60 patients from Center 1 were utilized as the training set for constructing the machine learning model, while 22 patients from Center 2 served as an external validation set to assess the model’s performance. We evaluated the performance of models predicted three proteins’ expression using receiver operating characteristic (ROC) analysis, calibration curve analysis, and decision curve analysis (DCA).

To construct machine learning models for predicting the expression of PTEN, PIK3CA, and mTOR, we respectively screened 5 radiomic and 7 clinicopathologic features, 4 radiomic and 9 clinicopathologic features, and 2 radiomic and 10 clinicopathologic features. The area under the curve (AUC) values of the radscore, clinicopathology, and combination models predicting PTEN expression were 0.875, 0.703, and 0.891 in the training set, and 0.750, 0.844, and 0.833 in the validation set, respectively. The AUC values for the models predicted PIK3CA expression in the training set were 0.856, 0.633, and 0.880, respectively, in the validation set, they were 0.842, 0.667, and 0.825. The AUC of each model for mTOR were 0.896, 0.831, and 0.912 in the training set, and 0.729, 0.847, and 0.829 in the validation set. Calibration curve analysis and DCA showed that the combination models were both well calibrated and clinically useful.

Machine learning models integrating multi-parametric MRI radiomics and clinicopathological features can be a potential tool for predicting PTEN, PIK3CA, and mTOR expression status in EC patients.