What type of study is this?

This is a Quantitative Study study.

September 17, 2025

Multisequence MRI‑based radiomics models for the assessment of microsatellite instability in endometrial cancer：A Feasibility Study

Key Points

The XGB model achieved the highest predictive performance for microsatellite instability in endometrial cancer.
Notable specificity and positive predictive value were observed in the diagnostic performance of the models assessed.
Multiple machine learning models, including DT, SVM, and RF, were evaluated for their ability to predict MSI status.
These findings suggest strong clinical utility in non-invasive assessments for high-risk endometrial cancer patients.

Abstract

Motivation: Accurately evaluating microsatellite instability (MSI) status in endometrial cancer is crucial for optimizing immunotherapy strategies and improving patient outcomes. Goal(s): This study aims to assess the feasibility of using multiple machine learning models to predict MSI status in endometrial cancer (EC) and to identify the model with the best diagnostic performance. Approach: We assessed the diagnostic performance of the DT, SVM, KNN, LR, RF, EBM, and XGB models by calculating their respective area under the curve (AUC) values. Results: The XGB model achieved the highest MSI predictive performance, with notable specificity and positive predictive value, offering strong clinical utility for identifying high-risk patients. Impact: This study demonstrated the value of various radiomics models for non-invasively assessing MSI status prediction, providing reliable support for diagnosing and treating endometrial cancer (EC) and enhancing patient prognosis.

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