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Abstract #2873

Multiparametric MRI Habitat Models Improve Non-Invasive Risk Stratification and Prognosis Prediction in Endometrial Carcinoma

Yunzhu Wu1 and Shenghong Ju1
1Institute for AI in Medicine, School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China

Synopsis

Keywords: Radiomics, Radiomics, Habitats, Multiparametric MRI, Endometrial Carcinoma, Tumor Heterogeneity

Motivation: Endometrial carcinoma (EC) risk stratification and prognosis is essential for treatment planning, yet conventional radiomics often lacks biological interpretability.

Goal(s): To assess if MRI-based habitat analysis can be a promising non-invasive tool for risk stratification and prognosis prediction in EC.

Approach: We developed structural and functional habitat models using T2WI, ADC, and CE-T1WI MRI sequences and used pathological examination results as the gold standard to evaluate habitat models' performance in predicting EC risk.

Results: Habitat models, particularly GMM- and K-means-based models, achieved comparable or improved predictive performance relative to the clinical model, furthermore offered non-invasive cellularity and vascularity information, enhancing EC risk assessment.

Impact: MRI-based habitat analysis and imaging provides a non-invasive method for predicting EC risk levels and prognosis at the celluar level, aiding treatment decision-making.

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