Keywords: Diagnosis/Prediction, Pelvis
Motivation: High-grade serous ovarian carcinoma (HGSOC) poses a significant challenge due to platinum resistance and the inherent difficulty in its prediction.
Goal(s): We aimed to explore MRI-based habitat model for predicting response of platinum-based chemotherapy in HGSOC patients, and compared with radiomics and deep learning models.
Approach: We leveraged the K-means algorithm for clustering on multiparameter MRI data. Then the radiomics, habitat, and deep learning models were constructed.
Results: Habitat model had the potential to predict platinum resistence, with a superior performance to radiomics and deep learning models. The nomogram integrating habitat with neoadjuvant chemotherapy yielded a better performance compared to others.
Impact: This study holds substantial clinical significance as it establishes a foundational framework for the customization of treatment strategies for patients afflicted with HGSOC.
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