Keywords: Diagnosis/Prediction, Diagnosis/Prediction
Motivation: Current machine learning methods for predicting prostate cancer (PCa) extraprostatic extension (EPE) do not leverage spatial information, which could enhance prediction accuracy.
Goal(s): To improve EPE prediction in PCa by integrating region-specific spatial information from mpMRI with clinical data, exploring how lesion location within the prostate affects predictive performance.
Approach: We combined mpMRI radiomics features with clinical data, applied feature selection to optimize performance, and trained a classification model to predict EPE status in subjects undergoing radical prostatectomy.
Results: Regional information can enhance EPE prediction, particularly for index csPCa lesions in the anterior or posterior prostate regions, which showed the highest performance metrics.
Impact: This study highlights the potential of incorporating spatial characteristics of csPCa to enhance EPE prediction through precision imaging, integrating region-specific mpMRI radiomics features with clinical and histopathological parameters to guide more precise treatment decisions and interventions.
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