Deep Learning and Clinical Data Fusion in Prostate Cancer: Diagnosis of Clinically Significant Lesions Using Multiparametric MRI

Motivation: Prostate cancer diagnosis requires accurate differentiation between clinically significant (csPCa) and non-significant (non-csPCa) lesions. Traditional methods, such as PSA and TRUS biopsy, lack specificity, often resulting in unnecessary biopsies. Goal(s): This study aims to enhance diagnostic accuracy and reduce interventions by developing an automated deep learning framework for lesion classification. Approach: Using the PROSTATEx dataset, an ensemble of DenseNet-121, ResNet-50, and ConvMixer integrates mpMRI and clinical data (lesion zone, PSAD). Results: The model outperformed individual networks and prior methods, showing high AUC, sensitivity, specificity, and accuracy, promising improved diagnostic efficiency and patient outcomes. Impact: This study's integration of AI, multiparametric MRI, and clinical data refines prostate cancer diagnostics, equipping clinicians with a robust tool for precise lesion classification. This approach fosters personalized patient management, reduces overtreatment, and encourages further advancements in AI-driven oncology.