Multi-regional Multiparametric Deep Learning Radiomics for Diagnosis of Clinically Significant Prostate Cancer.

Abstract

Non-invasive and precise identification of clinically significant prostate cancer (csPCa) is essential for the management of prostatic diseases. Our study introduces a novel and interpretable diagnostic method for csPCa, leveraging multi-regional, multiparametric deep learning radiomics based on magnetic resonance imaging (MRI). The prostate regions, including the peripheral zone (PZ) and transition zone (TZ), are automatically segmented using a deep learning framework that combines convolutional neural networks and transformers to generate region-specific masks. Radiomics features are then extracted and selected from multiparametric MRI at the PZ, TZ, and their combined area to develop a multi-regional multiparametric radiomics diagnostic model. Feature contributions are quantified to enhance the model's interpretability and assess the importance of different imaging parameters across various regions. The multi-regional model substantially outperforms single-region models, achieving an optimal area under the curve (AUC) of 0.903 on the internal test set, and an AUC of 0.881 on the external test set. Comparison with other methods demonstrates that our proposed approach exhibits superior performance. Features from diffusion-weighted imaging and apparent diffusion coefficient play a crucial role in csPCa diagnosis, with contribution degrees of 53.28% and 39.52%, respectively. We introduce an interpretable, multi-regional, multiparametric diagnostic model for csPCa using deep learning radiomics. By integrating features from various zones, our model improves diagnostic accuracy and provides clear insights into the key imaging parameters, offering strong potential for clinical applications in csPCa management.