Deep learning algorithm assisting diagnosis of prostate cancer extracapsular extension based on [

backgroundThis study aimed to develop and validate deep learning (DL) models based on multiparametric MRI (mpMRI) and [

methodsA total of 388 patients who underwent radical prostatectomy were enrolled from centers A, B and C. Three DL models based on mpMRI, PET/CT, and a combined MPC model were developed and compared with a manual model based on the ECE grading system. Additionally, three combined models (mpMRI-M, PET/CT-M, and MPC-M) were constructed by integrating the DL models with the Manual model. To enhance clinical applicability, an easy model (E-MPC-M) was developed. Model performance was evaluated using the area under the receiver-operating-characteristic curve (AUC) and metrics derived from the confusion matrix. Gradient-weighted class-activation-mapping (Grad-CAM) was employed to visualize model interpretability.

resultsIn the internal cohort, the Manual, MPC, and MPC-M models achieved AUCs of 0.752, 0.897, and 0.907, respectively; corresponding sensitivities were 0.616, 0.896, and 0.915, and specificities were 0.791, 0.740, and 0.802. In the external validation cohort, these models achieved AUCs of 0.665, 0.824, and 0.849; sensitivities of 0.318, 0.955, and 0.955; and specificities of 0.960, 0.600, and 0.640, respectively. The E-MPC-M model also showed robust performance, with an AUC of 0.862 in the internal cohort and 0.775 in the external cohort. Grad-CAM visualizations highlighted the model's focus on tumor-relevant regions, confirming effective learning of tumor features.

conclusionsThe MPC-M model demonstrated strong predictive performance for PCa ECE across internal and external cohorts, while the E-MPC-M model retained much of this performance with enhanced clinical practicality. However, these models should be considered as preliminary, and larger prospective multicenter studies are required to confirm their robustness and generalizability.