Frontiers in oncology · 2026
Confidence-based classification of clinically significant prostate cancer on biparametric MRI using Monte Carlo dropout and Grad-CAM visualization.
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Hao Cheng, Meng Yuze, Peng Yong, Du Xia
Аннотация
OBJECTIVE: Accurate early diagnosis of Clinically Significant prostate cancer (csPCa) is crucial for improving patient survival rates. This study aimed to develop and validate an interpretable, confidence-based deep learning model for csPCa classification using biparametric MRI (bpMRI).
METHODS: A retrospective analysis was conducted on bpMRI data from 410 patients. A classification model was constructed using the visual geometry group 16-layer (VGG-16) network, incorporating gradient-weighted class activation mapping (Grad-CAM) for visual interpretability, Monte Carlo Dropout (MC-Dropout) for uncertainty estimation, and a rejection mechanism. The model's performance was evaluated using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC).
RESULTS: Across all predictions, the confidence-based model achieved an overall accuracy of 79.3% before rejection. At a confidence threshold of 0.8, 80.8% of predictions were retained, among which the selective accuracy was 94.6%, with 99 false-positive and 2 false-negative predictions. Besides, the model's accuracy outperformed that of a senior radiologist (94.6% vs 90.8%). Qualitative Grad-CAM visualization demonstrated that the classifier frequently assigned high activation to MRI regions considered suspicious for csPCa; however, quantitative spatial agreement with lesion annotations was not assessed.
CONCLUSION: This confidence-based model identifies csPCa lesions and provides reliable confidence scores without pixel-level annotations. By rejecting uncertain predictions, it enhances the transparency and safety of clinical decision-making.
METHODS: A retrospective analysis was conducted on bpMRI data from 410 patients. A classification model was constructed using the visual geometry group 16-layer (VGG-16) network, incorporating gradient-weighted class activation mapping (Grad-CAM) for visual interpretability, Monte Carlo Dropout (MC-Dropout) for uncertainty estimation, and a rejection mechanism. The model's performance was evaluated using receiver operating characteristic (ROC) curves and area under the ROC curve (AUC).
RESULTS: Across all predictions, the confidence-based model achieved an overall accuracy of 79.3% before rejection. At a confidence threshold of 0.8, 80.8% of predictions were retained, among which the selective accuracy was 94.6%, with 99 false-positive and 2 false-negative predictions. Besides, the model's accuracy outperformed that of a senior radiologist (94.6% vs 90.8%). Qualitative Grad-CAM visualization demonstrated that the classifier frequently assigned high activation to MRI regions considered suspicious for csPCa; however, quantitative spatial agreement with lesion annotations was not assessed.
CONCLUSION: This confidence-based model identifies csPCa lesions and provides reliable confidence scores without pixel-level annotations. By rejecting uncertain predictions, it enhances the transparency and safety of clinical decision-making.