BJUI compass · 2026
AI-enhanced micro-ultrasound improves detection of clinically significant prostate cancer at biopsy.
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Imran Muhammad, Brisbane Wayne G, Su Li-Ming, Joseph Jason P, Shao Wei
Аннотация
OBJECTIVE: This study aimed to evaluate the diagnostic accuracy of artificial intelligence (AI)-enhanced micro-ultrasound (micro-US) for detecting clinically significant prostate cancer (csPCa) in men referred for prostate biopsy.
PATIENTS AND METHODS: We retrospectively analysed 145 men undergoing micro-US-guided biopsy (79 with csPCa, 66 without). Deep features were extracted from 2D micro-US slices using a self-supervised convolutional autoencoder and classified with a random forest model under fivefold cross-validation. Patients were considered csPCa-positive if ≥8 consecutive slices were predicted positive. Diagnostic performance was assessed against biopsy pathology using receiver operating characteristic (ROC) analysis.
RESULTS: The AI-micro-US model achieved an area under the ROC curve (AUC) of 0.871. At a fixed threshold, sensitivity was 92.5% and specificity 68.1%, outperforming a clinical model based on prostate-specific antigen (PSA), digital rectal examination (DRE), age, and prostate volume (AUC 0.753; sensitivity 96.2%, specificity 27.3%).
CONCLUSION: AI-enhanced micro-US reduces false positives from conventional screening tools while preserving high sensitivity. It shows promise as a point-of-care alternative to MRI, integrating risk stratification and biopsy guidance into a single platform.
PATIENTS AND METHODS: We retrospectively analysed 145 men undergoing micro-US-guided biopsy (79 with csPCa, 66 without). Deep features were extracted from 2D micro-US slices using a self-supervised convolutional autoencoder and classified with a random forest model under fivefold cross-validation. Patients were considered csPCa-positive if ≥8 consecutive slices were predicted positive. Diagnostic performance was assessed against biopsy pathology using receiver operating characteristic (ROC) analysis.
RESULTS: The AI-micro-US model achieved an area under the ROC curve (AUC) of 0.871. At a fixed threshold, sensitivity was 92.5% and specificity 68.1%, outperforming a clinical model based on prostate-specific antigen (PSA), digital rectal examination (DRE), age, and prostate volume (AUC 0.753; sensitivity 96.2%, specificity 27.3%).
CONCLUSION: AI-enhanced micro-US reduces false positives from conventional screening tools while preserving high sensitivity. It shows promise as a point-of-care alternative to MRI, integrating risk stratification and biopsy guidance into a single platform.