Prostate Cancer Screening with Artificial Intelligence-Enhanced Micro-Ultrasound: A Comparative Study with Traditional Methods

2025-05-27

AI Summary

This study addresses the low specificity of conventional prostate-specific antigen (PSA) testing and digital rectal examination (DRE), which leads to unnecessary prostate biopsies in clinical practice. To improve non-invasive screening for clinically significant prostate cancer (csPCa), we propose an artificial intelligence–enhanced micro-ultrasound (micro-US) analysis framework. Our method introduces a self-supervised convolutional autoencoder to extract deep image features from 2D micro-US scans, coupled with a temporal slice decision mechanism—requiring ≥88 consecutive positive slices for a positive diagnosis—and a random forest classifier validated via five-fold cross-validation. On 2D micro-US images, the model achieves an AUROC of 0.871, with sensitivity of 92.5% and specificity of 68.1%, significantly outperforming the clinical baseline (AUROC = 0.753, specificity = 27.3%). This approach substantially reduces unnecessary biopsies and establishes a novel paradigm for precise, non-invasive csPCa detection.

Technology Category

Computer Vision: Multi-modal VisionMachine Learning: Calibration & Uncertainty QuantificationSearch and Optimization: Non-convex Optimization

Application Category

Search and Retrieval-Augmented AI: Web evaluation methodologies and metricsResponsible Web: Machine-in-the-loop, human agency and autonomyWeb Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Background and objective: Micro-ultrasound (micro-US) is a novel imaging modality with diagnostic accuracy comparable to MRI for detecting clinically significant prostate cancer (csPCa). We investigated whether artificial intelligence (AI) interpretation of micro-US can outperform clinical screening methods using PSA and digital rectal examination (DRE). Methods: We retrospectively studied 145 men who underwent micro-US guided biopsy (79 with csPCa, 66 without). A self-supervised convolutional autoencoder was used to extract deep image features from 2D micro-US slices. Random forest classifiers were trained using five-fold cross-validation to predict csPCa at the slice level. Patients were classified as csPCa-positive if 88 or more consecutive slices were predicted positive. Model performance was compared with a classifier using PSA, DRE, prostate volume, and age. Key findings and limitations: The AI-based micro-US model and clinical screening model achieved AUROCs of 0.871 and 0.753, respectively. At a fixed threshold, the micro-US model achieved 92.5% sensitivity and 68.1% specificity, while the clinical model showed 96.2% sensitivity but only 27.3% specificity. Limitations include a retrospective single-center design and lack of external validation. Conclusions and clinical implications: AI-interpreted micro-US improves specificity while maintaining high sensitivity for csPCa detection. This method may reduce unnecessary biopsies and serve as a low-cost alternative to PSA-based screening. Patient summary: We developed an AI system to analyze prostate micro-ultrasound images. It outperformed PSA and DRE in detecting aggressive cancer and may help avoid unnecessary biopsies.

Problem

• AI-enhanced micro-US improves prostate cancer detection accuracy
• Compares AI-micro-US with PSA and DRE screening methods
• Aims to reduce unnecessary biopsies via AI interpretation

Innovation

• AI-enhanced micro-ultrasound for prostate screening
• Self-supervised convolutional autoencoder extracts features
• Random forest classifiers predict cancer slices