AI分析前列腺微超声可更准识别高危癌，减少误诊
Prostate Cancer Screening with Artificial Intelligence-Enhanced Micro-Ultrasound: A Comparative Study with Traditional Methods
- 用自监督网络提取超声图像特征，随机森林判断癌症风险
- AI模型灵敏度92.5%、特异度68.1%，优于传统方法的27.3%
- 适合希望降低过度活检风险的患者或资源有限地区使用
微超声（micro-US）在检测临床显著性前列腺癌（csPCa）方面诊断准确率接近MRI。本研究回顾性分析145名接受微超声引导活检的男性（其中79人确诊为csPCa，66人未患）。采用自监督卷积自动编码器从二维微超声切片中提取深层图像特征，通过五折交叉验证训练随机森林分类器，在切片层面预测csPCa。若连续88个以上切片预测为阳性，则判定患者为csPCa阳性。该模型性能与基于血清前列腺特异性抗原（PSA）、直肠指检（DRE）、前列腺体积和年龄的临床筛查模型进行比较。结果表明，AI-微超声模型与临床模型的受试者工作特征曲线下面积（AUROC）分别为0.871和0.753。在固定阈值下，微超声模型灵敏度达92.5%，特异度68.1%；而临床模型灵敏度96.2%，特异度仅27.3%。研究局限性包括单中心回顾性设计及缺乏外部验证。结论：经AI解读的微超声在保持高灵敏度的同时显著提升特异度，有助于减少不必要的活检，可作为低成本替代方案。患者摘要：我们开发了一种基于AI的前列腺微超声图像分析系统，其在识别侵袭性癌症方面优于传统血液和触诊筛查，有望避免不必要活检。
原文摘要 · Abstract (English)
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.
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