Background: The increase in multiparametric magnetic resonance imaging (mpMRI) examinations as a fundamental tool in prostate cancer (PCa) diagnostics raises the need for supportive computer-aided imaging analysis. Therefore, we evaluated the performance of a commercially available AI-based algorithm for prostate cancer detection and classification in a multi-center setting. Methods: Representative patients with 3T mpMRI between 2017 and 2022 at three different university hospitals were selected. Exams were read according to the PI-RADSv2.1 protocol and then assessed by an AI algorithm. Diagnostic accuracy for PCa of both human and AI readings were calculated using MR-guided ultrasound fusion biopsy as the gold standard. Results: Analysis of 91 patients resulted in 138 target lesions. Median patient age was 67 years (range: 49–82), median PSA at the time of the MRI exam was 8.4 ng/mL (range: 1.47–73.7). Sensitivity and specificity for clinically significant prostate cancer (csPCa, defined as ISUP ≥ 2) were 92%/64% for radiologists vs. 91%/57% for AI detection on patient level and 90%/70% vs. 81%/78% on lesion level, respectively (cut-off PI-RADS ≥ 4). Two cases of csPCa were missed by the AI on patient-level, resulting in a negative predictive value (NPV) of 0.88 at a cut-off of PI-RADS ≥ 3. Conclusions: AI-augmented lesion detection and scoring proved to be a robust tool in a multi-center setting with sensitivity comparable to the radiologists, even outperforming human reader specificity on both patient and lesion levels at a threshold of PI-RADS ≥3 and a threshold of PI-RADS ≥ 4 on lesion level. In anticipation of refinements of the algorithm and upon further validation, AI-detection could be implemented in the clinical workflow prior to human reading to exclude PCa, thereby drastically improving reading efficiency.
背景：多参数磁共振成像（mpMRI）作为前列腺癌（PCa）诊断的基础工具，其检查量的增加催生了对计算机辅助影像分析支持的需求。为此，我们在多中心环境下评估了一款商用人工智能算法在前列腺癌检测与分类中的性能表现。方法：选取2017年至2022年间三家不同大学医院接受3T mpMRI检查的代表性患者。所有影像均按照PI-RADSv2.1标准进行人工判读，随后由AI算法进行评估。以MR引导超声融合活检作为金标准，分别计算人工判读与AI判读对前列腺癌的诊断准确率。结果：共纳入91例患者的138个靶向病灶进行分析。患者中位年龄67岁（范围：49-82岁），MRI检查时前列腺特异性抗原（PSA）中位值为8.4 ng/mL（范围：1.47-73.7）。针对临床显著性前列腺癌（csPCa，定义为ISUP分级≥2），在患者层面放射科医师的敏感度/特异度为92%/64%，AI检测为91%/57%；在病灶层面分别为90%/70%与81%/78%（PI-RADS截断值≥4）。AI在患者层面漏诊2例csPCa，当PI-RADS截断值≥3时阴性预测值（NPV）为0.88。结论：在多中心环境下，AI增强的病灶检测与评分被证明是可靠的辅助工具，其敏感度与放射科医师相当，且在PI-RADS≥3阈值（患者与病灶层面）及PI-RADS≥4阈值（病灶层面）的特异度优于人工判读。随着算法优化及进一步验证，AI检测有望在人工阅片前应用于临床工作流程以排除前列腺癌，从而显著提升诊断效率。
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