To explore the value of prostate imaging reporting and data system version 2 (PI-RADS V2) combined with quantitative parameters derived from apparent diffusion coefficient (ADC) map in the diagnosis of peripheral zone prostate cancer. Methods: A total of 50 patients who underwent prostate multiparametric MRI (mpMRI) with suspicious peripheral nodules were retrospectively enrolled, and all patients were biopsy-proven histologically. Two radiologists analyzed the position and category of peripheral zone lesions based on PI-RADS V2. Then 12 ADC quantitative parameters were calculated regarding each lesion on the ADC map by post-processing software. The lesions were divided into malignant group and benign group according to histopathological findings. The ADC quantitative parameters between groups were compared, and stepwise logistic regression analysis was used to build a discriminative model. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were performed to evaluate the diagnostic power and clinical benefit. Results: Twenty-eight peripheral zone prostate malignant lesions and 25 benign lesions were obtained finally. The area under the ROC curve, sensitivity and specificity to differentiate peripheral zone prostate malignant from benign lesions were as follows: 0.803, 60.71%, 92.00% (PI-RADS V2 score), 0.857, 89.29%, 76.00% (ADC model), and 0.891, 71.43%, 92.00% (combined model), respectively. The discriminative power of the combined model was significantly improved compared with PI-RADS V2 score (P=0.012). The combined model had relatively optimal overall net benefit, which outperformed the PI-RADS V2 score when threshold probability varied in the range of 0.05-0.27 and 0.46-0.81. Conclusion: PI-RADS V2 combined with quantitative analysis of ADC map improve the power in discriminating peripheral zone prostate cancer from benign lesions, and the clinical benefit as well.
目的：探讨第2版前列腺影像报告和数据系统(prostate imaging reporting and data system version 2，PI-RADS V2)评分联合表观扩散系数(apparent diffusion coefficient，ADC)图像定量参数对外周带前列腺癌的诊断价值。方法：回顾性搜集50例前列腺多参数磁共振成像(multiparametric MRI，mpMRI)检查提示存在外周带结节的患者，且均经穿刺活检获得病理诊断。由两名高年资影像科医师根据PI-RADS V2标准对病灶进行定位及评分，分别利用后处理软件在ADC图像上分析、计算对应病灶区的12种ADC图像定量参数。根据病理结果将病灶分为癌灶组和良性病灶组。比较两组病灶各ADC定量参数的差异，对组间差异有统计学意义的参数采用逻辑回归逐步法拟合建模，通过受试者工作特征(receiver operating characteristic，ROC)曲线和决策曲线分析(decision curve analysis，DCA)评价不同方法的诊断效能和临床受益。结果：最终共获得外周带癌灶28个、良性病灶25个。PI-RADS V2评分、ADC模型及联合模型(PI-RADS V2评分+ ADC模型)区分外周带癌灶和良性病灶的ROC曲线下面积、敏感度、特异度分别为0.803，60.71%，92.00%；0.857，89.29%，76.00%；0.891，71.43%，92.00%，联合模型较PI-RADS V2评分的诊断效能有明显提升(P=0.012)，且在阈概率0.05~0.27和0.46~0.81范围内该联合模型具有相对最佳的总体净受益率，优于PI-RADS V2评分。结论：PI-RADS V2联合ADC图像定量分析能显著提高其区分外周带前列腺癌和良性病变的诊断效能，并改善临床受益。.