基于深度学习的计算机辅助诊断（DL-CAD）系统使用MRI进行前列腺癌（PCa）检测表现出良好的性能。然而，DL-CAD系统容易受到DWI中高度异质性的影响，从而影响DL-CAD评估并损害性能。本研究旨在比较使用放大视野回波平面DWI（z-DWI）和全视野DWI（f-DWI）的DL-CAD的PCa检测，并找到影响DL-CAD诊断效率的风险因素。该回顾性研究纳入了354名连续参与者，因临床上疑似PCa而接受了MRI检查，其中包括T2WI、f-DWI和z-DWI。DL-CAD用于比较f-DWI和z-DWI的性能，包括患者水平和病变水平。我们使用接受者操作特征曲线分析的曲线下面积（AUC）和替代自由响应接收器操作特征曲线分析来比较使用f-DWI和z-DWI的DL-CAD的性能。使用Logistic回归分析分析影响DL-CAD的风险因素。P值小于0.05被认为具有统计学意义。DL-CAD使用z-DWI在患者水平和病变水平上的整体准确性明显优于f-DWI（AUCpatient：0.89 vs. 0.86; AUClesion：0.86 vs. 0.76; P < 0.001）。DWI中病变的对比噪声比（CNR）是假阳性的独立风险因素（Odds Ratio [OR] = 1.12；P < 0.001）。直肠磁化感应伪影、病变直径和表观扩散系数（ADC）是DL-CAD假阳性（OR直肠磁化感应伪影 = 5.46; OR直径=1.12; OR ADC = 0.998;所有P < 0.001）和假阴性（OR直肠磁化感应伪影=3.31; OR直径=0.82; OR ADC=1.007;所有P≤ 0.03）的独立风险因素。Z-DWI有潜力提高基于前列腺MRI的DL-CAD的检测性能。ChiCTR，NO. ChiCTR2100041834。2023年作者（们）版权所有。

Deep-learning-based computer-aided diagnosis (DL-CAD) systems using MRI for prostate cancer (PCa) detection have demonstrated good performance. Nevertheless, DL-CAD systems are vulnerable to high heterogeneities in DWI, which can interfere with DL-CAD assessments and impair performance. This study aims to compare PCa detection of DL-CAD between zoomed-field-of-view echo-planar DWI (z-DWI) and full-field-of-view DWI (f-DWI) and find the risk factors affecting DL-CAD diagnostic efficiency.This retrospective study enrolled 354 consecutive participants who underwent MRI including T2WI, f-DWI, and z-DWI because of clinically suspected PCa. A DL-CAD was used to compare the performance of f-DWI and z-DWI both on a patient level and lesion level. We used the area under the curve (AUC) of receiver operating characteristics analysis and alternative free-response receiver operating characteristics analysis to compare the performances of DL-CAD using f- DWI and z-DWI. The risk factors affecting the DL-CAD were analyzed using logistic regression analyses. P values less than 0.05 were considered statistically significant.DL-CAD with z-DWI had a significantly better overall accuracy than that with f-DWI both on patient level and lesion level (AUCpatient: 0.89 vs. 0.86; AUClesion: 0.86 vs. 0.76; P < .001). The contrast-to-noise ratio (CNR) of lesions in DWI was an independent risk factor of false positives (odds ratio [OR] = 1.12; P < .001). Rectal susceptibility artifacts, lesion diameter, and apparent diffusion coefficients (ADC) were independent risk factors of both false positives (ORrectal susceptibility artifact = 5.46; ORdiameter, = 1.12; ORADC = 0.998; all P < .001) and false negatives (ORrectal susceptibility artifact = 3.31; ORdiameter = 0.82; ORADC = 1.007; all P ≤ .03) of DL-CAD.Z-DWI has potential to improve the detection performance of a prostate MRI based DL-CAD.ChiCTR, NO. ChiCTR2100041834 . Registered 7 January 2021.© 2023. The Author(s).