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.

中文翻译：

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评估 MRI 可见前列腺癌的自动深度学习系统：高级放大扩散加权成像与传统技术的比较

使用 MRI 检测前列腺癌 (PCa) 的基于深度学习的计算机辅助诊断 (DL-CAD) 系统已证明具有良好的性能。然而，DL-CAD 系统容易受到 DWI 中高度异质性的影响，这可能会干扰 DL-CAD 评估并损害性能。本研究旨在比较放大视野回波平面 DWI (z-DWI) 和全视野 DWI (f-DWI) 对 DL-CAD 的 PCa 检测，并找出影响 DL-CAD 的危险因素。 CAD 诊断效率。这项回顾性研究连续招募了 354 名参与者，他们因临床疑似 PCa 而接受了 MRI，包括 T2WI、f-DWI 和 z-DWI。DL-CAD 用于比较 f-DWI 和 z-DWI 在患者水平和病变水平上的性能。我们使用接受者操作特征分析的曲线下面积 (AUC) 和替代自由响应接受者操作特征分析来比较使用 f-DWI 和 z-DWI 的 DL-CAD 的性能。使用逻辑回归分析分析影响 DL-CAD 的危险因素。小于 0.05 的 P 值被认为具有统计学意义。使用 z-DWI 的 DL-CAD 在患者水平和病变水平上的总体准确度明显优于 f-DWI（AUCpatient：0.89 对 0.86；AUClesion：0.86 对 0.76；P < .001）。DWI 中病灶的对比噪声比 (CNR) 是假阳性的独立危险因素（比值比 [OR] = 1.12；P < .001）。直肠磁化伪影、病灶直径、和表观扩散系数 (ADC) 是假阳性（直肠磁敏伪影 = 5.46；直肠直径 = 1.12；ORADC = 0.998；所有 P < .001）和假阴性（直肠磁敏伪影 = 3.31；直肠直径 = 0.82）的独立危险因素；ORADC = 1.007；DL-CAD 的所有 P ≤ .03)。Z-DWI 有可能提高基于前列腺 MRI 的 DL-CAD 的检测性能。ChiCTR，没有。ChiCTR2100041834。2021 年 1 月 7 日注册。