Ozawa Mizuki, Sone Miyuki, Hijioka Susumu, Hara Hidenobu, Wakatsuki Yusuke, Ishihara Toshihiro, Hattori Chihiro, Hirano Ryo, Ambo Shintaro, Esaki Minoru, Kusumoto Masahiko, Matsui Yoshiyuki
Department of Diagnostic Radiology, National Cancer Center Hospital, 5-1-1, Tsukiji, Chuo-ku, Tokyo, 1040045, Japan.
Cancer Medicine, Jikei University Graduate School of Medicine, Tokyo, Japan.
Jpn J Radiol. 2025 Jul 18. doi: 10.1007/s11604-025-01836-z.
Detecting small pancreatic ductal adenocarcinomas (PDAC) is challenging owing to their difficulty in being identified as distinct tumor masses. This study assesses the diagnostic performance of a three-dimensional convolutional neural network for the automatic detection of small PDAC using both automatic tumor mass detection and indirect indicator evaluation.
High-resolution contrast-enhanced computed tomography (CT) scans from 181 patients diagnosed with PDAC (diameter ≤ 2 cm) between January 2018 and December 2023 were analyzed. The D/P ratio, which is the cross-sectional area of the MPD to that of the pancreatic parenchyma, was identified as an indirect indicator. A total of 204 patient data sets including 104 normal controls were analyzed for automatic tumor mass detection and D/P ratio evaluation. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were evaluated to detect tumor mass. The sensitivity of PDAC detection was compared with that of the software and radiologists, and tumor localization accuracy was validated against endoscopic ultrasonography (EUS) findings.
The sensitivity, specificity, PPV, and NPV for tumor mass detection were 77.0%, 76.0%, 75.5%, and 77.5%, respectively; for D/P ratio detection, 87.0%, 94.2%, 93.5%, and 88.3%, respectively; and for combined tumor mass and D/P ratio detections, 96.0%, 70.2%, 75.6%, and 94.8%, respectively. No significant difference was observed between the software's sensitivity and that of the radiologist's report (software, 96.0%; radiologist, 96.0%; p = 1). The concordance rate between software findings and EUS was 96.0%.
Combining indirect indicator evaluation with tumor mass detection may improve small PDAC detection accuracy.
由于难以将小胰腺癌（PDAC）识别为明显的肿瘤块，检测小胰腺癌具有挑战性。本研究使用自动肿瘤块检测和间接指标评估，评估三维卷积神经网络对小胰腺癌自动检测的诊断性能。
分析了2018年1月至2023年12月期间181例诊断为PDAC（直径≤2 cm）患者的高分辨率对比增强计算机断层扫描（CT）。主胰管（MPD）与胰腺实质横截面积之比（D/P比）被确定为间接指标。共分析了包括104例正常对照在内的2照在内的204例患者数据集，用于自动肿瘤块检测和D/P比评估。评估检测肿瘤块的灵敏度、特异性、阳性预测值（PPV）和阴性预测值（NPV）。将PDAC检测的灵敏度与软件和放射科医生的灵敏度进行比较，并根据内镜超声（EUS）结果验证肿瘤定位准确性。
肿瘤块检测的灵敏度、特异性、PPV和NPV分别为77.0%、76.0%、75.5%和77.5%；D/P比检测分别为87.0%、94.2%、93.5%和88.3%；肿瘤块和D/P比联合检测分别为96.0%、70.2%、75.6%和94.8%。软件的灵敏度与放射科医生报告的灵敏度之间未观察到显著差异（软件，96.0%；放射科医生，96.0%；p = 1）。软件结果与EUS之间的一致性率为96.0%。
将间接指标评估与肿瘤块检测相结合可能提高小胰腺癌的检测准确性。
