内镜超声医师对胰管腺癌（PDAC）的诊断高度依赖。本研究的目的是基于内镜超声图像开发深度学习影像组学（DLR）模型，用于识别PDAC并探索其真正的临床效益。以包括PDAC和良性病变的内镜超声图像的回顾性数据集作为训练组（N = 368名患者），开发DLR模型，并使用前瞻性数据集作为测试组（N = 123名患者）来验证DLR模型的有效性。此外，七名内镜超声医师进行了两轮读者研究，在有或没有DLR辅助的情况下对测试组进行进一步评估DLR模型的临床适用性和真正效益。在前瞻性测试组中，DLR表现出0.936的受试者工作特征曲线下面积（95％置信区间[CI]，0.889-0.976），敏感性分别为0.831（95％ CI，0.746-0.913）和0.904（95％ CI，0.820-0.980）。在DLR协助下，七名内镜超声医师的总体诊断表现有所改善：一名内镜超声医师实现了特异性显著扩展（p = 0.035），另一名实现了敏感性显著增加（p = 0.038）。在初级内镜超声医师组中，有DLR的诊断表现较高或可与无DLR协助的高级内镜超声医师组相媲美。前瞻性测试组验证了基于内镜超声图像的DLR模型有效识别PDAC。借助该模型的帮助，不同经验水平的内镜超声医师之间的差距缩小，内镜超声医师的准确性扩展。 ©2023美国癌症协会。

Endosonographers are highly dependent on the diagnosis of pancreatic ductal adenocarcinoma (PDAC). The objectives of this study were to develop a deep-learning radiomics (DLR) model based on endoscopic ultrasonography (EUS) images for identifying PDAC and to explore its true clinical benefit.A retrospective data set of EUS images that included PDAC and benign lesions was used as a training cohort (N = 368 patients) to develop the DLR model, and a prospective data set was used as a test cohort (N = 123 patients) to validate the effectiveness of the DLR model. In addition, seven endosonographers performed two rounds of reader studies on the test cohort with or without DLR assistance to further assess the clinical applicability and true benefits of the DLR model.In the prospective test cohort, DLR exhibited an area under the receiver operating characteristic curves of 0.936 (95% confidence interval [CI], 0.889-0.976) with a sensitivity of 0.831 (95% CI, 0.746-0.913) and 0.904 (95% CI, 0.820-0.980), respectively. With DLR assistance, the overall diagnostic performance of the seven endosonographers improved: one endosonographer achieved a significant expansion of specificity (p = .035,) and another achieved a significant increase in sensitivity (p = .038). In the junior endosonographer group, the diagnostic performance with the help of the DLR was higher than or comparable to that of the senior endosonographer group without DLR assistance.A prospective test cohort validated that the DLR model based on EUS images effectively identified PDAC. With the assistance of this model, the gap between endosonographers at different levels of experience narrowed, and the accuracy of endosonographers expanded.© 2023 American Cancer Society.