评估开发计算机视觉算法的可行性，该算法使用术前计算机断层扫描 (CT) 扫描来预测接受 Whipple 治疗胰腺导管腺癌 (PDAC) 的患者肠系膜上动脉 (SMA) 边缘状态，并将算法性能与专家的算法性能进行比较腹部放射科医生和外科肿瘤学家。完整的手术切除是治愈 PDAC 的唯一机会；然而，目前预测血管侵犯的方法准确性有限。纳入了接受 Whipple 治疗并进行术前增强 CT 扫描的 PDAC 成年患者（2010-2022 年）。 SMA 在 CT 扫描上手动注释，我们训练了用于 SMA 分割的 U-Net 算法和用于预测 SMA 边缘状态的 ResNet50 算法。放射科医生和外科医生以盲法方式审查扫描结果。以病理报告中的 SMA 边缘状态为参考。纳入了 200 名患者。四十名患者 (20%) 的 SMA 边缘呈阳性。对于分割任务，U-Net 模型的 Dice 相似度系数为 0.90。对于分类任务，所有读者都表现出有限的灵敏度，尽管算法的灵敏度最高为 0.43（放射科医生和外科医生分别为 0.23 和 0.36）。特异性非常好，放射科医生和算法显示出最高的特异性，为 0.94。最后，对于放射科医生和外科医生来说，该算法的准确度分别为 0.85、0.80 和 0.76。我们证明了开发计算机视觉算法来使用术前 CT 扫描预测 SMA 边缘状态的可行性，强调了其增强血管预测的潜力。参与。版权所有 © 2024 Wolters Kluwer Health, Inc. 保留所有权利。

To evaluate the feasibility of developing a computer vision algorithm that uses preoperative computed tomography (CT) scans to predict superior mesenteric artery (SMA) margin status in patients undergoing Whipple for pancreatic ductal adenocarcinoma (PDAC), and to compare algorithm performance to that of expert abdominal radiologists and surgical oncologists.Complete surgical resection is the only chance to achieve a cure for PDAC; however, current modalities to predict vascular invasion have limited accuracy.Adult patients with PDAC who underwent Whipple and had preoperative contrast-enhanced CT scans were included (2010-2022). The SMA was manually annotated on the CT scans, and we trained a U-Net algorithm for SMA segmentation and a ResNet50 algorithm for predicting SMA margin status. Radiologists and surgeons reviewed the scans in a blinded fashion. SMA margin status per pathology reports was the reference.Two hundred patients were included. Forty patients (20%) had a positive SMA margin. For the segmentation task, the U-Net model achieved a Dice Similarity Coefficient of 0.90. For the classification task, all readers demonstrated limited sensitivity, although the algorithm had the highest sensitivity at 0.43 (versus 0.23 and 0.36 for the radiologists and surgeons, respectively). Specificity was universally excellent, with the radiologist and algorithm demonstrating the highest specificity at 0.94. Finally, the accuracy of the algorithm was 0.85 versus 0.80 and 0.76 for the radiologists and surgeons, respectively.We demonstrated the feasibility of developing a computer vision algorithm to predict SMA margin status using preoperative CT scans, highlighting its potential to augment the prediction of vascular involvement.Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.