The early detection of pancreatic ductal adenocarcinoma (PDAC) is essential for optimal treatment of pancreatic cancer patients. We propose a tumor detection framework to improve the detection of pancreatic head tumors on CT scans. In this retrospective research study, CT images of 99 patients with pancreatic head cancer and 98 control cases from the Catharina Hospital Eindhoven were collected. A multi-stage 3D U-Net-based approach was used for PDAC detection including clinically significant secondary features such as pancreatic duct and common bile duct dilation. The developed algorithm was evaluated using a local test set comprising 59 CT scans. The model was externally validated in 28 pancreatic cancer cases of a publicly available medical decathlon dataset. The tumor detection framework achieved a sensitivity of 0.97 and a specificity of 1.00, with an area under the receiver operating curve (AUROC) of 0.99, in detecting pancreatic head cancer in the local test set. In the external test set, we obtained similar results, with a sensitivity of 1.00. The model provided the tumor location with acceptable accuracy obtaining a DICE Similarity Coefficient (DSC) of 0.37. This study shows that a tumor detection framework utilizing CT scans and secondary signs of pancreatic cancer can detect pancreatic tumors with high accuracy.
胰腺导管腺癌（PDAC）的早期检测对于胰腺癌患者获得最佳治疗至关重要。本研究提出一种肿瘤检测框架，旨在提高CT扫描对胰头肿瘤的检出能力。这项回顾性研究收集了来自埃因霍温Catharina医院的99例胰头癌患者和98例对照病例的CT影像。采用基于多阶段三维U-Net的方法进行PDAC检测，该方法整合了具有临床意义的继发性特征，如胰管和胆总管扩张。使用包含59例CT扫描的本地测试集对开发算法进行评估，并在公开医学十项全能数据集的28例胰腺癌病例中进行外部验证。在本地测试集的胰头癌检测中，该肿瘤检测框架的灵敏度达0.97，特异度达1.00，受试者工作特征曲线下面积（AUROC）为0.99。在外部测试集中获得相似结果，灵敏度达到1.00。模型能以可接受的精度提供肿瘤定位，获得0.37的DICE相似系数（DSC）。本研究表明，利用CT扫描和胰腺癌继发征象的肿瘤检测框架能够高精度地检测胰腺肿瘤。
肿瘤(癌症)患者之家