Introduction: Cholangiocarcinoma (CCA) is a highly lethal malignancy originating in the bile ducts, often diagnosed late with poor prognosis. Differentiating benign from malignant biliary tumors remains challenging, necessitating advanced diagnostic techniques. Objective: This study aims to enhance the diagnostic accuracy of endoscopic ultrasound (EUS) for distal cholangiocarcinoma (dCCA) using advanced convolutional neural networks (CCNs) for the classification and segmentation of EUS images, specifically targeting dCCAs, the pancreas, and the bile duct. Materials and Methods: In this retrospective study, EUS images from patients diagnosed with dCCA via biopsy and an EUS-identified bile duct tumor were evaluated. A custom CNN was developed for classification, trained on 156 EUS images. To enhance the model’s robustness, image augmentation techniques were applied, generating a total of 1248 images. For tumor and organ segmentation, the DeepLabv3+ network with ResNet50 architecture was utilized, employing Tversky loss to manage unbalanced classes. Performance evaluation included metrics such as accuracy, sensitivity, specificity, and Intersection over Union (IoU). These methods were implemented in collaboration with the ADAPTED Research Group at the Technical University of Cluj-Napoca. Results: The classification model achieved a high accuracy of 97.82%, with precision and specificity both at 100% and sensitivity at 94.44%. The segmentation models for the pancreas and bile duct demonstrated global accuracies of 84% and 90%, respectively, with robust IoU scores indicating good overlap between predicted and actual contours. The application performed better than the UNet model, particularly in generalization and boundary delineation. Conclusions: This study demonstrates the significant potential of AI in EUS imaging for dCCA, presenting a robust tool that enhances diagnostic accuracy and efficiency. The developed MATLAB application serves as a valuable aid for medical professionals, facilitating informed decision-making and improving patient outcomes in the diagnosis of cholangiocarcinoma and related pathologies.
引言：胆管癌是一种起源于胆管的高度致死性恶性肿瘤，常因诊断较晚而预后不良。鉴别胆道肿瘤的良恶性仍具挑战性，需要先进的诊断技术。目的：本研究旨在通过应用先进卷积神经网络对超声内镜图像进行分类与分割，提升其对远端胆管癌的诊断准确性，重点关注远端胆管癌、胰腺及胆管的识别。材料与方法：本回顾性研究纳入了经活检确诊为远端胆管癌且超声内镜发现胆管肿瘤患者的影像数据。研究构建了定制化卷积神经网络分类模型，使用156张超声内镜图像进行训练。为增强模型鲁棒性，采用图像增强技术将样本量扩充至1248张。针对肿瘤及器官分割任务，采用基于ResNet50架构的DeepLabv3+网络，并运用Tversky损失函数处理类别不平衡问题。性能评估指标包括准确率、敏感性、特异性及交并比。所有方法均与克卢日-纳波卡技术大学ADAPTED研究组合作完成。结果：分类模型准确率达97.82%，精确度与特异性均为100%，敏感性为94.44%。胰腺与胆管分割模型的整体准确率分别为84%和90%，其交并比评分显示预测轮廓与实际轮廓具有良好重合度。该应用在泛化能力与边界划分方面表现优于UNet模型。结论：本研究证实人工智能在远端胆管癌超声内镜成像中具有重要应用潜力，所开发的工具能显著提升诊断准确性与效率。基于MATLAB构建的应用程序可为医疗专业人员提供有力辅助，在胆管癌及相关疾病的诊断中促进临床决策优化，改善患者预后。
Deep Learning in Endoscopic Ultrasound: A Breakthrough in Detecting Distal Cholangiocarcinoma
肿瘤(癌症)患者之家