Qi Zuochao, Yuan Hao, Li Qingshan, Chen Pengyu, Li Dongxiao, Chen Kunlun, Meng Bo, Ning Peigang, Yu Haibo, Li Deyu
Department of Hepatobiliary and Pancreatic Surgery, Zhengzhou University People's Hospital, Zhengzhou, 450003, China.
Department of Hepatobiliary and Pancreatic Surgery, Henan University People's Hospital, Zhengzhou, 450003, China.
World J Surg Oncol. 2025 Apr 26;23(1):164. doi: 10.1186/s12957-025-03819-w.
To develop and validate an MRI-based fusion model for preoperative prediction of perineural invasion (PNI) status in patients with intrahepatic cholangiocarcinoma (ICC).
A retrospective collection of 192 ICC patients from three medical centers (training set: n = 147; external test set: n = 45) was performed. Patients were classified into the PNI-positive and PNI-negative groups based on postoperative pathological results. After image preprocessing, a total of 1,197 features were extracted from T2-weighted imaging (T2WI). Feature selection was performed, and a radiomics model was constructed using machine learning algorithms, followed by SHapley Additive exPlanations (SHAP) visualization. Subsequently, a deep learning model was constructed based on the pre-trained ResNet101, with Gradient-weighted Class Activation Mapping (Grad-CAM) used for visualization. Finally, a fusion model incorporating deep learning, radiomics, and clinical features was developed using logistic regression, and visualization was performed with a nomogram. The predictive performance of the model was evaluated based on the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).
The fusion model, which integrates deep learning signature, radiomics signature, and two clinical features, demonstrated strong discrimination for PNI status. In the training set, the AUC was 0.905, with an accuracy of 0.823; in the external test set, the AUC was 0.760, with an accuracy of 0.778. Visualization methods provided support for the practical application of the model.
The fusion model aids in the preoperative identification of PNI status in patients with ICC, and may help guide clinical decision-making regarding preoperative staging and adjuvant therapy.
开发并验证基于磁共振成像（MRI）的融合模型，用于术前预测肝内胆管癌（ICC）患者的神经周围侵犯（PNI）状态。
回顾性收集了来自三个医疗中心的192例ICC患者（训练集：n = 147；外部测试集：n = 45）。根据术后病理结果将患者分为PNI阳性组和PNI阴性组。经过图像预处理后，从T2加权成像（T2WI）中提取了总共1197个特征。进行特征选择，并使用机器学习算法构建放射组学模型，随后进行SHapley加性解释（SHAP）可视化。随后，基于预训练的ResNet101构建深度学习模型，使用梯度加权类激活映射（Grad-CAM）进行可视化。最后，使用逻辑回归开发了一个融合深度学习、放射组学和临床特征的融合模型，并使用列线图进行可视化。基于曲线下面积（AUC）、校准曲线和决策曲线分析（DCA）评估模型的预测性能。
融合模型整合了深度学习特征、放射组学特征和两个临床特征，对PNI状态具有很强的鉴别能力。在训练集中，AUC为0.905，准确率为0.823；在外部测试集中，AUC为0.760，准确率为0.778。可视化方法为模型的实际应用提供了支持。
该融合模型有助于术前识别ICC患者的PNI状态，并可能有助于指导关于术前分期和辅助治疗的临床决策。
