Zhuo Liyong, Li Xiaomeng, Dai Shuo, Xing Lihong, Song Zijun, Liu Xueyan, Wang Jianing, Li Caiying, Yin Xiaoping
Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China.
Department of Mathematical Sciences, Liaocheng University, Liaocheng, Shandong, People's Republic of China.
Cancer Med. 2025 Aug;14(15):e71120. doi: 10.1002/cam4.71120.
To establish a model based on intratumoral and peritumoral radiomics for preoperatively differentiating solitary intrahepatic mass-forming cholangiocarcinoma (IMCC) lesions from colorectal cancer liver metastases (CRLM).
Preoperative MRI scans from IMCC patients were retrospectively obtained from three academic medical centers. Radiomics features were extracted from the intratumoral and multiple peritumoral regions. After feature selection, the optimal peritumoral range was determined. The radiomics model was developed by integrating both single-sequence and multisequence models through probabilistic ensemble learning. Significant variables from clinical imaging features, radiomics, were integrated into a combined model, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC).
A total of 170 patients (93 IMCC, 77 CRLM) comprised the training cohort, and 42 (23 IMCC, 19 CRLM) formed the external validation cohort. The Combined model achieved superior AUCs in training (0.978 [95% CI: 0.971-0.985]) and validation (0.940 [0.899-0.968]), outperforming radiomics (training: 0.947 [(95% CI: 0.932-0.961]); validation: 0.908 [(95% CI: 0.861-0.943)]) and Clin-Imag models (training: 0.858 [95% CI: 0.831-0.880]; validation: 0.842 [95% CI: 0.786-0.889]) (DeLong test, p = 0.001). SHAP analysis identified DWI-based 5-mm peritumoral features and clinical-imaging variables (e.g., lesion location and bile duct dilation) as key discriminators.
The combined model integrating clinical-imaging variables and multiparametric MRI-derived intratumoral and 5-mm peritumoral radiomics features provides a non-invasive tool for distinguishing solitary IMCC from CRLM, offering potential clinical utility for guiding personalized treatment strategies and avoiding unnecessary invasive interventions.
建立一种基于肿瘤内和瘤周影像组学的模型，用于术前鉴别孤立性肝内肿块型胆管癌（IMCC）与结直肠癌肝转移（CRLM）。
回顾性收集来自三个学术医学中心的IMCC患者的术前MRI扫描图像。从肿瘤内和多个瘤周区域提取影像组学特征。在特征选择后，确定最佳瘤周范围。通过概率集成学习整合单序列和多序列模型来构建影像组学模型。将临床影像特征中的重要变量（即影像组学特征）整合到一个联合模型中，并使用受试者操作特征曲线下面积（AUC）评估其性能。
共有170例患者（93例IMCC，77例CRLM）组成训练队列，42例（23例IMCC，19例CRLM）组成外部验证队列。联合模型在训练（0.978 [95% CI：0.971 - 0.985]）和验证（0.940 [0.899 - 0.968]）中获得了更高的AUC，优于影像组学模型（训练：0.947 [(95% CI：0.932 - 0.961)]；验证：0.908 [(95% CI：0.861 - 0.943)]）和临床影像模型（训练：0.858 [95% CI：0.831 - 0.880]；验证：0.842 [95% CI：0.786 - 0.889]）（DeLong检验，p = 0.001）。SHAP分析确定基于扩散加权成像（DWI）的5毫米瘤周特征和临床影像变量（如病变位置和胆管扩张）为关键鉴别因素。
整合临床影像变量以及多参数MRI衍生的肿瘤内和5毫米瘤周影像组学特征的联合模型为区分孤立性IMCC和CRLM提供了一种非侵入性工具，为指导个性化治疗策略和避免不必要的侵入性干预提供了潜在的临床应用价值。
