Qian Hongwei, Huang Yanhua, Dong Yuxing, Xu Luohang, Chen Ruanchang, Zhou Fangzheng, Zhou Difan, Yu Jianhua, Lu Baochun
Department of Hepatobiliary and Pancreatic Surgery, Shaoxing People's Hospital, Shaoxing, China.
Shaoxing Key Laboratory of Minimally Invasive Abdominal Surgery and Precise Treatment of Tumor, Shaoxing, China.
Front Oncol. 2025 Mar 17;15:1546940. doi: 10.3389/fonc.2025.1546940. eCollection 2025.
This study aimed to develop and validate a predictive model integrating radiomics features and clinical variables to differentiate intrahepatic bile duct stones with cholangitis (IBDS-IL) from intrahepatic cholangiocarcinoma (ICC) preoperatively, as accurate distinction is crucial for determining appropriate treatment strategies.
A total of 169 patients (97 IBDS-IL and 72 ICC) who underwent surgical resection were retrospectively analyzed. Radiomics features were extracted from ultrasound images, and clinical variables with significant differences between groups were identified. Feature selection was performed using LASSO regression and recursive feature elimination (RFE). The radiomics model, clinical model, and combined model were constructed and evaluated using the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and SHAP analysis.
The radiomics model achieved an AUC of 0.962, and the clinical model achieved an AUC of 0.861. The combined model, integrating the Radiomics Score with clinical variables, demonstrated the highest predictive performance with an AUC of 0.988, significantly outperforming the clinical model ( < 0.05). Calibration curves showed excellent agreement between predicted and observed outcomes, and the Hosmer-Lemeshow test confirmed a good model fit ( = 0.998). DCA revealed that the combined model provided the greatest clinical benefit across a wide range of threshold probabilities. SHAP analysis identified the Radiomics Score as the most significant contributor, complemented by abdominal pain and liver atrophy.
The combined model integrating radiomics features and clinical data offers a powerful and reliable tool for preoperative differentiation of IBDS-IL and ICC. Its superior performance and clinical interpretability highlight its potential for improving diagnostic accuracy and guiding clinical decision-making. Further validation in larger, multicenter datasets is warranted to confirm its generalizability.
本研究旨在开发并验证一种整合放射组学特征和临床变量的预测模型，以在术前鉴别肝内胆管结石伴胆管炎（IBDS-IL）与肝内胆管癌（ICC），因为准确区分对于确定合适的治疗策略至关重要。
回顾性分析了169例行手术切除的患者（97例IBDS-IL和72例ICC）。从超声图像中提取放射组学特征，并确定组间有显著差异的临床变量。使用LASSO回归和递归特征消除（RFE）进行特征选择。使用曲线下面积（AUC）、校准曲线、决策曲线分析（DCA）和SHAP分析构建并评估放射组学模型、临床模型和联合模型。
放射组学模型的AUC为0.962，临床模型的AUC为0.861。将放射组学评分与临床变量相结合的联合模型表现出最高的预测性能，AUC为0.988，显著优于临床模型（<0.05）。校准曲线显示预测结果与观察结果之间具有良好的一致性，Hosmer-Lemeshow检验证实模型拟合良好（=0.998）。DCA显示联合模型在广泛的阈值概率范围内提供了最大的临床益处。SHAP分析确定放射组学评分为最主要的贡献因素，腹痛和肝萎缩也有辅助作用。
整合放射组学特征和临床数据的联合模型为术前鉴别IBDS-IL和ICC提供了一种强大而可靠的工具。其卓越的性能和临床可解释性凸显了其在提高诊断准确性和指导临床决策方面的潜力。有必要在更大的多中心数据集中进行进一步验证以确认其通用性。
