Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, No. 668 Jinhu Road, Huli District, Xiamen, 361015, Fujian, China.
Department of Radiology, Zhongshan Hospital, Fudan University, No. 180 Fenglin Road, Xuhui District, Shanghai, 200032, China.
Eur Radiol. 2024 Jan;34(1):548-559. doi: 10.1007/s00330-023-10002-w. Epub 2023 Aug 8.
To establish a non-invasive diagnostic system for intrahepatic mass-forming cholangiocarcinoma (IMCC) via decision tree analysis.
Totally 1008 patients with 504 pathologically confirmed IMCCs and proportional hepatocellular carcinomas (HCC) and combined hepatocellular cholangiocarcinomas (cHCC-CC) from multi-centers were retrospectively included (internal cohort n = 700, external cohort n = 308). Univariate and multivariate logistic regression analyses were applied to evaluate the independent clinical and MRI predictors for IMCC, and the selected features were used to develop a decision tree-based diagnostic system. Diagnostic efficacy of the established system was calculated by the receiver operating characteristic curve analysis in the internal training-testing and external validation cohorts, and also in small lesions ≤ 3 cm.
Multivariate analysis revealed that female, no chronic liver disease or cirrhosis, elevated carbohydrate antigen 19-9 (CA19-9) level, normal alpha-fetoprotein (AFP) level, lobulated tumor shape, progressive or persistent enhancement pattern, no enhancing tumor capsule, targetoid appearance, and liver surface retraction were independent characteristics favoring the diagnosis of IMCC over HCC or cHCC-CC (odds ratio = 3.273-25.00, p < 0.001 to p = 0.021). Among which enhancement pattern had the highest weight of 0.816. The diagnostic system incorporating significant characteristics above showed excellent performance in the internal training (area under the curve (AUC) 0.971), internal testing (AUC 0.956), and external validation (AUC 0.945) cohorts, as well as in small lesions ≤ 3 cm (AUC 0.956).
In consideration of the great generalizability and clinical efficacy in multi-centers, the proposed diagnostic system may serve as a non-invasive, reliable, and easy-to-operate tool in IMCC diagnosis, providing an efficient approach to discriminate IMCC from other HCC-containing primary liver cancers.
This study established a non-invasive, easy-to-operate, and explainable decision tree-based diagnostic system for intrahepatic mass-forming cholangiocarcinoma, which may provide essential information for clinical decision-making.
• Distinguishing intrahepatic mass-forming cholangiocarcinoma (IMCC) from other primary liver cancers is important for both treatment planning and outcome prediction. • The MRI-based diagnostic system showed great performance with satisfying generalization ability in the diagnosis and discrimination of IMCC. • The diagnostic system may serve as a non-invasive, easy-to-operate, and explainable tool in the diagnosis and risk stratification for IMCC.
通过决策树分析建立一种用于肝内肿块型胆管细胞癌(IMCC)的非侵入性诊断系统。
回顾性纳入来自多个中心的 1008 名患者,其中 504 名经病理证实为 IMCC,比例为肝细胞癌(HCC)和合并肝细胞胆管细胞癌(cHCC-CC)(内部队列 n=700,外部队列 n=308)。应用单因素和多因素逻辑回归分析评估 IMCC 的独立临床和 MRI 预测因素,并使用所选特征建立基于决策树的诊断系统。通过内部训练-测试和外部验证队列以及小病变(≤3cm)的受试者工作特征曲线分析计算所建立系统的诊断效能。
多因素分析显示,女性、无慢性肝病或肝硬化、CA19-9 水平升高、甲胎蛋白(AFP)水平正常、肿瘤形状分叶状、渐进性或持续性强化模式、无强化肿瘤包膜、靶样外观和肝表面回缩是支持 IMCC 诊断的独立特征,而非 HCC 或 cHCC-CC(比值比=3.273-25.00,p<0.001 至 p=0.021)。其中强化模式权重最高为 0.816。该纳入显著特征的诊断系统在内部训练(曲线下面积(AUC)0.971)、内部测试(AUC 0.956)和外部验证(AUC 0.945)队列中以及小病变(≤3cm)中表现出优异的性能(AUC 0.956)。
考虑到该系统在多中心的广泛适用性和临床疗效,该诊断系统可作为一种非侵入性、可靠且易于操作的 IMCC 诊断工具,为鉴别 IMCC 与其他含 HCC 的原发性肝癌提供有效的方法。
本研究建立了一种基于磁共振成像的肝内肿块型胆管细胞癌的非侵入性、易于操作且可解释的决策树诊断系统,可为临床决策提供重要信息。
鉴别肝内肿块型胆管细胞癌(IMCC)与其他原发性肝癌对于治疗计划和预后预测都很重要。
基于 MRI 的诊断系统在 IMCC 的诊断和鉴别中表现出优异的性能,具有令人满意的泛化能力。
该诊断系统可作为一种非侵入性、易于操作且可解释的工具,用于 IMCC 的诊断和风险分层。