Zhuo Liyong, Chen Wenjing, Xing Lihong, Li Xiaomeng, Song Zijun, Dong Jinghui, Zhang Yanyan, Li Hongjun, Cui Jingjing, Han Yuxiao, Hao Jiawei, Wang Jianing, Yin Xiaoping, Li Caiying
Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China.
Insights Imaging. 2025 May 14;16(1):101. doi: 10.1186/s13244-025-01985-9.
This study aimed to develop a quantitative approach to measure intratumor heterogeneity (ITH) using MRI scans and predict the pathological grading of intrahepatic mass-forming cholangiocarcinoma (IMCC).
Preoperative MRI scans from IMCC patients were retrospectively obtained from five academic medical centers, covering the period from March 2018 to April 2024. Radiomic features were extracted from the whole tumor and its subregions, which were segmented using K-means clustering. An ITH index was derived from a habitat model integrating output probabilities of the subregions-based models. Significant variables from clinical laboratory-imaging features, radiomics, and the habitat model were integrated into a predictive model, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC).
The final training and internal validation datasets included 197 patients (median age, 59 years [IQR, 52-65 years]); the external validation dataset included 43 patients (median age, 58.5 years [IQR, 52.25-69.75 years]). The habitat model achieved AUCs of 0.847 (95% CI: 0.783, 0.911) in the training set and 0.753 (95% CI: 0.595, 0.911) in the internal validation set. Furthermore, the combined model, integrating imaging variables, the habitat model, and radiomics model, demonstrated improved predictive performance, with AUCs of 0.895 (95% CI: 0.845, 0.944) in the training dataset, 0.790 (95% CI: 0.65, 0.931) in the internal validation dataset, and 0.815 (95% CI: 0.68, 0.951) in the external validation dataset.
The combined model based on MRI-derived quantification of ITH, along with clinical, laboratory, radiological, and radiomic features, showed good performance in predicting IMCC grading.
This model, integrating MRI-derived intrahepatic mass-forming cholangiocarcinoma (IMCC) classification metrics with quantitative radiomic analysis of intratumor heterogeneity (ITH), demonstrates enhanced accuracy in tumor grade prediction, advancing risk stratification for clinical decision-making in IMCC management.
Grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) is important for risk stratification, clinical decision-making, and personalized therapeutic optimization. Quantitative intratumor heterogeneity can accurately predict the pathological grading of IMCC. This combined model provides higher diagnostic accuracy.
本研究旨在开发一种定量方法，利用磁共振成像（MRI）扫描测量肿瘤内异质性（ITH），并预测肝内肿块型胆管癌（IMCC）的病理分级。
回顾性收集了2018年3月至2024年4月期间来自五个学术医疗中心的IMCC患者术前MRI扫描数据。从整个肿瘤及其子区域提取影像组学特征，这些子区域通过K均值聚类进行分割。ITH指数源自一个栖息地模型，该模型整合了基于子区域模型的输出概率。将临床实验室-影像特征、影像组学和栖息地模型中的显著变量整合到一个预测模型中，并使用受试者操作特征曲线（ROC）下面积（AUC）评估其性能。
最终的训练集和内部验证数据集包括197例患者（中位年龄59岁[四分位间距，52 - 65岁]）；外部验证数据集包括43例患者（中位年龄58.5岁[四分位间距，52.25 - 69.75岁]）。栖息地模型在训练集中的AUC为0.847（95%可信区间：0.783，0.911），在内部验证集中为0.753（95%可信区间：0.595，0.911）。此外，整合影像变量、栖息地模型和影像组学模型的联合模型显示出更好的预测性能，在训练数据集中的AUC为0.895（95%可信区间：0.845，0.944），在内部验证数据集中为0.790（95%可信区间：0.65，0.931），在外部验证数据集中为0.815（95%可信区间：0.68，0.951）。
基于MRI对ITH进行量化的联合模型，结合临床、实验室、放射学和影像组学特征，在预测IMCC分级方面表现良好。
该模型将基于MRI的肝内肿块型胆管癌（IMCC）分类指标与肿瘤内异质性（ITH）的定量影像组学分析相结合，在肿瘤分级预测中显示出更高的准确性，推进了IMCC管理中临床决策的风险分层。
肝内肿块型胆管癌（IMCC）的分级对于风险分层、临床决策和个性化治疗优化很重要。定量肿瘤内异质性可准确预测IMCC的病理分级。这种联合模型提供了更高的诊断准确性。
