Development and Validation of a Radiomic-Based Model for Prediction of Intrahepatic Cholangiocarcinoma in Patients With Intrahepatic Lithiasis Complicated by Imagologically Diagnosed Mass

• Published in: Frontiers in oncology Vol. 10; p. 598253
• Main Authors: Xue, Beihui, Wu, Sunjie, Zheng, Minghua, Jiang, Huanchang, Chen, Jun, Jiang, Zhenghao, Tian, Tian, Tu, Yifan, Zhao, Huanhu, Shen, Xian, Ramen, Kuvaneshan, Wu, Xiuling, Zhang, Qiyu, Zeng, Qiqiang, Zheng, Xiangwu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 07.01.2021
• Subjects: intrahepatic cholangiocarcinoma; intrahepatic lithiasis; nomogram; Oncology; radiomics; risk factors; radiomics; risk factors; nomogram; intrahepatic lithiasis; intrahepatic cholangiocarcinoma
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2020.598253

This study was conducted with the intent to develop and validate a radiomic model capable of predicting intrahepatic cholangiocarcinoma (ICC) in patients with intrahepatic lithiasis (IHL) complicated by imagologically diagnosed mass (IM). A radiomic model was developed in a training cohort of 96 patients with IHL-IM from January 2005 to July 2019. Radiomic characteristics were obtained from arterial-phase computed tomography (CT) scans. The radiomic score (rad-score), based on radiomic features, was built by logistic regression after using the least absolute shrinkage and selection operator (LASSO) method. The rad-score and other independent predictors were incorporated into a novel comprehensive model. The performance of the Model was determined by its discrimination, calibration, and clinical usefulness. This model was externally validated in 35 consecutive patients. The rad-score was able to discriminate ICC from IHL in both the training group (AUC 0.829, sensitivity 0.868, specificity 0.635, and accuracy 0.723) and the validation group (AUC 0.879, sensitivity 0.824, specificity 0.778, and accuracy 0.800). Furthermore, the comprehensive model that combined rad-score and clinical features was great in predicting IHL-ICC (AUC 0.902, sensitivity 0.771, specificity 0.923, and accuracy 0.862). The radiomic-based model holds promise as a novel and accurate tool for predicting IHL-ICC, which can identify lesions in IHL timely for hepatectomy or avoid unnecessary surgical resection.