Table_1_Development and Validation of a Radiomic-Based Model for Prediction of Intrahepatic Cholangiocarcinoma in Patients With Intrahepatic Lithiasis Complicated by Imagologically Diagnosed Mass.xlsx

BackgroundThis 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).
MethodsA 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.
ResultsThe 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).
ConclusionsThe 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.

背景 本研究旨在开发并验证一款放射组学模型，用于预测合并影像学确诊肿块（imagologically diagnosed mass, IM）的肝内结石（intrahepatic lithiasis, IHL）患者罹患肝内胆管癌（intrahepatic cholangiocarcinoma, ICC）的风险。
方法 本研究于2005年1月至2019年7月纳入96例合并IM的IHL患者作为训练队列以开发放射组学模型。研究从动脉期计算机断层扫描（arterial-phase computed tomography, CT）影像中提取放射组学特征；基于放射组学特征构建放射组学评分（radiomic score, rad-score）：先采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）方法进行特征筛选，再通过逻辑回归建立评分模型。随后将rad-score与其他独立预测因子整合，构建全新的综合模型。通过判别效能、校准度及临床实用性评估该模型的性能，并纳入35例连续入组患者开展外部验证。
结果 放射组学评分可在训练队列与验证队列中有效区分ICC与IHL：训练队列的曲线下面积（Area Under Curve, AUC）为0.829，灵敏度0.868，特异度0.635，准确率0.723；验证队列的AUC为0.879，灵敏度0.824，特异度0.778，准确率0.800。进一步整合rad-score与临床特征的综合模型，对IHL相关ICC的预测效能更优，其AUC达0.902，灵敏度0.771，特异度0.923，准确率0.862。
结论 本研究开发的基于放射组学的模型有望成为预测IHL相关ICC的新型精准辅助工具，可及时识别IHL患者中的癌性病灶以指导肝切除术开展，或避免不必要的外科切除操作。