Radiomic Analysis of Intrahepatic Cholangiocarcinoma: Non-Invasive Prediction of Pathology Data: A Multicenter Study to Develop a Clinical-Radiomic Model

Simple Summary: Intrahepatic cholangiocarcinoma is a disease with increasing incidence and poor prognosis. The clinicians have a limited capability to predict tumor behavior because the strongest predictors of survival are the pathology data that, unfortunately, can be determined only after surgery. Recently, radiomics, i.e., the mathematical analysis of imaging modalities, led to a major improvement in the non-invasive prediction of microscopic characteristics of several tumors. In this multicenter study, we collected a large number of patients affected by intrahepatic cholangiocarcinoma and we demonstrated that the radiomic data of the tumor and peritumoral tissue extracted from the computed tomography at diagnosis have a strong association with tumor grading and microscopic vascular invasion, which are two major biomarkers of tumor aggressiveness. The combination of radiomic and clinical data maximizes the accuracy of prediction. The integration of radiomics into clinical decision processes is probably one of the following steps toward a precision medicine approach in patients affected by intrahepatic cholangiocarcinoma.Standard imaging cannot assess the pathology details of intrahepatic cholangiocarcinoma (ICC). We investigated whether CT-based radiomics may improve the prediction of tumor characteristics. All consecutive patients undergoing liver resection for ICC (2009-2019) in six high-volume centers were evaluated for inclusion. On the preoperative CT, we segmented the ICC (Tumor-VOI, i.e., volume-of-interest) and a 5-mm parenchyma rim around the tumor (Margin-VOI). We considered two types of pathology data: tumor grading (G) and microvascular invasion (MVI). The predictive models were internally validated. Overall, 244 patients were analyzed: 82 (34%) had G3 tumors and 139 (57%) had MVI. For G3 prediction, the clinical model had an AUC = 0.69 and an Accuracy = 0.68 at internal cross-validation. The addition of radiomic features extracted from the portal phase of CT improved the model performance (Clinical data+Tumor-VOI: AUC = 0.73/Accuracy = 0.72; +Tumor-/Margin-VOI: AUC = 0.77/Accuracy = 0.77). Also for MVI prediction, the addition of portal phase radiomics improved the model performance (Clinical data: AUC = 0.75/Accuracy = 0.70; +Tumor-VOI: AUC = 0.82/Accuracy = 0.73; +Tumor-/Margin-VOI: AUC = 0.82/Accuracy = 0.75). The permutation tests confirmed that a combined clinical-radiomic model outperforms a purely clinical one (p < 0.05). The addition of the textural features extracted from the arterial phase had no impact. In conclusion, the radiomic features of the tumor and peritumoral tissue extracted from the portal phase of preoperative CT improve the prediction of ICC grading and MVI.