A machine learning model based on CT radiomics for preoperatively differentiating intrahepatic mass-type cholangiocarcinoma and inflammatory pseudotumours

Objective Intrahepatic cholangiocarcinoma (ICC) and hepatic inflammatory pseudotumours (IPTs) share similar imaging features, leading to unnecessary biopsies and surgeries. Accurate preoperative differentiation is essential. Current studies using traditional imaging analysis have limited accuracy. We developed a machine learning model based on clinical and CT radiomic features to improve diagnostic accuracy. Methods From May 2008 to January 2024, the data of 112 patients with ICC and 34 patients with hepatic IPTs who underwent preoperative plain and enhanced CT scans and whose diseases were confirmed by surgery and pathology were retrospectively analysed. A radiomic feature set, a clinical feature set, and a radiomic + clinical feature set were developed, and each was used to construct 14 machine learning models. The optimal hyperparameters were identified using fivefold cross-validation and a grid search. Finally, the area under the curve (AUC), accuracy, recall, precision, F1, Kappa value and other indicators were used to evaluate the performance of the models in the test sets to determine the optimal model for each feature subset. Results The machine learning model constructed with the radiomic features of all the CT sequences and the fused model constructed with both clinical features + all the CT sequence radiomic features performed well (AUC = 0.91 and 0.97, respectively), whereas the performance of the machine learning model constructed with the clinical features alone was relatively poor (AUC = 0.73). In terms of model performance in identifying the two diseases, the accuracy of the fused model was better in identifying ICCs than in identifying IPTs. Conclusion A diagnostic model constructed from clinical and CT radiomic features quickly differentiated between IPT from ICC. The model may be helpful for the preoperative identification of IPTs and ICC.