Data Sheet 1_A combined radiomics and clinical model for preoperative differentiation of intrahepatic cholangiocarcinoma and intrahepatic bile duct stones with cholangitis: a machine learning approach.docx

BackgroundThis study aimed to develop and validate a predictive model integrating radiomics features and clinical variables to differentiate intrahepatic bile duct stones with cholangitis (IBDS-IL) from intrahepatic cholangiocarcinoma (ICC) preoperatively, as accurate distinction is crucial for determining appropriate treatment strategies.
MethodsA total of 169 patients (97 IBDS-IL and 72 ICC) who underwent surgical resection were retrospectively analyzed. Radiomics features were extracted from ultrasound images, and clinical variables with significant differences between groups were identified. Feature selection was performed using LASSO regression and recursive feature elimination (RFE). The radiomics model, clinical model, and combined model were constructed and evaluated using the area under the curve (AUC), calibration curves, decision curve analysis (DCA), and SHAP analysis.
ResultsThe radiomics model achieved an AUC of 0.962, and the clinical model achieved an AUC of 0.861. The combined model, integrating the Radiomics Score with clinical variables, demonstrated the highest predictive performance with an AUC of 0.988, significantly outperforming the clinical model (p < 0.05). Calibration curves showed excellent agreement between predicted and observed outcomes, and the Hosmer-Lemeshow test confirmed a good model fit (p = 0.998). DCA revealed that the combined model provided the greatest clinical benefit across a wide range of threshold probabilities. SHAP analysis identified the Radiomics Score as the most significant contributor, complemented by abdominal pain and liver atrophy.
ConclusionThe combined model integrating radiomics features and clinical data offers a powerful and reliable tool for preoperative differentiation of IBDS-IL and ICC. Its superior performance and clinical interpretability highlight its potential for improving diagnostic accuracy and guiding clinical decision-making. Further validation in larger, multicenter datasets is warranted to confirm its generalizability.