Multiparametric magnetic resonance imaging-derived radiomics for the prediction of Ki67 expression in intrahepatic cholangiocarcinoma.

BackgroundIntrahepatic cholangiocarcinoma (ICC) is an aggressive liver malignancy, and Ki67 is associated with prognosis in patients with ICC and is an attractive therapeutic target.PurposeTo predict Ki67 expression based on multiparametric magnetic resonance imaging (MRI) radiomics multiscale tumor region in patients with ICC.Material and MethodsA total of 191 patients (training cohort, n = 133; validation cohort, n = 58) with pathologically confirmed ICC were enrolled in this retrospective study. All patients underwent baseline abdominal MR scans in our institution. Univariate logistic analysis was conducted of the correlation between clinical and MRI characteristics and Ki67 expression. Radiomics features were extracted from the image of six MRI sequences (T1-weighted imaging, fat-suppression T2-weighted imaging, diffusion-weighted imaging, and 3-phases contrast-enhanced T1-weighted imaging sequences). Using the least absolute shrinkage and selection operator (LASSO) to select Ki67-related radiomics features in four different tumor volumes (VOItumor, VOI+8mm, VOI+10mm, VOI+12mm). The Rad-score was calculated with logistic regression, and models for prediction of Ki67 expression were constructed. The receiver operating curve was used to analyze the predictive performance of each model.ResultsClinical and regular MRI characteristics were independent of Ki67 expression. Four Rad-scores all showed favorable prediction efficiency in both the training and validation cohorts (AUC = 0.849-0.912 vs. 0.789-0.838). DeLong's test showed that there was no significant difference between the AUC of the radiomics scores, while the Rad-score (VOI+10mm) performed the most stable predictive efficiency with △AUC 0.033.ConclusionMultiparametric MRI radiomics based on multiscale tumor regions can help predict the expression status of Ki67 in ICC patients.