Radiomic mapping model for prediction of Ki-67 expression in adrenocortical carcinoma

AIM: To determine the value of contrast-enhanced computed tomography (CT)-derived radiomic features in the preoperative prediction of Ki-67 expression in adrenocortical carcinoma (ACC) and to detect significant associations between radiomic features and Ki-67 expression in ACC. MATERIALS AND METHODS: For this retrospective analysis, patients with histopathologically proven ACC were reviewed. Radiomic features were extracted for all patients from the preoperative contrast-enhanced abdominal CT images. Statistical analysis identified the radiomic features predicting the Ki-67 index in ACC and analysed the correlation with the Ki-67 index. RESULTS: Fifty-three cases of ACC that met eligibility criteria were identified and analysed. Of the radiomic features analysed, 10 showed statistically significant differences between the high and low Ki-67 expression subgroups. Multivariate linear regression analysis yielded a predictive model showing a significant association between radiomic signature and Ki-67 expression status in ACC (R²=0.67, adjusted R²=0.462, p=0.002). Further analysis of the independent predictors showed statistically significant correlation between Ki-67 expression and shape flatness, elongation, and grey-level long run emphasis (p=0.002, 0.01, and 0.04, respectively). The area under the curve for identification of high Ki-67 expression status was 0.78 for shape flatness and 0.7 for shape elongation. CONCLUSION: Radiomic features derived from preoperative contrast-enhanced CT images show encouraging results in the prediction of the Ki-67 index in patients with ACC. Morphological features, such as shape flatness and elongation, were superior to other radiomic features in the detection of high Ki-67 expression.