In this study, we investigated whether radiomic features of CT image data can accurately predict HMGA2 and C-MYC gene expression status and identify the patient survival time using a machine learning approach in pancreatic ductal adenocarcinoma (PDAC). A cohort of 111 patients with PDAC was enrolled in our study. Radiomic features were extracted using conventional (shape and texture analysis) and deep learning approaches following to segmentation of preoperative CT data. To predict patient survival time, significant radiomic features were identified using a log-rank test. After surgical resection, level of HMGA2 and C-MYC gene expressions of PDAC tumor regions were classified using a support vector machines method. The model was evaluated in terms of accuracy, sensitivity, specificity, and area under the curve (AUC). Besides, inter-reader reliability analysis was used to demonstrate the robustness of the proposed features. The identified features consistently achieved good performance in survival prediction and classification of gene expression status, on images segmented by different radiologists. Using CT data from 111 patients, six features in the segmented region of images were highly correlated with survival time. Using extracted deep features of excised lesions from 47 patients, we observed an average AUC score of 0.90 with an accuracy of 95% in C-MYC prediction (sensitivity: 92% and specificity: 98%). In HGMA2 group, using shape features, the average AUC score was measured as 0.91 with an accuracy of 88% (sensitivity: 89% and specificity: 88%). In conclusion, the radiomic features of CT image can accurately predict the expression status of HMGA2 and C-MYC genes and identify the survival time of PDAC patients.
Keywords: Genomics; machine learning; pancreatic ductal adenocarcinoma; patient survival prediction; texture analysis.