Radiomics Analysis of Iodine-Based Material Decomposition Images With Dual-Energy Computed Tomography Imaging for Preoperatively Predicting Microsatellite Instability Status in Colorectal Cancer

Purpose: To investigate the value of radiomics analysis of iodine-based material decomposition (MD) images with dual energy computed tomography (DECT) imaging for preoperatively predicting microsatellite instability (MSI) status in colorectal cancer (CRC). Methods: This study included 102 CRC patients proved by postoperative pathology, and their MSI status was confirmed by immunohistochemistry staining. All patients underwent preoperative DECT imaging scanned on either a Revolution CT or Discovery CT 750HD scanner, and the iodine-based MD images in the venous phase were reconstructed. The clinical, CT-reported and radiomics features were obtained and analyzed. Data from the Revolution CT scanner were used to establish a radiomics model to predict MSI status (70% samples were randomly selected as the training set, and the remaining samples were used to validate); and data from the Discovery CT 750HD scanner were used to test the radiomics model. The stable radiomics features with both inter-user and intra-user stability were selected for next analysis. The feature dimension reduction was performed by using Student’s t test or Mann-Whitney U test, Spearman’s rank correlation test, Min-Max standardization, one-hot encoding, and least absolute shrinkage and selection operator selection method. The multi-parameter logistic regression model was established to predict MSI status. The model performances were evaluated: the discrimination performance was accessed by receiver operating characteristic curve (ROC) analysis; the calibration performance was tested by calibration curve accompanied by Hosmer-Lemeshow test; the clinical utility was assessed by decision curve analysis. Results: Nine top-ranked features were finally selected to construct the radiomics model. In the training set, the area under ROC curve (AUC) was 0.961 (accuracy: 0.875; sensitivity: 1.000; specificity: 0.812); in the validation set, the AUC was 0.918 (accuracy: 0.875; sensitivity: 0.875; specificity: 0.857). In the testing set, the diagnostic performance was slightly lower with AUC of 0.875 (accuracy: 0.788; sensitivity: 0.909; specificity: 0.727). Nomogram based on clinical factors and radiomics score was generated via the proposed logistic regression model. Good calibration and clinical utility were observed using the calibration and decision curve analysis, respectively. Conclusion: Radiomics analysis of iodine-based MD images with DECT imaging holds great potential to predict MSI status in CRC patients.

Abstract licence: CC BY

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