TCGA-TCIA-Based CT Radiomics Study for Noninvasively Predicting Epstein-Barr Virus Status in Gastric Cancer.

Abstract

OBJECTIVE. The purpose of this study was to investigate the value of TCGA-TCIA (The Cancer Genome Atlas and The Cancer Imaging Archive)-based CT radiomics for noninvasive prediction of Epstein-Barr virus (EBV) status in gastric cancer (GC). MATERIALS AND METHODS. A total of 133 patients with pathologically confirmed GC (94 in the training cohort and 39 in the validation cohort) who were identified from the TCGA-TCIA public data repository and two hospitals were retrospectively enrolled in the study. Two-dimensional and 3D radiomics features were extracted to construct corresponding radiomics signatures. Then, 2D and 3D nomograms were built by combining radiomics signatures and clinical information on the basis of multivariable analysis. Their performance and clinical practicability were determined, validated, and compared with respect to discrimination, calibration, reclassification, and time spent on tumor segmentation. RESULTS. Both 2D and 3D nomograms were robust and showed good calibration. The AUCs of the 2D and 3D nomograms showed no significant difference in the training cohort (0.919 vs 0.945, respectively; p = .41) or validation cohort (0.939 vs 0.955, respectively; p = .71). The net reclassification index showed that the 3D nomogram revealed no significant improvement in risk reclassification when compared with the 2D nomogram in the training cohort (net reclassification index, 0.68%; p = .14) and the validation cohort (net reclassification index, 6.06%; p = .08). Of note, the time spent on 3D segmentation (median, 907 seconds) was higher than that spent on 2D segmentation (median, 129 seconds). CONCLUSION. The 2D and 3D radiomics nomograms might have the potential to be used as effective tools for prediction of EBV in GC. When time spent on segmentation is considered, the 2D nomogram is more highly recommended for clinical application.