Image processing was applied before feature extraction, including image resampling to 1 × 1 × 1 mm3 voxel size using linear interpolation and image gray normalization to uniform grayscale of 0‐255. A total of 396 texture features were extracted using AK software (Analysis Kit; GE Healthcare). The feature set included histogram features, GLCM features, gray level run‐length matrix features, form factor features, and gray level size‐zone matrix features. These features could characterize intratumor heterogeneity.

To eliminate the differences in the value scales of extraction features, feature normalization was carried out before feature selection, and each feature for all patients was normalized with Z‐scores, subtracting the mean value and dividing by SD. Minimum redundancy maximum relevance was used to eliminate the redundant and irrelevant features. The top five features 14 among 396 texture features with the greatest correlation with the MGMT methylation status were screened in. The Radscore of each sequence was then calculated for each patient using a linear combination of the five selected features weighted by their respective coefficients. The data texture analysis flow is shown in Figure 2.

Workflow of texture analysis to detect methylguanine methyltransferase (MGMT) methylation in glioma. CE, contrast‐enhanced; DCA, decision curve analysis; FLAIR, fluid attenuated inversion recovery; GLCM, gray level co‐occurrence matrix; GLZSM, gray level size‐zone matrix; RLM, gray level run‐length matrix; Radscore, radiomic signature; ROC, receiver operating characteristic; VOI, volume of interest