T2-weighted imaging-based radiomic-clinical machine learning model for predicting the differentiation of colorectal adenocarcinoma

Figure 1 Workflow of the study. The workflow for constructing a machine learning model based on T2-weighted images to predict the differentiation degree of colorectal cancer patients included segmentation, feature extraction, feature selection, model construction and validation. ROI: Region of intrest; CV: Cross validation; MSE: Mean square error; ROC: Receiver operating characteristic; DCA: Decision curve analysis; GLCM: Grayscale co-occurrence matrix; GLRLM: Grayscale run length matrix; GLSZM: Grayscale size region matrix, NGTDM: Neighbourhood grayscale difference matrix; GLDM: Grayscale dependency matrix.