Computed tomography-based radiomics combined with machine learning allows differentiation between primary intestinal lymphoma and Crohn's disease

Figure 1 Flowchart of radiomics and clinical-radiomics model construction to classify primary intestinal lymphoma and Crohn's disease. CD: Crohn's disease; PIL: Primary intestinal lymphoma; CT: Computed tomography; ROI: Region of interest; ROC: Receive operating curve; Lasso: Least absolute shrinkage and selection operator.

Figure 2 The nomogram of combined model. Gender: 1-Male; 2-Female; Enhanced pattern: 1-Homogeneous enhancement; 2-Inhomogeneous enhancement; 3-Hierarchical enhancement; 4-Mucosal enhancement. PIL: Primary intestinal lymphoma; OSV: Original shape voxel volume; LSGL: Log sigma 3-0-mm-3D glcm lmc2; WLLHGR: Wavelet LLH glrlm run percentage; WHLLFM: Wavelet HLL firstorder minimum; WHHHGL: Wavelet HHH glrlm long run high gray level emphasis; WLLLGR: Wavelet LLL glrlm run percentage; WLLLGA: Wavelet LLL glcm autocorrelation; N: Non-contrast enhanced; A: Arterial; V: Venous.

Figure 3 The receive operating curves. A: The receive operating curves of models based on the training set; B: The receive operating curves of models based on the internal validation set; C: The receive operating curves of models based on the external validation set. AUC: Area under the curve.

Figure 4 The confusion matrix. A: The performance of all phase radiomics model in the internal validation set; B: The performance of combined model in the internal validation set; C: The performance of all phase radiomics model in the external validation set; D: The performance of combined model in the external validation set. CD: Crohn's disease; PIL: Primary intestinal lymphoma.

Figure 5 The calibration curve. A: The calibration curve of combined model in the training set; B: The calibration curve of combined model in the internal validation set; C: The calibration curve of combined model in the external validation set.