Abstract
BACKGROUND: We established and evaluated a radiomics nomogram based on multislice computed tomography (MSCT) arterial phase contrast-enhanced images to distinguish between Crohn's disease (CD) and ulcerative colitis (UC) objectively, quantitatively, and reproducibly. METHODS: MSCT arterial phase-enhancement images of 165 lesions (99 CD, 66 UC) in 87 patients with inflammatory bowel disease (IBD) confirmed by endoscopy or surgical pathology were retrospectively analyzed. A total of 132 lesions (80%) were selected as the training cohort and 33 lesions (20%) as the test cohort. A total of 1648 radiomic features were extracted from each region of interest (ROI), and the Pearson correlation coefficient and tree-based method were used for feature selection. Five machine learning classifiers, including logistic regression (LR), support vector machine (SVM), random forest (RF), stochastic gradient descent (SGD), and linear discriminative analysis (LDA), were trained. The best classifier was evaluated and obtained, and the results were transformed into the Rscore. Three clinical factors were screened out from 8 factors by univariate analysis. The logistic regression method was used to synthesize the significant clinical factors and the Rscore to generate the nomogram, which was compared with the clinical model and LR model. RESULTS: Among all machine learning classifiers, LR performed the best (AUC =0.8077, accuracy =0.697, sensitivity =0.8, specificity =0.5385), SGD model had the second best performance (AUC =0.8, accuracy =0.6667, sensitivity =0.75, specificity =0.5385), and the DeLong test results showed that there was no significant difference between LR and SGD (P=0.465>0.05), while the other models performed poorly. Texture features had the greatest impact on classification results among all imaging features. The significant features of the LR model were used to calculate the Rscore. The 3 significant clinical factors were perienteric edema or inflammation, CT value of arterial phase-enhancement (AP-CT value), and lesion location. Finally, a nomogram was constructed based on the 3 significant clinical factors and the Rscore, whose AUC (0.8846) was much higher than that of the clinical model (0.6154) and the LR model (0.8077). CONCLUSIONS: The nomogram is expected to provide a new auxiliary tool for radiologists to quickly identify CD and UC.