A Novel Radiomics Model Integrating Luminal and Mesenteric Features to Predict Mucosal Activity and Surgery Risk in Crohn's Disease Patients: A Multicenter Study.

BACKGROUND: To investigate the feasibility of integrating radiomics and morphological features based on computed tomography enterography (CTE) for developing a noninvasive grading model for mucosal activity and surgery risk of Crohn's disease (CD) patients. METHODS: A total of 167 patients from three centers were enrolled. Radiomics and image morphological features were extracted to quantify segmental and global simple endoscopic score for Crohn's disease (SES-CD). An image-fusion-based support vector machine (SVM) classifier was used for grading SES-CD and identifying moderate-to-severe SES-CD. The performance of the predictive model was assessed using the area under the receiver operating characteristic curve (AUC). A multiparametric model was developed to predict surgical progression in CD patients by combining sum-image scores and clinical data. RESULTS: The AUC values of the multicategorical segmental SES-CD fusion radiomic model based on a combination of luminal and mesenteric radiomics were 0.828 and 0.709 in training and validation cohorts. The image fusion model integrating the fusion radiomics and morphological features could accurately distinguish bowel segments with moderate-to-severe SES-CD in both the training cohort (AUC = 0.847, 95% confidence interval (CI): 0.784-0.902) and the validation cohort (AUC = 0.896, 95% CI: 0.812-0.960). A predictive nomogram for interval surgery was developed based on multivariable cox analysis. CONCLUSIONS: This study demonstrated the feasibility of integrating lumen and mesentery radiomic features to develop a promising noninvasive grading model for mucosal activity of CD. In combination with clinical data, the fusion-image score may yield an accurate prognostic model for time to surgery.

Two decomposition algorithms of dual-energy cone beam CT and their dependence on the phantom sizes / 中华放射医学与防护杂志

Objective:To analyze the effects of two decomposition algorithms of dual-energy cone beam CT (DECBCT) (direct decomposition and iterative decomposition) on the image quality and material decomposition accuracy of different sizes of phantoms.Methods:Different sizes of imaging parts of patients were simulated using the combination of CatPhan604 phantoms and customized annuluses. CBCT with high energy of 140 kVp and low energy of 100 kVp were acquired using the Varian Edge CBCT system. Then the material decomposition of DECBCT images was performed using the two algorithms. The electron density (ED) and contrast-to-noise ratio (CNR) of each material in the CTP682 module were calculated. They were used to assess the decomposition accuracy and image quality of the two algorithms.Results:Based on the values in the Catphan604 manual, both algorithms have high ED accuracy. Only the ED accuracy of four materials of the smallest sized phantom showed statistical difference ( z = -4.21, 4.30, 2.87, 5.45, P < 0.05), but the average relative error was less than 1%. The CNR of the iterative decomposition algorithm was significantly higher than that of the direct decomposition, increasing by 51.8%-703.47%. The increase in the phantom size significantly reduced the accuracy of ED, and the increased amplitude of the relative error was up to a maximum of 2.52%. The large phantom size also reduced the image quality of iterative decomposition, and the decreased amplitude of CNR was up to a maximum of 39.71. Conclusions:Compared with the direct decomposition, the iterative decomposition algorithm can significantly reduce the image noise and improve the contrast without losing the accuracy of electron density in the DECBCT construction of different sizes of phantoms.