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.
Key words
Abbreviations:
AIC (akaike information criterion), ALB (albumin level), AUC (area under the receiver operating characteristic curve), CD (Crohn's disease), CI (confidence interval), CIR (cecal intubation rate), CRP (C-reactive protein), CTE (computed tomography enterography), Hb (hemoglobin), HR (hazard ratio), IBD (inflammatory bowel disease), ICC (intraclass correlation coefficients), LASSO (least absolute shrinkage and selection operator), LR (logistic regression), MH (mucosal healing), PLT (platelet count), RF (random forest), ROC (receiver operating characteristic), SES-CD (simple endoscopic score for Crohn's disease), SFFS (sequential forward floating selection), SFS (Surgery-free survival), SVM (support vector machine), TH (transmural healing: VOIs, volumes of interest), WBC (white blood cell), XGBoost (eXtreme Gradient Boosting)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: May 04, 2023
Accepted:
March 18,
2023
Received in revised form:
March 16,
2023
Received:
February 19,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
