Crohn’s disease: review and standardization of nomenclature

Magalhães, Fabrício Galdino; Lima, E.; Carpentieri-Primo, Pedro; Barreto, Míriam Menna; Rodrigues, Rosana Souza; Parente, Daniella Braz

doi:10.1590/0100-3984.2022.0082-en

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…Quantitative parameters are ultimately displayed as the arithmetic mean of the value obtained by two radiologists. When qualitative parameters were inconsistent, disagreements were resolved by consensus [ 25 ]. The consistency analysis between observers is shown in Supplementary Tables 1 and 2 .…”

Section: Methodsmentioning

confidence: 99%

Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn’s disease activity

Li,

Xie,

Tang

et al. 2024

Insights Imaging

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Objectives The simplified endoscopic score of Crohn’s disease (SES-CD) is the gold standard for quantitatively evaluating Crohn’s disease (CD) activity but is invasive. This study aimed to develop and validate a machine learning (ML) model based on dual-energy CT enterography (DECTE) to noninvasively evaluate CD activity. Methods We evaluated the activity in 202 bowel segments of 46 CD patients according to the SES-CD score and divided the segments randomly into training set and testing set at a ratio of 7:3. Least absolute shrinkage and selection operator (LASSO) was used for feature selection, and three models based on significant parameters were established based on logistic regression. Model performance was evaluated using receiver operating characteristic (ROC), calibration, and clinical decision curves. Results There were 110 active and 92 inactive bowel segments. In univariate analysis, the slope of spectral curve in the venous phases (λHU-V) has the best diagnostic performance, with an area under the ROC curve (AUC) of 0.81 and an optimal threshold of 1.975. In the testing set, the AUC of the three models established by the 7 variables to differentiate CD activity was 0.81–0.87 (DeLong test p value was 0.071–0.766, p > 0.05), and the combined model had the highest AUC of 0.87 (95% confidence interval (CI): 0.779–0.959). Conclusions The ML model based the DECTE can feasibly evaluate CD activity, and DECTE parameters provide a quantitative analysis basis for evaluating specific bowel activities in CD patients. Critical relevance statement The machine learning model based on dual-energy computed tomography enterography can be used for evaluating Crohn’s disease activity noninvasively and quantitatively. Key Points Dual-energy CT parameters are related to Crohn’s disease activity. Three machine learning models effectively evaluated Crohn’s disease activity. Combined models based on conventional and dual-energy CT have the best performance. Graphical Abstract

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Section: Methodsmentioning

confidence: 99%

Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn’s disease activity

Li,

Xie,

Tang

et al. 2024

Insights Imaging

View full text Add to dashboard Cite

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Computed Tomography Enterography-Based Radiomics Nomograms to Predict Inflammatory Activity for Ileocolonic Crohn’s Disease

Ma,

Zhu,

Cui

et al. 2024

Preprint

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ObjectivesTo develop and validate nomograms, derived from morphological features, and computed tomography enterography (CTE) -based radiomics, for evaluating inflammatory activity in patients with ileocolonic Crohn’s disease (CD). MethodsA total of 54 CD patients (237 bowel segments) with clinically confirmed CD were retrospectively reviewed. The simple endoscopic score for Crohn’s disease (SES-CD) was used as a reference standard to quantify the degree of mucosal inflammation and evaluate the disease severity. Radiomics and image morphological features were extracted in the training cohort, and then a morphological model (M-score) and a radiomics model (Rad-score) were built respectively. A combined nomogram was further generated by incorporating M-score and Rad-score. Predictive values of each model were assessed using receiver operating characteristic (ROC) curve analysis. Calibration curve and decision curve analysis (DCA) were employed to evaluate the accuracy and clinical applicability of the nomogram in the testing cohort. ResultsThe area under the ROC curve (AUC) of the nomogram, based on the stenosis, comb sign and Rad-score, was 0.834 [95% confidence interval (CI): 0.728–0.940] for distinguishing active from remissive disease. Moreover, the nomogram built using comb sign and Rad-score also achieved satisfied AUC [0.781 (95%CI: 0.611–0.951)] in distinguishing mild activity from moderate-to-severe activity. The calibration curve and DCA confirmed the accuracy and clinical utility of both nomograms. Conclusions Nomograms constructed by combining CTE-based radiomics and morphological features might be a useful supporting tool for grading inflammatory activity, aiding clinical decision-making for the management of CD.

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Crohn’s disease: review and standardization of nomenclature

Cited by 2 publications

References 18 publications

Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn’s disease activity

Establishing a machine learning model based on dual-energy CT enterography to evaluate Crohn’s disease activity

Computed Tomography Enterography-Based Radiomics Nomograms to Predict Inflammatory Activity for Ileocolonic Crohn’s Disease

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