Computed tomography enterography for crohn's disease: correlation between the imaging findings and histopathologic scoring system

Multimodality imaging features of small bowel cancers complicating Crohn's disease: a pictorial review

Patients with Crohn's disease (CD) are at increased risk of developing small bowel cancer, since chronic inflammation may trigger the histopathological sequence that begins from low-grade dysplasia of the intestinal epithelium and may eventually lead to malignant transformation. Owing to their location in a portion of the gastrointestinal tract which is not easily accessible to conventional endoscopic techniques, the detection of CD-related small bowel cancers is still a clinical challenge. The radiological features of CD-related small bowel adenocarcinoma (SBA) in patients with CD have been described in some previous studies, including its appearance in both CT and MRI examinations. Radiological signs of active or fibrostenotic CD may be intermixed with those suggesting the presence of CD-related SBA. In CT studies, the most relevant findings consistent with malignant transformation are the presence of a stricture with irregular asymmetric thickening of small bowel walls, loss of mural stratification, and moderate enhancement after intravenous administration of iodinated contrast media, in association with enlarged adjacent mesenteric lymph nodes. Many of the CD-related SBA features that can be observed on CT imaging are similar to those detectable by MRI. This latter modality provides the additional value of the functional characterization of small bowel strictures, thereby helping to distinguish between inflammatory, fibrotic, and malignant stenosis in the setting of active CD. Positron Emission Tomography (PET)/CT enables the metabolic assessment of enlarged mesenteric lymph nodes, and PET/MRI fusion imaging can incorporate morphological, functional and metabolic information into a single set of imaging data, thus overcoming the limitations of the separate assessment of each individual modality. Owing to the low incidence and prevalence of this long-term complication of CD, we believe that a detailed multimodality pictorial essay on this topic, also including the PET-CT and fusion imaging documentation of some cases, would be useful to the medical literature.

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

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.

Diagnostic prediction of gastrointestinal graft-versus-host disease based on a clinical- CT- signs nomogram model

OBJECTIVE

Gastrointestinal graft-versus-host disease (GI-GVHD) is one of the complications that can easily occur after hematopoietic stem cell transplantation (HSCT). Timely diagnosis and treatment are pivotal factors that greatly influence the prognosis of patients. However, the current diagnostic method lacks adequate non-invasive diagnostic tools.

METHODS

A total of 190 patients who suspected GI-GVHD were retrospectively included and divided into training set (n = 114) and testing set (n = 76) according to their discharge time. Least absolute shrinkage and selection operator (LASSO) regression was used to screen for clinically independent predictors. Based on the logistic regression results, both computed tomography (CT) signs and clinically independent predictors were integrated in order to build the nomogram, while the testing set was verified independently. The receiver operating characteristic (ROC), area under the curve (AUC), decision curve, and clinical impact curve were used to measure the accuracy of prediction, clinical net benefit, and consistency of diagnostic factors.

RESULTS

Four key factors, including II-IV acute graft-versus-host disease (aGVHD), the circular target sign, multifocal intestinal inflammation, and an increased in total bilirubin, were identified. The combined model, which was constructed from CT signs and clinical factors, showed higher predictive performances. The AUC, sensitivity, and specificity of the training set were 0.867, 0.787, and 0.811, respectively. Decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) showed that the developed model exhibited a better prediction accuracy than the others.

CONCLUSIONS

This combined model facilitates timely diagnosis and treatment and subsequently improves survival and overall outcomes in patients with GI-GVHD.

CRITICAL RELEVANCE STATEMENT

GI-GVHD is one of the complications that can easily occur after HSCT. However, the current diagnostic approach lacks adequate non-invasive diagnostic methods. This non-invasive combined model facilitates timely treatment and subsequently improves patients with GI-GVHD survival and overall outcomes.

KEY POINTS

• There is currently lacking of non-invasive diagnostic methods for GI-GVHD. • Four clinical CT signs are the independent predictors for GI-GVHD. • Association between the CT signs with clinical factors may improve the diagnostic performance of GI-GVHD.

Intestinal Ultrasound Combined with Blood Inflammatory Markers Is a More Efficient Tool in Evaluating Severity of Crohn's Disease: A Pilot Study

Background and Aims

Intestinal ultrasound (IUS) is considered a nonirradiating, noninvasive, well-tolerated, and valuable tool for objectively assessing Crohn's disease (CD) activity. However, there is no widely accepted intestinal ultrasound scoring system. This study is aimed at evaluating the efficacy of IUS key parameters, the International Bowel Ultrasound Activity Score (IBUS-SAS), and IBUS-SAS combined with blood inflammatory markers in assessing CD activity.

Methods

40 CD patients were reviewed in this retrospective study and were divided into the moderate-severe group (n = 25) and nonmoderate-severe group (n = 15) based on a simplified endoscopic score of Crohn's disease (SES-CD). Double-balloon enteroscopy/colonoscopy were reviewed by three gastroenterologists. A transabdominal ultrasound was performed by two ultrasound specialists. Blood inflammatory markers were measured from morning samples.

Results

In evaluating moderate to severe CD patients, (1) IBUS-SAS had a good predictive effect with an area-under-the-curve (AUC) of 0.855 (P < 0.001); (2) IUS key parameters (including BWT, CDS, BWS, and I-fat) yielded good predictive effects with AUC of 0.811, 0.731, 0.724, and 0.747, respectively (P < 0.001); (3) blood inflammatory markers (including ESR, CRP, PLR, MLR, and NLR) also had good predictive effects with AUC of 0.771, 0.837, 0.728, 0.743, and 0.775, respectively (P < 0.001); (4) IBUS-SAS combined with ESR and CRP exerted the best predictive effect with the highest AUC of 0.912 (95% CI: 0.823-1.000), and the sensitivity and specificity were 88.0% and 80.0%, respectively (P < 0.001).

Conclusion

IBUS-SAS combined with ESR and CRP is a more efficient tool than IBUS-SAS alone or inflammatory markers alone in evaluating CD patients with moderate to severe disease activity.

Retrospective Cohort Study of Shear-Wave Elastography and Computed Tomography Enterography in Crohn's Disease

Distinguishing between inflammatory and fibrotic lesions drastically influences treatment decision-making regarding Crohn's disease. However, it is challenging to distinguish these two phenotypes before surgery. This study investigates the diagnostic yield of shear-wave elastography and computed tomography enterography to distinguish intestinal phenotypes in Crohn's disease. Thirty-seven patients (mean age, 29.51 ± 11.52; 31 men) were evaluated with average value of shear-wave elastography (Emean) and computed tomography enterography (CTE) scores. The results demonstrated that a positive correlation between the Emean and fibrosis (Spearman's r = 0.653, p = 0.000). The cut-off value for fibrotic lesions was 21.30 KPa (AUC: 0.877, sensitivity: 88.90%, specificity: 89.50%, 95% CI:0.755~0.999, p = 0.000). The CTE score showed a positive correlation with inflammation (Spearman's r = 0.479, p = 0.003), and a 4.5-point grading system was the optimal cut-off value for inflammatory lesions (AUC: 0.766, sensitivity: 73.70%, specificity: 77.80%, 95% CI: 0.596~0.936, p = 0.006). Combining these two metrics improved the diagnostic performance and specificity (AUC: 0.918, specificity: 94.70%, 95% CI: 0.806~1.000, p = 0.000). In conclusion, shear-wave elastography can be used to help detect fibrotic lesions and the computed tomography enterography score emerged as a feasible predictor of inflammatory lesions. The combination of these two imaging techniques is proposed to distinguish intestinal predominant phenotypes.

[68Ga]Ga-FAPI-04 PET/CT on assessing Crohn's disease intestinal lesions

BACKGROUND

Fibrosis and inflammation are major pathological changes of Crohn's disease (CD). Early detection and accurate severity evaluation of CD are critical for patient's prognosis. Endoscopy is widely used to evaluate CD progression. Herein, we evaluated the efficacy of [⁶⁸Ga]Ga-FAPI-04 PET/CT to identify lesions and assess the progression of CD.

METHODS

All CD patients received computed tomography enterography (CTE), [⁶⁸Ga]Ga-FAPI-04 PET/CT examination, and ileocolonoscopy within 1 week. Two independent gastroenterologists computed the Crohn's disease activity index (CDAI) of all patients. Two radiology physicians assessed the CTE images separately, and the CTE scores were calculated. Lastly, two nuclear medicine physicians independently examined the [⁶⁸Ga]Ga-FAPI-04 PET/CT images. Once the FAPI uptake of the intestinal segment was equal or higher relative to the liver (considered FAPI-positive), the target-to-background ratio (TBR) and global FAPI PET/CT score were computed, representing the independent intestinal activity and activity of all intestinal segments, respectively. Levels of fecal calprotectin (FCP) and C-reactive protein (CRP) were determined before the endoscopy. The Crohn's disease endoscopy index of severity (CDEIS) and the simple endoscopic score for Crohn's disease (SES-CD) were calculated during the endoscopy. Finally, all data were obtained and analyzed.

RESULTS

There were 74 intestinal segments in 16 patients were assessed. [⁶⁸Ga]Ga-FAPI-04 PET/CT identified 42 of 45 endoscopically lesioned segments (endoscopic lesions detection sensitivity: 93.3%), while CTE identified 39 of them (endoscopic lesions detection sensitivity: 86.7%). According to the receiver operating characteristic (ROC) analysis, [⁶⁸Ga]Ga-FAPI-04 PET/CT showed better performance in the detection of endoscopic lesions compared with CTE (P < 0.05). The TBR was significantly associated with the CTE score (r = 0.81; (95% CI): 0.736-0.869; P < 0.0001) and SES-CD values (r = 0.86; (95% CI): 0.776-0.908; P < 0.0001). In addition, the global FAPI PET/CT score was significantly correlated with FCP (r = 0.52; 95% CI, 0.02-0.81; P = 0.039), CRP (r = 0.60; 95% CI, 0.13-0.85; P = 0.014), CDEIS (r = 0.55; 95% CI, 0.06-0.83; P = 0.028), and CDAI (r = 0.81; 95% CI, 0.50-0.93; P < 0.0001).

CONCLUSION

In summary, [⁶⁸Ga]Ga-FAPI-04 PET/CT correlated well with endoscopic, CTE, clinical, and biomarkers of CD. It was also highly sensitive in the detection of different classes of lesions in all intestinal segments, and unlike other examinations, this technique required no patient fasting or bowel preparation. Therefore, [⁶⁸Ga]Ga-FAPI-04 PET/CT may be a promising method for assessing the activity of CD.

Prediction of the activity of Crohn's disease based on CT radiomics combined with machine learning models

PURPOSE

To investigate the value of a CT-based radiomics model in identification of Crohn's disease (CD) active phase and remission phase.

METHODS

CT images of 101 patients diagnosed with CD were retrospectively collected, which included 60 patients in active phase and 41 patients in remission phase. These patients were randomly divided into training group and test group at a ratio of 7 : 3. First, the lesion areas were manually delineated by the physician. Meanwhile, radiomics features were extracted from each lesion. Next, the features were selected by t-test and the least absolute shrinkage and selection operator regression algorithm. Then, several machine learning models including random forest (RF), extreme gradient boosting (XGBoost), support vector machine (SVM), logistic regression (LR) and K-nearest neighbor (KNN) algorithms were used to construct CD activity classification models respectively. Finally, the soft-voting mechanism was used to integrate algorithms with better effects to perform two classifications of data, and the receiver operating characteristic curves were applied to evaluate the diagnostic value of the models.

RESULTS

Both on the training set and the test set, AUC of the five machine learning classification models reached 0.85 or more. The ensemble soft-voting classifier obtained by using the combination of SVM, LR and KNN could better distinguish active CD from CD remission. For the test set, AUC was 0.938, and accuracy, sensitivity, and specificity were 0.903, 0.911, and 0.892, respectively.

CONCLUSION

This study demonstrated that the established radiomics model could objectively and effectively diagnose CD activity. The integrated approach has better diagnostic performance.