Detecting and characterizing creeping fat in Crohn's disease: agreement between intestinal ultrasound and computed tomography enterography.

OBJECTIVES: Creeping fat (CF) is associated with stricture formation in Crohn's disease (CD). This study evaluated the feasibility of intestinal ultrasound (IUS) for semiquantitative analysis of CF and compared the agreement between IUS and computed tomography enterography (CTE). METHODS: In this retrospective study, we recruited consecutive CD patients who underwent IUS and CTE. CF wrapping angle was analyzed on the most affected bowel segment and was independently evaluated by IUS and CTE. We evaluated the wrapping angle of CF in the cross- and vertical sections of the diseased bowel. CF wrapping angle was divided into < 180° and ≥ 180°. IUS performance was assessed using CTE as a reference standard, and IUS interobserver consistency was evaluated. RESULTS: We enrolled 96 patients. CTE showed that CF wrapping angle was < 180° in 35 patients and ≥ 180° in 61 patients. We excluded three cases in which the observation positions were inconsistent between the IUS and CTE. Excellent agreement was shown between US and CTE (82/93, 88.2%). The eleven remaining cases showed inconsistencies mostly in the terminal ileum (n = 5) and small intestine (n = 4). Total agreement between IUS observers was 89.6% (86/96, κ = 0.839, p = 0.000), with perfect agreement for the ileocecal and colonic segments (35/37, 94.6% and 20/21, 95.2%, respectively) and moderate agreement for small intestinal segments (16/21, 76.2%). CONCLUSIONS: IUS could be of value and complementary to CTE for assessing CF, particularly in patients with affected terminal ileum and colon. IUS is a non-invasive technique for monitoring CD patients. CRITICAL RELEVANCE STATEMENT: In our study, excellent agreement was shown between intestinal US observers as well as between US and CT enterography (CTE) for assessing creeping fat (CF), which showed that ultrasound could be of value and complementary to CTE. KEY POINTS: Creeping fat (CF) is a potential therapeutic target in Crohn's disease. Excellent agreement was shown between US and CT Enterography (CTE) for assessing CF. Ultrasound could be complementary to CTE for assessing CF.

Prevalence of stricturing, penetrating complications and extraintestinal manifestations in inflammatory bowel disease detected on cross-sectional imaging in a tertiary care setting.

BACKGROUND: Stricturing, penetrating complications and extraintestinal manifestations (EIMs) are frequent in patients with inflammatory bowel disease (IBD). There is limited data on the prevalence of these complications in patients with IBD. Therefore, we aimed to assess the burden of these complications detected incidentally on cross-sectional imaging. METHODS: A retrospective study conducted at two tertiary care centers in London, Ontario. Patients (≥18 years) with a confirmed diagnosis of IBD who underwent CT enterography (CTE) or MR enterography (MRE) between 1 Jan 2010 and 31 Dec 2018 were included. Categorical variables were reported as proportions and the mean and standard deviations were reported for continuous variables. RESULTS: A total of 615 imaging tests (MRE: 67.3% [414/615]) were performed in 557 IBD patients (CD: 91.4% [509/557], UC: 8.6% [48/557]). 38.2% (213/557) of patients were male, with mean age of 45.6 years (±15.8), and median disease duration of 11.0 years (±12.5). Among patients with CD, 33.2% (169/509) had strictures, with 7.8% having two or more strictures and 66.3% considered inflammatory. A fistula was reported in 10.6% (54/509), the most common being perianal fistula (27.8% [15/54]), followed by enterocutaneous fistula (16.8% [9/54]), and enteroenteric fistula (16.8% [9/54]). Additionally, 7.4% (41/557) of patients with IBD were found to have an EIM on cross-sectional imaging, with the most prevalent EIM being cholelithiasis (63.4% [26/41]), followed by sacroiliitis (24.4% [10/41]), primary sclerosing cholangitis (4.8% [2/41]) and nephrolithiasis (4.8% [2/41]). CONCLUSIONS: Approximately 40% of patients with CD undergoing cross-sectional imaging had evidence of a stricture or fistulizing disease, with 7% of patients with IBD having a detectable EIM. These results highlight the burden of disease and the need for specific therapies for these disease phenotypes.

Improving diagnostic confidence in low-dose dual-energy CTE with low energy level and deep learning reconstruction.

OBJECTIVE: To demonstrate the value of using 50 keV virtual monochromatic images with deep learning image reconstruction (DLIR) in low-dose dual-energy CT enterography (CTE). METHODS: In this prospective study, 114 participants (62 % M; 41.9 ± 16 years) underwent dual-energy CTE. The early-enteric phase was performed using standard-dose (noise index (NI): 8) and images were reconstructed at 70 keV and 50 keV with 40 % strength ASIR-V (ASIR-V40%). The late-enteric phase used low-dose (NI: 12) and images were reconstructed at 50 keV with ASIR-V40%, and DLIR at medium (DLIR-M) and high strength (DLIR-H). Image standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), edge-rise-slope (ERS) were computed. The quantitative comb sign score was calculated for the 27 patients with Crohn's disease. The subjective noise, image contrast, display of rectus artery were scored using a 5-point scale by two radiologists blindly. RESULTS: Effective dose was reduced by 50 % (P < 0.001) in the late-enteric phase to 3.26 mSv. The lower-dose 50 keV-DLIR-H images (SD:17.7 ± 0.5HU) had similar image noise (P = 0.97) as the standard-dose 70 keV-ASIR-V40% images (SD:17.7 ± 0.73HU), but with higher (P < 0.001) SNR, CNR, ERS and quantitative comb sign score (5.7 ± 0.17, 1.8 ± 0.12, 156.04 ± 5.21 and 5.05 ± 0.73, respectively). Furthermore, the lower-dose 50 keV-DLIR-H images obtained the highest score in the rectus artery visibility (4.27 ± 0.6). CONCLUSIONS: The 50 keV images in dual-energy CTE with DLIR provides high-quality images, with a 50 % reduction in radiation dose. Images with high contrast and density resolutions significantly enhance the diagnostic confidence of Crohn's disease and are essential for the clinical development of individualized treatment plans.

CT-based pancreatic radiomics predicts secondary loss of response to infliximab in biologically naïve patients with Crohn's disease.

OBJECTIVES: Predicting secondary loss of response (SLR) to infliximab (IFX) is paramount for tailoring personalized management regimens. Concurrent pancreatic manifestations in patients with Crohn's disease (CD) may correlate with SLR to anti-tumor necrosis factor treatment. This work aimed to evaluate the potential of pancreatic radiomics to predict SLR to IFX in biologic-naive individuals with CD. METHODS: Three models were developed by logistic regression analyses to identify high-risk subgroup prone to SLR. The area under the curve (AUC), calibration curve, decision curve analysis (DCA), and integrated discrimination improvement (IDI) were applied for the verification of model performance. A quantitative nomogram was proposed based on the optimal prediction model, and its reliability was substantiated by 10-fold cross-validation. RESULTS: In total, 184 CD patients were enrolled in the period January 2016 to February 2022. The clinical model incorporated age of onset, disease duration, disease location, and disease behavior, whereas the radiomics model consisted of five texture features. These clinical parameters and the radiomics score calculated by selected texture features were applied to build the combined model. Compared to other two models, combined model achieved favorable, significantly improved discrimination power (AUCcombined vs clinical 0.851 vs 0.694, p = 0.02; AUCcombined vs radiomics 0.851 vs 0.740, p = 0.04) and superior clinical usefulness, which was further converted into reliable nomogram with an accuracy of 0.860 and AUC of 0.872. CONCLUSIONS: The first proposed pancreatic-related nomogram represents a credible, noninvasive predictive instrument to assist clinicians in accurately identifying SLR and non-SLR in CD patients. CRITICAL RELEVANCE STATEMENT: This study first built a visual nomogram incorporating pancreatic texture features and clinical factors, which could facilitate clinicians to make personalized treatment decisions and optimize cost-effectiveness ratio for patients with CD. KEY POINTS: • The first proposed pancreatic-related model predicts secondary loss of response for infliximab in Crohn's disease. • The model achieved satisfactory predictive accuracy, calibration ability, and clinical value. • The model-based nomogram has the potential to identify long-term failure in advance and tailor personalized management regimens.

Improved overall image quality in low-dose dual-energy computed tomography enterography using deep-learning image reconstruction.

OBJECTIVE: To demonstrate the clinical advantages of a deep-learning image reconstruction (DLIR) in low-dose dual-energy computed tomography enterography (DECTE) by comparing images with standard-dose adaptive iterative reconstruction-Veo (ASIR-V) images. METHODS: In this Institutional review board approved prospective study, 86 participants who underwent DECTE were enrolled. The early-enteric phase scan was performed using standard-dose (noise index: 8) and images were reconstructed at 5 mm and 1.25 mm slice thickness with ASIR-V at a level of 40% (ASIR-V40%). The late-enteric phase scan used low-dose (noise index: 12) and images were reconstructed at 1.25 mm slice thickness with ASIR-V40%, and DLIR at medium (DLIR-M) and high (DLIR-H). The 70 keV monochromatic images were used for image comparison and analysis. For objective assessment, image noise, artifact index, SNR and CNR were measured. For subjective assessment, subjective noise, image contrast, bowel wall sharpness, mesenteric vessel clarity, and small structure visibility were scored by two radiologists blindly. Radiation dose was compared between the early- and late-enteric phases. RESULTS: Radiation dose was reduced by 50% in the late-enteric phase [(6.31 ± 1.67) mSv] compared with the early-enteric phase [(3.01 ± 1.09) mSv]. For the 1.25 mm images, DLIR-M and DLIR-H significantly improved both objective and subjective image quality compared to those with ASIR-V40%. The low-dose 1.25 mm DLIR-H images had similar image noise, SNR, CNR values as the standard-dose 5 mm ASIR-V40% images, but significantly higher scores in image contrast [5(5-5), P < 0.05], bowel wall sharpness [5(5-5), P < 0.05], mesenteric vessel clarity [5(5-5), P < 0.05] and small structure visibility [5(5-5), P < 0.05]. CONCLUSIONS: DLIR significantly reduces image noise at the same slice thickness, but significantly improves spatial resolution and lesion conspicuity with thinner slice thickness in DECTE, compared to conventional ASIR-V40% 5 mm images, all while providing 50% radiation dose reduction.

Structured Reporting of Computed Tomography Enterography in Crohn's Disease.

BACKGROUND: To compare the integrity, clarity, conciseness, etc., of the structured report (SR) versus free-text report (FTR) for computed tomography enterography of Crohn's disease (CD). METHODS: FTRs and SRs were generated for 30 patients with CD. The integrity, clarity, conciseness etc., of SRs versus FTRs, were compared. In this study, an evidence-based medicine practice model was utilized on 92 CD patients based on SR in order to evaluate its clinical value. Then, the life quality of the patients in two groups was evaluated before and after three months of intervention using an Inflammatory Bowel Disease Questionnaire (IBDQ). RESULTS: SRs received higher ratings for satisfaction with integrity (median rating 4.27 vs. 3.75, P=0.008), clarity (median rating 4.20 vs. 3.43, P=0.003), conciseness (median rating 4.23 vs. 3.20, P=0.003), the possibility of contacting a radiologist to interpret (median rating 4.17 vs. 3.20, P<0.001), and overall clinical impact (median rating 4.23 vs. 3.27, P<0.001) than FTRs. Besides, research group had higher score of IBDQ intestinal symptom dimension (median score 61.13 vs. 58.02, P=0.003), IBDQ systemic symptom dimension (median score 24.48 vs. 20.67, P<0.001), IBDQ emotional capacity dimension (median score 65.65 vs. 61.74, P<0.001), IBDQ social ability dimension (median score 26.80 vs. 22.37, P<0.001), and total IBDQ score (median score 178.07 vs. 162.80, P<0.001) than control group. CONCLUSION: The SR of CTE in CD patients was conducive to improving the quality and readability of the report, and CD patients' life quality could significantly improve after the intervention of an evidence-based medicine model based on SR.

Individualized small bowel preparation for computed tomography enterography: A prospective randomized controlled trial.

BACKGROUND AND AIM: The study aims to evaluate the feasibility of body mass index (BMI)-based individualized small bowel preparation for computed tomography enterography (CTE). METHODS: In this prospective randomized controlled study, patients undergoing CTE were randomly assigned to the individualized group or standardized group. Those in individualized group were given different volumes of mannitol solution based on BMI (1000 mL for patients with BMI < 18.5 kg/m2, 1500 mL for patients with 18.5 kg/m2 ≤ BMI < 25 kg/m2 and 2000 mL for patients with BMI ≥ 25 kg/m2) while patients in the standardized group were all asked to consume 1500-mL mannitol solution. CTE images were reviewed by two experienced radiologists blindly. Each segment of the small bowel was assessed for small bowel image quality and disease detection rates. Patients were invited to record a diary regarding adverse events and acceptance. RESULTS: A total of 203 patients were enrolled and randomly divided into two groups. For patients with BMI < 18.5 kg/m2, 1000-mL mannitol solution permitted a significantly lower rate of flatulence (P = 0.045) and defecating frequency (P = 0.011) as well as higher acceptance score (P = 0.015), but did not affect bowel image quality and diseases detection compared with conventional dosage. For patients with BMI ≥ 25 kg/m2, 2000-mL mannitol solution provided better overall image quality (P = 0.033) but comparable rates of adverse events and patients' acceptance compared with conventional dosage. CONCLUSIONS: Individualized bowel preparation could achieve both satisfactory image quality and patients' acceptance thus might be an acceptable alternative in CTE.

CT-based radiomics signature of visceral adipose tissue and bowel lesions for identifying patients with Crohn's disease resistant to infliximab.

PURPOSE: To develop a CT-based radiomics model combining with VAT and bowel features to improve the predictive efficacy of IFX therapy on the basis of bowel model. METHODS: This retrospective study included 231 CD patients (training cohort, n = 112; internal validation cohort, n = 48; external validation cohort, n = 71) from two tertiary centers. Machine-learning VAT model and bowel model were developed separately to identify CD patients with primary nonresponse to IFX. A comprehensive model incorporating VAT and bowel radiomics features was further established to verify whether CT features extracted from VAT would improve the predictive efficacy of bowel model. Area under the curve (AUC) and decision curve analysis were used to compare the prediction performance. Clinical utility was assessed by integrated differentiation improvement (IDI). RESULTS: VAT model and bowel model exhibited comparable performance for identifying patients with primary nonresponse in both internal (AUC: VAT model vs bowel model, 0.737 (95% CI, 0.590-0.854) vs. 0.832 (95% CI, 0.750-0.896)) and external validation cohort [AUC: VAT model vs. bowel model, 0.714 (95% CI, 0.595-0.815) vs. 0.799 (95% CI, 0.687-0.885)), exhibiting a relatively good net benefit. The comprehensive model incorporating VAT into bowel model yielded a satisfactory predictive efficacy in both internal (AUC, 0.840 (95% CI, 0.706-0.930)) and external validation cohort (AUC, 0.833 (95% CI, 0.726-0.911)), significantly better than bowel alone (IDI = 4.2% and 3.7% in internal and external validation cohorts, both p < 0.05). CONCLUSION: VAT has an effect on IFX treatment response. It improves the performance for identification of CD patients at high risk of primary nonresponse to IFX therapy with selected features from RM. CRITICAL RELEVANCE STATEMENT: Our radiomics model (RM) for VAT-bowel analysis captured the pathophysiological changes occurring in VAT and whole bowel lesion, which could help to identify CD patients who would not response to infliximab at the beginning of therapy. KEY POINTS: • Radiomics signatures with VAT and bowel alone or in combination predicting infliximab efficacy. • VAT features contribute to the prediction of IFX treatment efficacy. • Comprehensive model improved the performance compared with the bowel model alone.

Residual abnormalities on CTE predict adverse outcomes in Crohn's disease with endoscopic healing.

BACKGROUND: Residual abnormalities on computed tomography enterography (CTE) in Crohn's disease (CD) with endoscopic healing (EH) may have prognostic implications and affect therapeutic strategy. METHODS: CD patients with EH who underwent CTE between March 2015 and June 2022 were enrolled. CTE findings of the terminal ileum and the most severe segment of colon at the time of EH were assessed respectively for each patient. Cox regression analysis and Kaplan-Meier curves were used to evaluate the association between residual abnormalities and adverse outcomes. RESULTS: A total of 140 patients (217 digestive segments) were included. Mesenteric edema (hazard ratio [HR] = 3.61, 95% CI = 1.81-7.20, P<0.001), fibrofatty proliferation (HR = 3.40, 95% CI = 1.97-5.85, P<0.001) and active small bowel inflammation (HR = 2.74, 95% CI = 1.59-4.71, P<0.001) were risk factors for clinical relapse. Furthermore, we built a scoring system using the three parameters. Radiologic score ≥ 1 was the best threshold to predict clinical relapse (HR = 4.56, 95% CI = 2.54-8.19, P<0.001) and it was validated in different outcomes. CONCLUSION: The scoring system based on three residual abnormalities on CTE can predict adverse outcomes in CD patients with EH.

Automatic Segmentation and Radiomics for Identification and Activity Assessment of CTE Lesions in Crohn's Disease.

BACKGROUND: The purpose of this article is to develop a deep learning automatic segmentation model for the segmentation of Crohn's disease (CD) lesions in computed tomography enterography (CTE) images. Additionally, the radiomics features extracted from the segmented CD lesions will be analyzed and multiple machine learning classifiers will be built to distinguish CD activity. METHODS: This was a retrospective study with 2 sets of CTE image data. Segmentation datasets were used to establish nnU-Net neural network's automatic segmentation model. The classification dataset was processed using the automatic segmentation model to obtain segmentation results and extract radiomics features. The most optimal features were then selected to build 5 machine learning classifiers to distinguish CD activity. The performance of the automatic segmentation model was evaluated using the Dice similarity coefficient, while the performance of the machine learning classifier was evaluated using the area under the curve, sensitivity, specificity, and accuracy. RESULTS: The segmentation dataset had 84 CTE examinations of CD patients (mean age 31 ±â€…13 years , 60 males), and the classification dataset had 193 (mean age 31 ±â€…12 years , 136 males). The deep learning segmentation model achieved a Dice similarity coefficient of 0.824 on the testing set. The logistic regression model showed the best performance among the 5 classifiers in the testing set, with an area under the curve, sensitivity, specificity, and accuracy of 0.862, 0.697, 0.840, and 0.759, respectively. CONCLUSION: The automated segmentation model accurately segments CD lesions, and machine learning classifier distinguishes CD activity well. This method can assist radiologists in promptly and precisely evaluating CD activity.

The automatic segmentation and radiomics of computed tomography enterography images can assist radiologists in accurately and quickly identifying Crohn's disease lesions and evaluating Crohn's disease activity.

Development and validation of multiparametric models based on computed tomography enterography to determine endoscopic activity and surgical risk in patients with Crohn's disease: A multi-center study.

Objective: To develop novel multiparametric models based on computed tomography enterography (CTE) scores to identify endoscopic activity and surgical risk in patients with Crohn's disease (CD). Methods: We analyzed 171 patients from 3 hospitals. Correlations between CTE outcomes and endoscopic scores were assessed using Spearman's rank correlation analysis. Predictive models for moderate to severe CD were developed, and receiver operating characteristic (ROC) curves were constructed to determine the area under the ROC curve (AUC). A combined nomogram based on CTE scores and clinical variables was also developed for predicting moderate to severe CD and surgery. Results: CTE scores were significantly correlated with endoscopy scores at the segment level. The global CTE score was an independent predictor of severe (HR = 1.231, 95% CI: 1.048-1.446, p = 0.012) and moderate-to-severe Simplified Endoscopic Scores for Crohn's Disease (SES-CD) (HR = 1.202, 95% CI: 1.090-1.325, p < 0.001). The nomogram integrating CTE and clinical data predicted moderate to severe SES-CD and severe SES-CD scores in the validation cohort with AUCs of 0.837 and 0.807, respectively. The CTE score (HR = 1.18; 95% CI: 1.103-1.262; p = 0.001) and SES-CD score (HR = 3.125, 95% CI: 1.542-6.33; p = 0.001) were independent prognostic factors for surgery-free survival. A prognostic nomogram incorporating CTE scores, SES-CD and C-reactive protein (CRP) accurately predicted the risk of surgery in patients with CD. Conclusion: The newly developed CTE score and multiparametric models displayed high accuracy in predicting moderate to severe CD and surgical risk for CD patients.

Enhancing the Differentiation between Intestinal Behçet's Disease and Crohn's Disease through Quantitative Computed Tomography Analysis.

Behçet's disease (BD) behaves similarly to Crohn's disease (CD) when the bowel is involved. Computed tomography enterography (CTE) can accurately show intestinal involvement and obtain body composition data. The objective of this study was to evaluate whether CTE could improve the ability to distinguish between intestinal BD and CD. This study evaluated clinical, laboratory, endoscopic, and CTE features on first admission. Body composition analysis was based on the CTE arterial phase. The middle layers of the L1-L5 vertebral body were selected. The indicators assessed included: the area ratio of visceral adipose tissue (VAT)/subcutaneous adipose tissue (SAT) (VSR) in each layer, the total volume ratio of VAT/SAT, the quartile of VAT attenuation in each layer and the coefficient of variation (CV) of the VAT area for each patient was also calculated. Two models were developed based on the above indicators: one was a traditional model (age, gender, ulcer distribution) and the other was a comprehensive model (age, gender, ulcer distribution, proximal ileum involvement, asymmetrical thickening of bowel wall, intestinal stenosis, VSRL4, and CV). The areas under the receiver operating characteristic (ROC) curve of the traditional (sensitivity: 80.0%, specificity: 81.0%) and comprehensive (sensitivity: 95.0%, specificity: 87.2%) models were 0.862 and 0.941, respectively (p = 0.005).

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.

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.

Computed tomography enterography-based multiregional radiomics model for differential diagnosis of Crohn's disease from intestinal tuberculosis.

PURPOSE: To build computed tomography enterography (CTE)-based multiregional radiomics model for distinguishing Crohn's disease (CD) from intestinal tuberculosis (ITB). MATERIALS AND METHODS: A total of 105 patients with CD and ITB who underwent CTE were retrospectively enrolled. Volume of interest segmentation were performed on CTE and radiomic features were obtained separately from the intestinal wall of lesion, the largest lymph node (LN), and region surrounding the lesion in the ileocecal region. The most valuable radiomic features was selected by the selection operator and least absolute shrinkage. We established nomogram combining clinical factors, endoscopy results, CTE features, and radiomic score through multivariate logistic regression analysis. Receiver operating characteristic (ROC) curves and decision curve analysis (DCA) were used to evaluate the prediction performance. DeLong test was applied to compare the performance of the models. RESULTS: The clinical-radiomic combined model comprised of four variables including one radiomic signature from intestinal wall, one radiomic signature from LN, involved bowel segments on CTE, and longitudinal ulcer on endoscopy. The combined model showed good diagnostic performance with an area under the ROC curve (AUC) of 0.975 (95% CI 0.953-0.998) in the training cohort and 0.958 (95% CI 0.925-0.991) in the validation cohort. The combined model showed higher AUC than that of the clinical model in cross-validation set (0.958 vs. 0.878, P = 0.004). The DCA showed the highest benefit for the combined model. CONCLUSION: Clinical-radiomic combined model constructed by combining CTE-based radiomics from the intestinal wall of lesion and LN, endoscopy results, and CTE features can accurately distinguish CD from ITB.

Computed tomography-based radiomics nomogram using machine learning for predicting 1-year surgical risk after diagnosis of Crohn's disease.

BACKGROUND: Identifying patients with aggressive Crohn's disease (CD) threatened by a high risk of early onset surgery is challenging. PURPOSE: We aimed to establish and validate a radiomics nomogram to predict 1-year surgical risk after the diagnosis of CD, thereby facilitating therapeutic strategies making. METHODS: Patients with CD who had undergone baseline computed tomography enterography (CTE) examination at diagnosis were recruited and randomly divided into training and test cohorts at a ratio of 7:3. Enteric phase CTE images were obtained. Inflamed segments and mesenteric fat were semiautomatically segmented, followed by feature selection and signature building. A nomogram of radiomics was constructed and validated using a multivariate logistic regression algorithm. RESULTS: A total of 268 eligible patients were retrospectively included, 69 of whom underwent surgery 1-year after diagnosis. A total of 1218 features from inflamed segments and 1218 features from peripheral mesenteric fat were extracted, and reduced to 10 and 15 potential predictors, respectively, to construct two radiomic signatures. By incorporating the radiomics signatures and clinical factors, the radiomics-clinical nomogram showed favorable calibration and discrimination in the training cohort, with an area under the curve (AUC) of 0.957, which was confirmed in the test set (AUC, 0.898). Decision curve analysis and net reclassification improvement index demonstrated the clinical usefulness of the nomogram. CONCLUSIONS: We successfully established and validated a CTE-based radiomic nomogram with both inflamed segment and mesenteric fat simultaneously evaluated to predict 1-year surgical risk in CD patients, which assisted in clinical decision-making and individualized management.

Preoperative computed tomography enterography-based radiomics signature: A potential predictor of postoperative anastomotic recurrence in patients with Crohn's disease.

BACKGROUND: More than half of patients with Crohn's disease (CD) require at least one surgery for symptom management; however, approximately half of the patients may experience postoperative anastomotic recurrence (PAR). OBJECTIVES: This study aims to develop and validate a preoperative computed tomography enterography (CTE)-based radiomics signature to predict early PAR in CD. DESIGN: A total of 186 patients with CD (training cohort, n = 134; test cohort, n = 52) who underwent preoperative CTE and surgery between January 2014 and June 2020 were included in this retrospective multi-centre study. METHODS: 106 radiomic features were initially extracted from intestinal lesions and peri-intestinal mesenteric fat, respectively; significant radiomic features were selected from them and then used to develop intestinal or mesenteric radiomics signatures, using the least absolute shrinkage and selection operator and a Cox regression model. A radiomics-based nomogram incorporating these signatures with clinical-radiological factors was created for comparison with a model based on clinical-radiological features alone. RESULTS: 68 of 134 patients in training cohort and 16 of 52 patients in test cohort suffered from PAR. The intestinal radiomic signature (hazard ratio [HR]: 2.17; 95% confidence interval [CI]: 1.32-3.58; P = 0.002) and mesenteric radiomic signature (HR: 2.19; 95% CI: 1.14-4.19; P = 0.018) were independent risk factors for PAR in the training cohort as per a multivariate analysis. The radiomics-based nomogram (C-index: 0.710; 95% CI: 0.672-0.748) yielded superior predictive performance than the clinical-radiological model (C-index, 0.607; 95% CI: 0.582-0.632) in the test cohort. Decision curve analysis demonstrated that the radiomics-based nomogram outperformed the clinical-radiological model in terms of clinical usefulness. CONCLUSIONS: Preoperative mesenteric and intestinal CTE radiomics signatures are potential non-invasive predictors of PAR in postoperative patients with CD.

CT-based radiomics signature of visceral adipose tissue for prediction of disease progression in patients with Crohn's disease: A multicentre cohort study.

Background: Visceral adipose tissue (VAT) is involved in the pathogenesis of Crohn's disease (CD). However, data describing its effects on CD progression remain scarce. We developed and validated a VAT-radiomics model (RM) using computed tomography (CT) images to predict disease progression in patients with CD and compared it with a subcutaneous adipose tissue (SAT)-RM. Methods: This retrospective study included 256 patients with CD (training, n = 156; test, n = 100) who underwent baseline CT examinations from June 19, 2015 to June 14, 2020 at three tertiary referral centres (The First Affiliated Hospital of Sun Yat-Sen University, The First Affiliated Hospital of Shantou University Medical College, and The First People's Hospital of Foshan City) in China. Disease progression referred to the development of penetrating or stricturing diseases or the requirement for CD-related surgeries during follow-up. A total of 1130 radiomics features were extracted from VAT on CT in the training cohort, and a machine-learning-based VAT-RM was developed to predict disease progression using selected reproducible features and validated in an external test cohort. Using the same modeling methodology, a SAT-RM was developed and compared with the VAT-RM. Findings: The VAT-RM exhibited satisfactory performance for predicting disease progression in total test cohort (the area under the ROC curve [AUC] = 0.850, 95% confidence Interval [CI] 0.764-0.913, P < 0.001) and in test cohorts 1 (AUC = 0.820, 95% CI 0.687-0.914, P < 0.001) and 2 (AUC = 0.871, 95% CI 0.744-0.949, P < 0.001). No significant differences in AUC were observed between test cohorts 1 and 2 (P = 0.673), suggesting considerable efficacy and robustness of the VAT-RM. In the total test cohort, the AUC of the VAT-RM for predicting disease progression was higher than that of SAT-RM (AUC = 0.786, 95% CI 0.692-0.861, P < 0.001). On multivariate Cox regression analysis, the VAT-RM (hazard ratio [HR] = 9.285, P = 0.005) was the most important independent predictor, followed by the SAT-RM (HR = 3.280, P = 0.060). Decision curve analysis further confirmed the better net benefit of the VAT-RM than the SAT-RM. Moreover, the SAT-RM failed to significantly improve predictive efficacy after it was added to the VAT-RM (integrated discrimination improvement = 0.031, P = 0.102). Interpretation: Our results suggest that VAT is an important determinant of disease progression in patients with CD. Our VAT-RM allows the accurate identification of high-risk patients prone to disease progression and offers notable advantages over SAT-RM. Funding: This study was supported by the National Natural Science Foundation of China, Guangdong Basic and Applied Basic Research Foundation, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Nature Science Foundation of Shenzhen, and Young S&T Talent Training Program of Guangdong Provincial Association for S&T. Translation: For the Chinese translation of the abstract see Supplementary Materials section.