Comparative analysis of [18F]F-FAPI PET/CT, [18F]F-FDG PET/CT and magnetization transfer MR imaging to detect intestinal fibrosis in Crohn's disease: A prospective animal model and human cohort study.

PURPOSE: Accurately and early detection of intestinal fibrosis in Crohn's disease (CD) is crucial for clinical management yet remains an unmet need. Fibroblast activation protein inhibitor (FAPI) PET/CT has emerged as a promising tool to assess fibrosis. We aimed to investigate the diagnostic capability of [18F]F-FAPI PET/CT in detecting intestinal fibrosis and compared it with[18F]F-FDG PET/CT and magnetization transfer MR imaging (MTI). METHODS: Twenty-two rats underwent TNBS treatment to simulate fibrosis development, followed by three quantitative imaging sessions within one week. Mean and maximum standardized uptake values (SUVmean and SUVmax) were calculated on[18F]F-FAPI and [18F]F-FDG PET/CT, along with normalized magnetization transfer ratio on MTI. Intestinal fibrosis was assessed pathologically, with MTI serving as imaging standard for fibrosis. The diagnostic efficacy of imaging parameters in fibrosis was compared using pathological and imaging standards. Ten patients with 34 bowel strictures were prospectively recruited to validate their diagnostic performance, using the identical imaging protocol. RESULTS: In CD patients, the accuracy of FAPI uptake (both AUCs = 0.87, both P ≤ 0.01) in distinguishing non-to-mild from moderate-to-severe fibrosis was higher than FDG uptake (both AUCs = 0.82, P ≤ 0.01) and comparable to MTI (AUCs = 0.90, P ≤ 0.001). In rats, FAPI uptake responded earlier to fibrosis development than FDG and MTI; consistently, during early phase, FAPI uptake showed a stronger correlation (SUVmean: R = 0.69) with pathological fibrosis than FDG (SUVmean: R = 0.17) and MTI (R = 0.52). CONCLUSION: The diagnostic efficacy of [18F]F-FAPI PET/CT in detecting CD fibrosis is superior to [18F]F-FDG PET/CT and comparable to MTI, exhibiting great potential for early detection of intestinal fibrosis.

Cross-sectional imaging: current status and future potential in adult celiac disease.

Celiac disease (CD), triggered by exposure to gluten in genetically susceptible individuals, is an immune-mediated small bowel disease affecting about 1% of the population worldwide. But the prevalence of CD varies with age, sex, and location. A strict gluten-free diet remains the primary treatment for CD, currently. Most of patients with CD respond well to gluten-free diet with good prognosis, while some patients fail to get symptomatic relief or histological remission (e.g., nonresponsive or refractory CD). Because of heterogeneous clinical appearance, the diagnosis of CD is difficult. Moreover, malignant complications and poor outcomes accompanied with refractory CD present great challenges in disease management. Over the past three decades, cross-sectional imaging techniques (computed tomography [CT] and magnetic resonance imaging [MRI]) play an important role in small bowel inflammatory and neoplastic diseases. Compared with endoscopic techniques, cross-sectional imaging permits clearly presentation of both intraluminal and extraluminal abnormalities. It provides vascular and functional information, thus improving the possibility as diagnostic and follow-up tool. The value of cross-sectional imaging for patients with suspected or confirmed CD has been gradually demonstrated. Studies revealed that certain features suggested by cross-sectional imaging could help to establish the early diagnosis of CD. Besides, the potential contributions of cross-sectional imaging may lie in the evaluation of disease activity and severity, which helps guiding management strategies. The purpose of this review is to provide current overviews and future directions of cross-sectional imaging in adult CD, thus facilitating the understanding and application in clinical practice. CLINICAL RELEVANCE STATEMENT: In this review, we systematically summarized the existing knowledge of cross-sectional imaging in adult CD and analyzed their possible roles in clinical practice, including disease diagnosis, complication identification, treatment evaluation, and prognostic prediction. KEY POINTS: • Regarding a condition described as "celiac iceberg", celiac disease remains underdiagnosed and undertreated. • Cross-sectional imaging is helpful in clinical management of celiac disease, including disease diagnosis, complication identification, treatment evaluation, and prognostic prediction. • Cross-sectional imaging should be considered as the valuable examination in patients suspected from celiac disease.

Clinical outcome is distinct between radiological stricture and endoscopic stricture in ileal Crohn's disease.

OBJECTIVES: Differences in clinical adverse outcomes (CAO) based on different intestinal stricturing definitions in Crohn's disease (CD) are poorly documented. This study aims to compare CAO between radiological strictures (RS) and endoscopic strictures (ES) in ileal CD and explore the significance of upstream dilatation in RS. METHODS: This retrospective double-center study included 199 patients (derivation cohort, n = 157; validation cohort, n = 42) with bowel strictures who simultaneously underwent endoscopic and radiologic examinations. RS was defined as a luminal narrowing with wall thickening relative to the normal gut on cross-sectional imaging (group 1 (G1)), which further divided into G1a (without upstream dilatation) and G1b (with upstream dilatation). ES was defined as an endoscopic non-passable stricture (group 2 (G2)). Strictures met the definitions of RS (with or without upstream dilatation) and ES were categorized as group 3 (G3). CAO referred to stricture-related surgery or penetrating disease. RESULTS: In the derivation cohort, G1b (93.3%) had the highest CAO occurrence rate, followed by G3 (32.6%), G1a (3.2%), and G2 (0%) (p < 0.0001); the same order was found in the validation cohort. The CAO-free survival time was significantly different among the four groups (p < 0.0001). Upstream dilatation (hazard ratio, 1.126) was a risk factor for predicting CAO in RS. Furthermore, when upstream dilatation was added to diagnose RS, 17.6% of high-risk strictures were neglected. CONCLUSIONS: CAO differs significantly between RS and ES, and clinicians should pay more attention to strictures in G1b and G3. Upstream dilatation has an important impact on the clinical outcome of RS but may not be an essential factor for RS diagnosis. CLINICAL RELEVANCE STATEMENT: This study explored the definition of intestinal stricture with the greatest significance for the clinical diagnosis and prognosis of patients with CD, and consequently provided effective auxiliary information for clinicians to formulate strategies for the treatment of CD intestinal strictures. KEY POINTS: • The retrospective double-center study showed that clinical adverse outcome is different between radiological strictures and endoscopic strictures in CD. • Upstream dilatation has an important impact on the clinical outcome of radiological strictures but may not be an essential factor for diagnosis of radiological strictures. • Radiological stricture with upstream dilatation and simultaneous radiological and endoscopic stricture were at increased risk for clinical adverse outcomes; thus, closer monitoring should be considered.

Intestinal fibrosis classification in patients with Crohn's disease using CT enterography-based deep learning: comparisons with radiomics and radiologists.

OBJECTIVES: Accurate evaluation of bowel fibrosis in patients with Crohn's disease (CD) remains challenging. Computed tomography enterography (CTE)-based radiomics enables the assessment of bowel fibrosis; however, it has some deficiencies. We aimed to develop and validate a CTE-based deep learning model (DLM) for characterizing bowel fibrosis more efficiently. METHODS: We enrolled 312 bowel segments of 235 CD patients (median age, 33 years old) from three hospitals in this retrospective study. A training cohort and test cohort 1 were recruited from center 1, while test cohort 2 from centers 2 and 3. All patients performed CTE within 3 months before surgery. The histological fibrosis was semi-quantitatively assessed. A DLM was constructed in the training cohort based on a 3D deep convolutional neural network with 10-fold cross-validation, and external independent validation was conducted on the test cohorts. The radiomics model (RM) was developed with 4 selected radiomics features extracted from CTE images by using logistic regression. The evaluation of CTE images was performed by two radiologists. DeLong's test and a non-inferiority test were used to compare the models' performance. RESULTS: DLM distinguished none-mild from moderate-severe bowel fibrosis with an area under the receiver operator characteristic curve (AUC) of 0.828 in the training cohort and 0.811, 0.808, and 0.839 in the total test cohort, test cohorts 1 and 2, respectively. In the total test cohort, DLM achieved better performance than two radiologists (*1 AUC = 0.579, *2 AUC = 0.646; both p < 0.05) and was not inferior to RM (AUC = 0.813, p < 0.05). The total processing time for DLM was much shorter than that of RM (p < 0.001). CONCLUSION: DLM is better than radiologists in diagnosing intestinal fibrosis on CTE in patients with CD and not inferior to RM; furthermore, it is more time-saving compared to RM. KEY POINTS: • Question Could computed tomography enterography (CTE)-based deep learning model (DLM) accurately distinguish intestinal fibrosis severity in patients with Crohn's disease (CD)? • Findings In this cross-sectional study that included 235 patients with CD, DLM achieved better performance than that of two radiologists' interpretation and was not inferior to RM with significant differences and much shorter processing time. • Meaning This DLM may accurately distinguish the degree of intestinal fibrosis in patients with CD and guide gastroenterologists to formulate individualized treatment strategies for those with bowel strictures.

Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease.

PURPOSE: Lifelong re-examination of CT enterography (CTE) in patients with inflammatory bowel disease (IBD) may be necessary, and reducing radiation exposure during CT examinations is crucial. We investigated the potential application of deep learning reconstruction (DLR) in CTE to reduce radiation dose and improve image quality in IBD. METHODS: Thirty-six patients with known or suspected IBD were prospectively recruited to the low-dose CTE (LDCTE) group, while forty patients were retrospectively selected from previous clinical standard-dose CTE (STDCTE) scans as controls. STDCTE images were reconstructed with hybrid-IR (adaptive iterative dose reduction 3-dimensional [AIDR3D], standard setting); LDCTE images were reconstructed with AIDR3D and DLR (Advanced Intelligence ClearIQ Engine [AiCE], Body mild/standard/strong, Sharp Body mild/standard/strong setting). The effective radiation dose (ED), image noise, signal-to-noise ratio (SNR), overall image quality, subjective image noise, and diagnostic effectiveness were compared between the LDCTE and STDCTE groups. RESULTS: Compared with STDCTE, the ED of LDCTE was lower by 54.1% (p<0.001). Compared with STDCTE-AIDR3D, LDCTE-AIDR3D reconstruction objective image noise and SNR were greater (p<0.05), the subjective overall image quality was lower (p<0.05), and the diagnostic efficiency was lower (AUC=0.52, p<0.05). The SNRs of reconstructedimages of LDCTE-AiCE Body Strong and LDCTE-AiCE Body Sharp standard/strong groups were greater than that of STDCTE-AIDR3D group (all p<0.05), and the diagnostic performance was better than or comparable to that of STDCTE; the AUCs were 0.83, 0.76 and 0.76, respectively CONCLUSION: Compared with STDCTE with AIDR3D, LDCTE with DLR effectively reduced the radiation dose and improve image quality in IBD patients.

A novel multidisciplinary machine learning approach based on clinical, imaging, colonoscopy, and pathology features for distinguishing intestinal tuberculosis from Crohn's disease.

OBJECTIVES: Differentiating intestinal tuberculosis (ITB) from Crohn's disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach for distinguishing ITB from CD. METHODS: Eighty-two patients including 25 patients with ITB and 57 patients with CD were retrospectively recruited (54 in training cohort and 28 in testing cohort). The region of interest (ROI) for the lesion was delineated on magnetic resonance enterography (MRE) and colonoscopy images. Radiomic features were extracted by least absolute shrinkage and selection operator regression. Pathological feature was extracted automatically by deep-learning method. Clinical features were filtered by logistic regression analysis. Diagnostic performance was evaluated by receiver operating characteristic (ROC) curve and decision curve analysis (DCA). Delong's test was applied to compare the efficiency between the multidisciplinary-based model and the other four single-disciplinary-based models. RESULTS: The radiomics model based on MRE features yielded an AUC of 0.87 (95% confidence interval [CI] 0.68-0.96) on the test data set, which was similar to the clinical model (AUC, 0.90 [95% CI 0.71-0.98]) and higher than the colonoscopy radiomics model (AUC, 0.68 [95% CI 0.48-0.84]) and pathology deep-learning model (AUC, 0.70 [95% CI 0.49-0.85]). Multidisciplinary model, integrating 3 clinical, 21 MRE radiomic, 5 colonoscopy radiomic, and 4 pathology deep-learning features, could significantly improve the diagnostic performance (AUC of 0.94, 95% CI 0.78-1.00) on the bases of single-disciplinary-based models. DCA confirmed the clinical utility. CONCLUSIONS: Multidisciplinary-based model integrating clinical, MRE, colonoscopy, and pathology features was useful in distinguishing ITB from CD.

Multiomics reveals microbial metabolites as key actors in intestinal fibrosis in Crohn's disease.

Intestinal fibrosis is the primary cause of disability in patients with Crohn's disease (CD), yet effective therapeutic strategies are currently lacking. Here, we report a multiomics analysis of gut microbiota and fecal/blood metabolites of 278 CD patients and 28 healthy controls, identifying characteristic alterations in gut microbiota (e.g., Lachnospiraceae, Ruminococcaceae, Muribaculaceae, Saccharimonadales) and metabolites (e.g., L-aspartic acid, glutamine, ethylmethylacetic acid) in moderate-severe intestinal fibrosis. By integrating multiomics data with magnetic resonance enterography features, putative links between microbial metabolites and intestinal fibrosis-associated morphological alterations were established. These potential associations were mediated by specific combinations of amino acids (e.g., L-aspartic acid), primary bile acids, and glutamine. Finally, we provided causal evidence that L-aspartic acid aggravated intestinal fibrosis both in vitro and in vivo. Overall, we offer a biologically plausible explanation for the hypothesis that gut microbiota and its metabolites promote intestinal fibrosis in CD while also identifying potential targets for therapeutic trials.

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.

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.

Potentiation of tumor radiotherapy by a radiation-inducible oncolytic and oncoapoptotic adenovirus in cervical cancer xenografts.

The p53 tumor suppressor pathway is impaired in more than 90% of cervical cancers and cancer-derived cell lines as a result of infection by human papillomavirus (HPV). The HPV E6 oncoprotein forms complexes with p53 and promotes its degradation via ubiquitin-dependent mechanism. In our study, we attempted to improve the clinical outcomes of this combined therapy by modifying the p53-targeted adenovirus to become radiation-responsive. The antitumor adenovirus was constructed by inserting a radiation-responsive expression cassette composed of the promoter of early growth response-1 (Egr-1) and the proapoptotic protein TRAIL. We showed that the addition of adenovirus containing Egr-1/TRAIL significantly increased cell death and apoptosis caused by radiotherapy. In mice bearing xenograft tumors, intratumoral administration of the Egr-1/TRAIL adenovirus followed by radiation significantly reduced tumor growth and enhanced tumor survival. Our Egr-1/TRAIL adenoviral gene product may offer a novel "one-two punch" tumor therapy for cervical cancers not only by potentiating radiation treatment but also by preserving p53 defect-specific tumor killing of the oncolytic adenovirus.

Mesenteric abnormalities play an important role in grading intestinal fibrosis in patients with Crohn's disease: a computed tomography and clinical marker-based nomogram.

Background: While the grading of intestinal fibrosis is closely related to the therapeutic strategy of patients with Crohn's disease (CD), it has not yet been well resolved. Mesenteric abnormalities are inextricably linked to intestinal fibrosis. Objectives: We aimed to establish an optimal model for assessing intestinal fibrosis using computed tomography enterography (CTE) and clinical markers. Design: A total of 174 patients with CD between January 2014 and June 2020 were included in this retrospective multicentre study. Methods: All patients underwent CTE within 3 months prior to surgery. Intestinal fibrosis was pathologically scored as non-mild or moderate-to-severe. Selected imaging of the intestinal walls and mesentery and/or clinical factors were used to develop the diagnostic models. The area under the receiver operating characteristic curve (AUC) analysis was used to evaluate the discrimination performance of the models. A decision curve analysis was performed to evaluate the clinical usefulness of the models. Results: One-, two-, and three-variable models were identified as possible diagnostic models. Model 1 [mesenteric creeping fat index (MCFI)], Model 2 (mesenteric oedema and MCFI), and Model 3 (mesenteric oedema, MCFI, and disease duration) were established. The AUCs of Model 1 in training and test cohorts 1 and 2 were 0.799, 0.859, and 0.693, respectively; Model 2 was 0.851, 0.833, and 0.757, respectively; and Model 3 was 0.832, 0.821, and 0.850, respectively. We did not observe any significant difference in diagnostic performance between the training and total test cohorts in any model (all p > 0.05). The decision curves showed that Model 3 had the highest net clinical benefit in test cohort 2. The nomogram of this optimal model was constructed by considering the favourable and robust performance of Model 3. Conclusion: A nomogram integrating mesenteric abnormalities on CTE with a clinical marker was optimal for differentiating between non-mild and moderate-to-severe fibrosis in patients with CD.