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.

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.

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.

Dual-energy CT improves differentiation of non-hypervascular pancreatic neuroendocrine neoplasms from CA 19-9-negative pancreatic ductal adenocarcinomas.

PURPOSE: To evaluate the utility of dual-energy CT (DECT) in differentiating non-hypervascular pancreatic neuroendocrine neoplasms (PNENs) from pancreatic ductal adenocarcinomas (PDACs) with negative carbohydrate antigen 19-9 (CA 19-9). METHODS: This retrospective study included 26 and 39 patients with pathologically confirmed non-hypervascular PNENs and CA 19-9-negative PDACs, respectively, who underwent contrast-enhanced DECT before treatment between June 2019 and December 2021. The clinical, conventional CT qualitative, conventional CT quantitative, and DECT quantitative parameters of the two groups were compared using univariate analysis and selected by least absolute shrinkage and selection operator regression (LASSO) analysis. Multivariate logistic regression analyses were performed to build qualitative, conventional CT quantitative, DECT quantitative, and comprehensive models. The areas under the receiver operating characteristic curve (AUCs) of the models were compared using DeLong's test. RESULTS: The AUCs of the DECT quantitative (based on normalized iodine concentrations [nICs] in the arterial and portal venous phases: 0.918; 95% confidence interval [CI] 0.852-0.985) and comprehensive (based on tumour location and nICs in the arterial and portal venous phases: 0.966; 95% CI 0.889-0.995) models were higher than those of the qualitative (based on tumour location: 0.782; 95% CI 0.665-0.899) and conventional CT quantitative (based on normalized conventional CT attenuation in the arterial phase: 0.665; 95% CI 0.533-0.797; all P < 0.05) models. The DECT quantitative and comprehensive models had comparable performances (P = 0.076). CONCLUSIONS: Higher nICs in the arterial and portal venous phases were associated with higher blood supply improving the identification of non-hypervascular PNENs.

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.

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.

Use of dual-layer spectral detector computed tomography in the diagnosis of pancreatic neuroendocrine neoplasms.

PURPOSE: To explore the optimal energy level of dual-layer spectral detector computed tomography (DLCT) images of pancreatic neuroendocrine neoplasms (pNENs) and investigate the value in their detection. METHODS: This retrospective analysis included 134 pNEN patients with 136 lesions; they underwent contrast-enhanced DLCT scanning with histopathological confirmation of pNENs. Virtual monoenergetic images (VMI) of 40-100 keV, iodine concentration map (IC map), Z-effective atomic number map (Zeff map), and conventional images were analysed. The optimal energy level was obtained by comparing the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR). The lesion detection rates of DLCT and conventional images were compared. Subjective image analysis was performed by two readers who assessed the image quality and lesion conspicuity on a 5-point scale. RESULTS: The SNR of VMIs from 40 to 80 keV (arterial phase, P < 0.001; venous phase, P < 0.05) and CNR from 40 to 60 keV (arterial and venous phases, each P < 0.05) were higher than that of conventional images; VMI40keV showed the highest SNR and CNR. There was a good inter-reader agreement between the two reviewers (Kappa values > 0.61); the scores of Zeff and IC maps were higher than those of conventional images and VMI40keV (P < 0.05). The detection performance of DLCT images was better than conventional images. CONCLUSIONS: The VMI40keV demonstrated the best CNR and SNR of pNENs compared to other VMIs. Zeff and IC maps improve objective image quality and reader preference compared to conventional images. These findings could possess important clinical implications in formulating treatment strategies.

Inflammatory bowel disease cross-sectional imaging: What's new?

Cross-sectional imaging-ultrasonography, computed tomography enterography, and magnetic resonance enterography-is a routine and indispensable tool for patients with Crohn's disease (CD) that helps to detect or monitor disease characteristics before, during, and after CD treatment. New emerging radiological technologies may have further clinical applications in the management of CD. In this review article, we focus on the latest developments in cross-sectional imaging in CD research, including its role in intra- and extra-luminal lesion detection, intestinal inflammation and fibrosis grading, therapeutic response assessment and outcome prediction, postoperative recurrence detection and prediction, and the gut-brain axis.

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.

Multi-frequency magnetic resonance elastography of the pancreas: measurement reproducibility and variance among healthy volunteers.

Background: Patients with chronic pancreatitis often have irreversible pancreatic insufficiency before a clinical diagnosis. Pancreatic cancer is a fatal malignant tumor in the advanced stages. Patients having high risk of pancreatic diseases must be screened early to obtain better outcomes using new imaging modalities. Therefore, this study aimed to investigate the reproducibility of tomoelastography measurements for assessing pancreatic stiffness and fluidity and the variance among healthy volunteers. Methods: Forty-seven healthy volunteers were prospectively enrolled and underwent two tomoelastography examinations at a mean interval of 7 days. Two radiologists blindly and independently measured the pancreatic stiffness and fluidity at the first examination to determine the reproducibility between readers. One radiologist measured the adjacent pancreatic slice at the first examination to determine the reproducibility among slices and measured the pancreas at the second examination to determine short-term repeatability. The stiffness and fluidity of the pancreatic head, body, and tail were compared to determine anatomical differences. The pancreatic stiffness and fluidity were compared based on sex, age, and body mass index (BMI). Results: Bland-Altman analyses (all P > 0.05) and intraclass correlation coefficients (all >0.9) indicated near perfect reproducibility among readers, slices, and examinations at short intervals. Neither stiffness (P = 0.477) nor fluidity (P = 0.368) differed among the pancreatic anatomical regions. The mean pancreatic stiffness was 1.45 ± 0.09 m/s; the mean pancreatic fluidity was 0.83 ± 0.06 rad. Stiffness and fluidity did not differ by sex, age, or BMI. Conclusion: Tomoelastography is a promising and reproducible tool for assessing pancreatic stiffness and fluidity in healthy volunteers.

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.

Utility of Quantitative Metrics From Dual-Layer Spectral-Detector CT for Differentiation of Pancreatic Neuroendocrine Tumor and Neuroendocrine Carcinoma.

BACKGROUND. The 2019 WHO classification of digestive system tumors separates neuroendocrine neoplasms (NENs) into neuroendocrine tumors (NETs) and neuroendocrine carcinomas (NECs), which are considered to represent pathologically distinct entities warranting different management approaches. Dual-layer spectral-detector CT (DLCT) may aid their differentiation through specific material decomposition. OBJECTIVE. The purpose of this study was to assess the utility of quantitative metrics derived from DLCT for the differentiation of pancreatic NET and NEC. METHODS. This retrospective study included 104 patients (mean age, 51 ± 13 [SD] years; 52 women, 52 men) with pathologically confirmed NEN (89 NET, including 22 grade 1, 48 grade 2, and 19 grade 3; 15 NEC) who underwent multiphase DLCT within 15 days before biopsy or resection. Two radiologists independently placed ROIs to record tumor attenuation, iodine concentration (IC), and effective atomic number (Zeff) across phases and assessed qualitative features (composition, homogeneity, margins, calcifications, main pancreatic duct dilatation, vascular invasion, lymphadenopathy). Interobserver agreement was assessed. Mean and median values of both readers' measurements were obtained for quantitative measures; consensus was reached for qualitative features. NET and NEC were compared using multivariable regression analysis and ROC analysis. RESULTS. Interobserver agreement, expressed as intraclass correlation coefficients, ranged from 0.869 to 0.992 for quantitative metrics and, expressed as kappa coefficients, ranged from 0.723 to 0.816 for qualitative features. In multivariable analysis of qualitative and quantitative features, significant independent predictors of NEC (p < .05) were IC in the portal venous phase (median, 1.3 mg/mL for NEC vs 2.7 mg/mL for NET), Zeff in the portal venous phase (median, 8.1 vs 8.6), and attenuation in the portal venous phase (median, 78.2 vs 113.5 HU). AUC for predicting NEC was 0.897 for IC, 0.884 for Zeff, 0.921 for combination of IC and Zeff, and 0.855 for attenuation. Predicted probability based on a combination of IC and Zeff achieved sensitivity of 93.33% and specificity of 80.90% for predicting NEC. Significant independent predictors (p < .05) for differentiating grade 3 NET and NEC were IC (median, 2.0 vs 1.3 mg/mL; AUC = 0.789) and attenuation (mean, 90.3 vs 78.2 HU; AUC = 0.647), both measured in the portal venous phase. CONCLUSION. Incorporation of DLCT metrics improves differentiation of NET and NEC compared with conventional CT attenuation and qualitative features. CLINICAL IMPACT. DLCT may help select patients with pancreatic NENs for platinum-based chemotherapies.

Optimization of the tumour response threshold in advanced gastroenteropancreatic neuroendocrine carcinomas treated with cisplatin/etoposide combined chemotherapy.

OBJECTIVES: To identify early and more accurate imaging response criteria for computed tomography evaluation to define 'responders' in advanced gastroenteropancreatic neuroendocrine carcinoma (GEP-NEC) patients treated with cisplatin/etoposide combined chemotherapy. MATERIALS AND METHODS: Thirty-seven patients with GEP-NEC treated with first-line cisplatin/etoposide (E/P) combined chemotherapy were enrolled in this study. Computed tomography scans of the chest, abdomen, and pelvis were performed at baseline, during the treatment course, and during follow-up. Tumour size was measured, and tumour response was evaluated by Response Evaluation Criteria in Solid Tumours (RECIST) 1.1. Receiver operating characteristic (ROC) analysis was carried out among the patients who progressed during follow-up. Thresholds from -55% to + 5% were tested by Kaplan-Meier analysis to define "responders" for significantly improved progression-free survival (PFS). The overall survival rate was compared between these two groups. RESULTS: A reduction of 45% (vs. baseline) achieved the highest sensitivity (70%) and specificity (90%) by ROC analysis. This threshold divided patients into 15 responders and 22 nonresponders. Patients who were grouped as responders by the -45% threshold had a significantly longer PFS (11.06 months) than nonresponders (7.97 months, hazard ratio, 3.636; 95% confidence interval, 1.293-10.164). No significant difference was shown in overall survival between these two groups (29.1 vs. 21.4 months, P = 0.190). CONCLUSION: A 45% reduction in target lesions may be considered to be a more reliable predictor than the RECIST 1.1 criteria in evaluating the outcome of GEP-NEC patients treated with E/P chemotherapy.

A Type I Collagen-Targeted MR Imaging Probe for Staging Fibrosis in Crohn's Disease.

Fibrostenosis is a serious complication of Crohn's disease (CD), affecting approximately one-half of all patients. Surgical resection is the typical clinical end due to ineffective antifibrotic therapy mainly through anti-inflammatory treatment and fibrosis can be reverted only at early stages. Mover, human fibrotic disorders is known to be associated with aging process. Thus, accurate monitoring of the progression of fibrosis is crucial for CD management as well as can be benefit to aging related fibrosis. The excessive deposition of type I collagen (ColI) is the core point in major complications of fibrosis, including that in patients with CD and aging related fibrosis. Therefore, a MR imaging probe (EP-3533) targeted ColI was employed to stage bowel fibrosis in CD using a rat model and to compare its efficiency with the common MR imaging contrast medium gadopentetatedimeglumine (Gd-DTPA). The bowel fibrotic rat model was established with different degrees of bowel fibrosis, were scanned using a 3.0-T MRI scanner with a specialized animal coil. MRI sequence including T 1 mapping and T1-weighed imaging were performed before and after injecting the MRI probe (EP-3533 or Gd-DTPA). The T 1 relaxation time (T 1 value) and change in the contrast-to-noise ratio (ΔCNR) were measured to evaluate bowel fibrosis. Masson's trichrome staining was performed to determine the severity of fibrosis. EP-3533 offered a better longitudinal relaxivity (r1) with 67.537 L/mmol·s, which was approximately 13 times that of Gd-DTPA. The T 1 value on bowel segments was reduced in the images from EP-3533 compared to that from Gd-DTPA (F = 16.478; p < 0.001). Additionally, a better correlation between ΔCNR calculated from EP-3533 imaging and bowel fibrosis (AUC = 0.846) was determined 10 min after enhanced media administration than with Gd-DTPA (AUC = 0.532). The 10th-minute ΔCNR performed using the ColI probe showed the best correlation with the severity of bowel fibrosis (r = 0.538; p = 0.021). Our results demonstrates that targeted MRI probe (EP-3533) supplies a better enhanced effect compared to Gd-DTPA and could be a promising method to evaluate the progression and monitor the therapeutic response of bowel fibrosis.

A narrative review of multiple endocrine neoplasia syndromes: genetics, clinical features, imaging findings, and diagnosis.

OBJECTIVE: We aimed to provide ideas for clinicians, especially radiologists, for the diagnosis of multiple endocrine neoplasia (MEN) syndromes. BACKGROUND: MEN syndromes include MEN1, MEN2, and MEN4 and usually involve 2 or more endocrine tumors. The MEN syndromes are a group of euchromatic dominant genetic diseases, and the main genes involved include MEN1 (MEN1), RET (MEN2), and CDKN1B (MEN4). METHODS: In this article, involving 8 cases (4 cases of MEN1, 2 cases of MEN2A, 1 case of MEN2B, 1 case of MEN4) from our center, we introduced the disease spectrum, clinical manifestations (especially imaging findings), and related genes involved in each type of MEN syndromes. We also discussed the differential diagnosis between MEN and sporadic tumors and emphasized that MEN should be screened and the relevant required examinations. CONCLUSIONS: Considering that MEN syndromes involve multiple endocrine gland tumors and nonendocrine organ diseases, it is very important to identify potential patients early and perform multiple examinations on them, including biochemical and multitype, and multisite imaging examinations according to the disease spectrum of each type. Considering that this is a group of genetic diseases, both interviewing patients about their family history and genetic testing are also very important. Only in this way can a comprehensive and accurate diagnosis be made, enabling patients to receive appropriate treatment and improve their prognosis.

Predicting the recurrence risk of pancreatic neuroendocrine neoplasms after radical resection using deep learning radiomics with preoperative computed tomography images.

BACKGROUND: To establish and validate a prediction model for pancreatic neuroendocrine neoplasms (pNENs) recurrence after radical surgery with preoperative computed tomography (CT) images. METHODS: We retrospectively collected data from 74 patients with pathologically confirmed pNENs (internal group: 56 patients, Hospital I; external validation group: 18 patients, Hospital II). Using the internal group, models were trained with CT findings evaluated by radiologists, radiomics, and deep learning radiomics (DLR) to predict 5-year pNEN recurrence. Radiomics and DLR models were established for arterial (A), venous (V), and arterial and venous (A&V) contrast phases. The model with the optimal performance was further combined with clinical information, and all patients were divided into high- and low-risk groups to analyze survival with the Kaplan-Meier method. RESULTS: In the internal group, the areas under the curves (AUCs) of DLR-A, DLR-V, and DLR-A&V models were 0.80, 0.58, and 0.72, respectively. The corresponding radiomics AUCs were 0.74, 0.68, and 0.70. The AUC of the CT findings model was 0.53. The DLR-A model represented the optimum; added clinical information improved the AUC from 0.80 to 0.83. In the validation group, the AUCs of DLR-A, DLR-V, and DLR-A&V models were 0.77, 0.48, and 0.64, respectively, and those of radiomics-A, radiomics-V, and radiomics-A&V models were 0.56, 0.52, and 0.56, respectively. The AUC of the CT findings model was 0.52. In the validation group, the comparison between the DLR-A and the random models showed a trend of significant difference (P=0.058). Recurrence-free survival differed significantly between high- and low-risk groups (P=0.003). CONCLUSIONS: Using DLR, we successfully established a preoperative recurrence prediction model for pNEN patients after radical surgery. This allows a risk evaluation of pNEN recurrence, optimizing clinical decision-making.

Quantitative Contrast-Enhanced Ultrasound by Sonazoid in the Early Diagnosis of Biliary Atresia: An Experimental Study of Rats With Bile Duct Ligation.

We aimed to investigate the feasibility of quantitative Sonazoid contrast-enhanced ultrasound (CEUS) in the diagnosis of biliary atresia (BA) at an early disease stage. First, liver tissue from 10 patients with BA and 10 patients with infantile hepatitis syndrome (IHS) was immunostained with cluster of differentiation 68 (CD68) to compare Kupffer cell counts. Then, quantitative Sonazoid CEUS was performed to evaluate the hyperplasia of the artery branches during the artery phase and changes in Kupffer cell counts during the Kupffer phase in a rat model of BA (common bile duct ligation). Based on our results, liver enhancement from Kupffer cells in BA patients was significantly higher than that in IHS patients (p = 0.026 < 0.05). Furthermore, quantitative CEUS showed that the liver enhancement intensity of rats with bile duct ligation was substantially greater than that of rats without bile duct ligation during the artery phase 3 d post-operation and the Kupffer phase 5 d post-operation. The increased enhancement intensity in the Kupffer phase was in agreement with the increase in Kupffer cell counts in pathologic staining (18/20, 90%). In conclusion, quantitative Sonazoid CEUS can detect the hyperplasia of artery branches in artery phase and the increase of Kupffer cells in Kupffer phase in the liver of BA rats at an early disease stage. Therefore, Sonazoid CEUS could potentially be a valuable non-invasive alternative for the diagnosis of BA.

Role of Lung Ultrasound in Adjusting Ultrafiltration Volume in Hemodialysis Patients.

Chronic fluid over-hydration is common in dialysis patients. It is associated with mortality and cardiovascular events. Optimal methods for adjusting fluid volume status and ideal dry weight remain uncertain. The purpose of this study was to evaluate the usefulness of ultrasound in quantifying body water. In 35 hemodialysis patients, we performed ultrasound of the chest, pre-tibial skin tissue thickness (TT), heart and inferior vena cava (IVC) before and after dialysis. We compared B-line scores of lungs, IVC diameters and cardiac functions in pre-dialysis and post-dialysis groups. We then estimated the correlations between ultrasound parameters and ultrafiltration volumes. Ultrafiltration parameters were adjusted prospectively for subsequent dialysis. As a result, both extravascular and intravascular water decreased during ultrafiltration. The median numbers of B-line scores (10 [0-42] vs. 4 [0-30]; p < 0.001); mitral valve blood flow velocities E (0.83 ± 0.23 m/s vs. 0.70 ± 0.20 m/s; p < 0.001), A (0.93 ± 0.28 vs. 0.89 ± 0.23 m/s; p < 0.001) and E/e' (12.47 ± 4.92 vs. 10.37 ± 4.0; p < 0.001); IVC diameters at end-expiration (17.51 ± 3.33 mm vs. 14.26 ± 3.45 mm; p < 0.001); and right pre-tibial TT (2.86 ± 1.36 mm vs. 2.43 ± 1.24 mm; p < 0.001) decreased during dialysis. Ultrafiltration volume was most associated with B-line score (adjusting for age and sex) (ß = -3.340; p = 0.003). In addition, the B-line score after dialysis was significantly associated with left ventricular ejection fraction (r = -0.393; p = 0.019) and TT (r = -0.447; p = 0.007). Ultrafiltration volume was prospectively increased then if the B-line score was >6 in the previous dialysis. All patients tolerated the protocol well without any symptoms. Ultrafiltration volume was most associated with lung water, reflected by variation in B-line score. It was not associated with cardiac function, IVC diameter, IVC collapse rate or TT. Lung ultrasound is a useful imaging tool for dialysis patients.