Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Purpose To compare the MRI features of hepatic sinusoidal obstruction syndrome(HSOS)and Budd-Chiari syndrome(BCS).Materials and Methods A total of 12 cases of HSOS and 12 cases of BCS diagnosed with liver biopsy or liver transplantation disease or digital subtraction angiography were retrospectively collected from April 2016 to November 2023 in Beijing Friendship Hospital,Capital Medical University.The clinical and MRI features of the two groups were analyzed,and the characteristic clinical and MRI features were summarized.Results The glutamic-oxaloacetic transaminase,total bilirubin and direct bilirubin in HSOS patients were significantly higher than those in BCS group,while albumin was significantly lower than that in BCS group,the difference was statistically significant(Z=-3.407,-2.078,-2.425,-2.252,all P<0.05).Of the 12 HSOS patients,ten patients with HSOS had a history of taking hebel medicine(gynura segetum),six patients with HSOS showed the second portal of liver characteristic trifolium-like enhancement,BCS features did not appear.Of the 12 BCS patients,ten had main portal vein widening,ten spleen enlargement,ten accessory hepatic vein,eight transverse hepatic vein,eleven perihepatic lateral branch and seven paravertebral varicose veins.In HSOS and BCS patients,the uneven proportion of liver parenchymal signals was identified by MRI plain scan(12 cases,10 cases,respectively),and the proportion of liver parenchymal signal mottle-like enhancement was also demonstrated in portal and delayed phases(12 cases,12 cases,respectively).Conclusion The characteristic MRI signs are helpful for the diagnosis of HSOS and BCS,and the history of taking hebel medicine(gynura segetum)is helpful for the differentiation of the two diseases.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective To establish and validate a machine learning model integrating abdominal contrast-enhanced CT radiomics features and clinical characteristics,and to construct a predictive model for the efficacy of anticoagulant treatment in portal vein thrombosis(PVT).Methods A retrospective selection was conducted on 94 PVT patients who received anticoagulant treatment.Patients were divided into effective and ineffective treatment groups based on the follow-up results.Clinical information was collected,and imaging features were evaluated.Univariate and multivariate logistic regression were performed to select clinical information and imaging fea-tures for constructing a clinical-imaging model.On CT venous phase images,the PVT mask was delineated and radiomics features were extracted,and the radiomics model was screened and established.A combined model was further developed using features from both the clinical-imaging and radiomics models.Receiver operating characteristic(ROC)curves were used to evaluate the predictive efficacy of different models.Results The area under the curve(AUC)for the clinical-imaging model,radiomics model,and com-bined model were 0.594,0.794,and 0.776,respectively.The radiomics and combined models demonstrated superior predictive efficacy for anticoagulant treatment in PVT compared to the clinical-imaging model.No significant difference in performance was observed between radiomics and combined models.Conclusion The radiomics model and combined model based on abdominal contrast-enhanced CT can effectively predict the efficacy of anticoagulant treatment for PVT.

Objective To establish a predictive model for hepatic sinusoidal obstruction syndrome(HSOS)and Budd-Chiari syn-drome(BCS),and to evaluate the performance of the model.Methods From April 2016 to February 2024,20 patients with HSOS and 40 patients with BCS who were first discharged from the Beijing Friendship Hospital Affiliated to Capital Medical University hospital infor-mation system(HIS)system were retrospectively collected.The clinical data of all patients were extracted,and the independent risk fac-tors for HSOS were screened by multi-factor Logistic regression method,and the differential diagnosis model was established accordingly,and then the efficacy was evaluated by receiver operating characteristic(ROC)curve.Results Multivariate Logistic regression analysis showed that hepatic vein stenosis or unclear display(OR=39.441,95％CI:5.928-262.429)was an independent risk factor for HSOS,and paravertebral vein opening was an independent protective factor for HSOS(OR=0.026,95％CI:0.002-0.285).Based on the above two parameters,a prediction model for HSOS in patients with hepatic venous outlet tract obstruction was established.The ROC curve showed that the area under the curve(AUC)of the model was 0.922(95％CI:0.864-0.991),with the sensitivity of 85.0％and the specificity of 92.5％.Conclusion Hepatic vein stenosis or unclear display is an independent risk factor for HSOS in patients with hepatic vein outflow tract obstruction during hospitalization,and the model constructed can predict the risk of HSOS.

Crohn disease is a chronic inflammatory bowel disease and characterized by many complicated complications and diverse imaging manifestations.At present,in regards of diagnosis and treatment,a key issue is to find a non-invasive,fast and accurate examination method.In recent years,a variety of imaging techniques such as computed tomography enterography,spectral CT,magnetic resonance enterography and magnetic resonance functional imaging have shown promising application prospects in evaluating of disease severity and therapeutic effect of Crohn disease.The research progress of CT and MRI for evaluation of Crohn disease were reviewed in this article,in order to provide imaging reference for the clinical diagnosis and treatment of Crohn disease.

Objective: Accurate evaluation of inflammation severity in ulcerative colitis (UC) can guide treatment strategy selection. The potential value of the pericolic fat attenuation index (FAI) on CT as an indicator of disease severity remains unknown.This study aimed to assess the diagnostic accuracy of pericolic FAI in predicting UC severity. Materials and Methods: This retrospective study enrolled 148 patients (mean age 48 years; 87 males). The fat attenuation on CT was measured in four different locations: the mesocolic vascular side (MS) and opposite side of MS (OMS) around the most severe bowel lesion, the retroperitoneal space (RS), and the subcutaneous area. The fat attenuation indices (FAI MS, FAI OMS, and FAI RS) were calculated as the fat attenuation measured in MS, OMS, and RS, respectively, minus that of the subcutaneous area, and were obtained in the non-enhanced, arterial, and delayed phases. Correlations between the FAI and UC Endoscopic Index of Severity (UCEIS) were assessed using Spearman’s correlation. Predictors of severe UC (UCEIS ≥7) were selected by univariable analysis. The performance of FAI in predicting severe UC was evaluated using the area under the receiver operating characteristic curve (AUC). Results: The FAIMS and FAI OMS scores were significantly higher than FAI RS in three phases (all P < 0.001). The FAIMS and FAI OMS scores moderately correlated with the UCEIS score (r = 0.474–0.649 among the three phases). Additionally, FAI MS and FAI OMS identified severe UC, with AUC varying from 0.77 to 0.85. Conclusion Increased CT attenuation of pericolic adipose tissue could serve as a noninvasive marker for evaluating UC severity. FAI MS and FAI OMS of three phases showed similar prediction accuracies for severe UC identification.

Objective:To dynamically assess liver fibrosis using magnetic resonance elastography (MRE) and explore factors associated with fibrosis reversal in patients with metabolic dysfunction-associated steatohepatitis (MASH).Methods:This study included data from patients diagnosed with MASH by liver biopsy who underwent at least two MRE examinations. Patients were divided into a fibrosis reversal group and a non-reversal group according to whether MRE values decreased by 20% during follow-up. Differences in clinical data between the groups were compared using analysis of variance, the Kruskal-Wallis test, and the chi-square test. Univariate and multivariate logistic regression analyses were used to explore independent risk factors for fibrosis reversal in MASH.Results:A total of 46 cases were included in this study (mean age 50.1±12.3 years, BMI 26.1±3.1 kg/m2). Among them, the reversal group accounted for 26.1%. The rate of decrease in MRI proton density fat fraction (PDFF) was significantly higher in the reversal group (-50.0% vs. -8.1%, P=0.001) than in the non-reversal group between the two MRE examinations. The reversal group showed a more significant change rate of decreases in fasting insulin (-37.3% vs. -3.6%, P=0.011), insulin resistance index (-38.6% vs. -6.5%, P=0.044), and ALP (-24.9% vs. 0, P=0.004). Multivariate logistic regression analysis indicated that the rate of change in MRI PDFF was an independent predictor of fibrosis reversal ( OR=0.96, 95% CI: 0.92-1.00, P=0.046). Conclusion:A decrease in MRI proton density fat fraction levels is independently associated with liver fibrosis reversal in MASH, suggesting that intervention targeting liver fat content may be an effective treatment strategy.

Objective To establish and validate a machine learning model integrating abdominal contrast-enhanced CT radiomics features and clinical characteristics,and to construct a predictive model for the efficacy of anticoagulant treatment in portal vein thrombosis(PVT).Methods A retrospective selection was conducted on 94 PVT patients who received anticoagulant treatment.Patients were divided into effective and ineffective treatment groups based on the follow-up results.Clinical information was collected,and imaging features were evaluated.Univariate and multivariate logistic regression were performed to select clinical information and imaging fea-tures for constructing a clinical-imaging model.On CT venous phase images,the PVT mask was delineated and radiomics features were extracted,and the radiomics model was screened and established.A combined model was further developed using features from both the clinical-imaging and radiomics models.Receiver operating characteristic(ROC)curves were used to evaluate the predictive efficacy of different models.Results The area under the curve(AUC)for the clinical-imaging model,radiomics model,and com-bined model were 0.594,0.794,and 0.776,respectively.The radiomics and combined models demonstrated superior predictive efficacy for anticoagulant treatment in PVT compared to the clinical-imaging model.No significant difference in performance was observed between radiomics and combined models.Conclusion The radiomics model and combined model based on abdominal contrast-enhanced CT can effectively predict the efficacy of anticoagulant treatment for PVT.