Objective To investigate the imaging features and causes of missed diagnosis of colorectal cancer(CRC)in routine abdomi-nopelvic CT examinations.Methods A retrospective analysis was conducted on the clinical and imaging data of 38 patients with CRC which were initially overlooked in routine abdominopelvic CT examinations.The imaging features and causes of missed diagnosis were analyzed.Results Distribution of lesions:ten cases(26.3％)located in the caecum-ascending colon,6 cases(15.8％)located in the transverse colon,1 case(2.6％)located in the descending colon,18 cases(47.4％)located in the sigmoid colon and 3 cases(7.9％)located in the rectum.The mean length of diseased bowel was(2.8±1.3)cm.Pericolic fat stranding was found in 16 cases,locore-gional adenopathy was found in 19 cases,and focal pericolic neovascularity was found in 12 cases.The four reasons for missed diagno-sis included:(1)comprehensive assessment of colorectal conditions was not routinely performed;(2)small lesion size;(3)unfamiliar with the subtle secondary signs of CRC;(4)image-related reasons.Conclusion Radiologists should conduct conventional and com-prehensive assessment of the colorectum in routine abdominopelvic CT examinations.With the accumulation of diagnostic experience,it will be beneficial to reduce the missed diagnosis rate of CRC.

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.

Purpose To measure the spatial resolution in MRI quality control testing automatically based on the American College of Radiology(ACR)phantom using the support vector machine(SVM)method,and the feasibility,accuracy and measurement speed of this method are explored.Materials and Methods Quality control tests were performed using eight MRI devices at Beijing Friendship Hospital of Capital Medical University.A retrospective study was conducted on 71 MRI quality control test images collected based on ACR phantoms between 2017 and 2019.The images were preprocessed by binarization,extraction region of interest and so on.An SVM-based classification model was constructed for analyzing the spatial resolution of dot arrays in row and column directions.The dataset was randomly split into a training set and a test set.The generalization performance of the classification model in this study was evaluated through accuracy,precision,recall and F1 score on the test set.Comparing the results of spatial resolution measurements obtained by both manual and automatic method,we demonstrated the feasibility and accuracy of the method.Additionally,the time taken for the automatic spatial resolution measurement was recorded.Results In this study,the proposed method of automatically measuring the spatial resolution of ACR phantom test images using SVM was feasible,high accuracy and short time.In classification performance test,the accuracy of the spatial resolution of the row directional latices was 95%,the precision was 100%.The accuracy of the spatial resolution of the column directional latices was 97%,the precision was 100%.Among the test cases,the results of automatic measurements matched those of manual measurements in 13 out of 14 cases.On average,automatic spatial resolution measurement took 0.158 seconds per case.Conclusion This study achieves automatic measurement of spatial resolution in MRI quality control based on the ACR phantom using SVM method.The method demonstrates high accuracy and fast measurement speeds,holding significant implications for future rapid MRI quality control stability testing.

Purpose To explore an automatic detection method for quality control performance indicators of radio frequency coils based on American College of Radiology(ACR)phantom,and verify its accuracy stability and computational efficiency.Materials and Methods A retrospective study was conducted on 50 quality control images collected based on ACR phantom in Beijing Friendship Hospital,Capital Medical University from May 2017 to July 2019.The measurement and calculation methods of signal noise ratio(SNR),percent image uniformity(PIU)and percent signal ghosting(PSG)were used to automatically calculate the above indicators using a self-designed program in Python.A simple linear regression analysis on the automatically calculated SNR,PSG and PIU values compared to the manually measured results was performed,and Bland-Altman analysis was used to calculate the percentage difference to evaluate the consistency and bias between the performance indicators calculated by the two methods.The time consumption of two detection methods was compared to verify their computational complexity and efficiency.Results There was a strong correlation between the performance indicators SNR,PSG and PIU of radio frequency coils measured and calculated automatically and manually(r=0.991 4,0.992 8 and 0.909 8,all P<0.0001).The Bland-Altman results showed that most of the data fall within the 95%confidence interval and were evenly distributed.In terms of computational complexity and efficiency,compared to the complex manual delineation and calculation of 2-3 minutes per case,automatic detection could simultaneously obtain SNR,PSG and PIU values in less than 1 second.Conclusion The automatic and manual measurement methods have good consistency,and the automatic detection method is easy to operate,which is helpful for the daily quality control work and performance monitoring of radio frequency coils.

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: 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: 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: 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.