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 compare the ability of single-phase,dual-phase,and triphasic models in forecasting postoperative pancreatic fistula(POPF)after pancreaticoduodenectomy(PD)using radiomics based on triphasic enhanced CT.Methods A total of 181 patients who underwent multi-phase enhanced CT prior to PD were retrospectively selected,and the collection phase included non-contrast,arterial phase(AP),and equilibrium phase(EP).3D Slicer software was utilized to segment the region of interest(ROI)for the postoperative pancreatic remnant on each phase.Radiomics feature extraction was performed using R software,followed by feature selection through least absolute shrinkage and selection operator(LASSO)regression with five-fold cross-validation to prevent model overfitting.The effective features selected were combined in a weighted linear manner to obtain a Radiomics score(Radscore).The patients were divided into training set and test set in a 7︰3 ratio.Logistic regression was employed to construct seven POPF prediction models(three single-phase,three dual-phase,and one triphasic models)based on different phase combinations.The diagnostic performance of the models was evaluated using the area under the curve(AUC)of receiver operating characteristic(ROC)curve,accuracy(ACC),sensitivity(SEN),and specificity(SPE).The DeLong test was applied to compare the differences in AUC among different models.Results After LASSO regression,24 effective features associated with POPF were selected from different phases.In the test set,the triphasic model exhibited the highest AUC and ACC(AUC=0.76,ACC=0.808).The calibration curve demonstrated the strongest agreement between the estimated probabilities and observed probabilities for the triphasic model.The decision curve analysis(DCA)curve indicated that the triphasic model had the largest threshold range with a higher net benefit.Conclusion Compared with single-phase and dual-phase models,the triphasic model based on enhanced CT provides better prediction of POPF after PD,aiding clinical decision-making and improve prognosis.

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.

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.

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 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.

OBJECTIVE To compare the ability of ultra-high-resolution CT(U-HRCT)and multi-slice spiral CT(MSCT)to display key vocal transmission structures in the middle ear.METHODS Subjects with normal middle ear structures who underwent 0.1 mm layer thickness U-HRCT and 0.625 mm layer thickness MSCT scans at the same time in Beijing Friendship Hospital affiliated to Capital Medical University from December 2019 to August 2024 were retrospectively enrolled.Two experienced head and neck radiologists reconstruct standard transsectional,coronal images based on the thinnest layer thickness.According to the 5-point method,16 key sound transmission structures of the middle ear,including malleus,incus and stapes,as well as joints,ligaments and tendons,were evaluated for image quality scoring.The standard deviation(SD)value,signal noise ratio(SNR),and contrast noise ratio(CNR)of bone in the malleus region and intratympanic gas were measured and calculated on the two examination images.RESULTS Thirty patients(47 sides)with normal middle ear structure were included,including 18 males and 12 females.The two physicians compared the results of U-HRCT in showing malleus head,malleus neck,malleus handle,incus body,long process,and short process,5 points accounted for 100%,and the 5-point scores of incudomalleolar joint space,incudostapedial joint space,stapes footplate and annular ligament were 100%,98.29%,75.83%and 77.83%,respectively,which were significantly higher than those of MSCT(P<0.001).In addition,U-HRCT showed higher scores for lenticular process,stapes head,anterior arch of stapes,posterior arch of stapes,annular ligament,stapes muscle,and tendo musculi tensoris tympani than MSCT(P<0.001),and the lenticular process showed a 100%display rate.There was no significant difference in the SNR between the two groups(P>0.05),but the SD value of the malleus in U-HRCT was 161.6±36.4,which was significantly lower than that in MSCT(297.8±128.1),and the difference was statistically significant(P<0.001).CONCLUSION U-HRCT can clearly visualize the key sound transmission structures of the middle ear,and its visualization ability is significantly better than that of MSCT.

Objective:To evaluate the relationship between transverse sinus stenosis (TSS) and cerebral blood flow in normal adults based on four-dimensional flow (4D Flow) MRI.Methods:The study was a cross-sectional study. Totally 81 normal volunteers who underwent magnetic resonance venography (MRV) and 4D Flow MRI were prospectively enrolled at Beijing Friendship Hospital, Capital Medical University from January 2020 to December 2022. Based on MRV evaluation of transverse sinus dysplasia, the volunteers were divided into a dysplasia group (26 cases) and a non-dysplasia group (55 cases); The area of the stenosis and the normal transverse sinus at the distal end were measured. The degree of TSS and the bilateral average transverse sinus stenosis (BA-TSS) were calculated. TSS was determined using TSS levels of 1/3, 1/2, and 2/3 as thresholds, and was divided into three groups: no TSS group, unilateral TSS group, and bilateral TSS group, with 28, 39, and 14 cases, 37, 37, and 7 cases, and 43, 36, and 2 cases, respectively. Based on 4D Flow MRI, the blood flow of the internal carotid artery, vertebral artery, superior sagittal sinus, straight sinus, and transverse sinus distal and proximal ends were measured. The cerebral blood flow (bilateral internal carotid artery blood flow+bilateral vertebral artery blood flow), venous sinus return blood flow 1 (superior sagittal sinus blood flow+straight sinus blood flow), return blood flow 2 (sum of bilateral transverse sinus distal end blood flow), return blood flow 3 (sum of bilateral transverse sinus proximal end blood flow), and the ratio of return blood flow to cerebral blood flow were calculated. Independent sample t-test was used to compare the differences between the group with and without transverse sinus dysplasia; Single factor analysis of variance was used to compare the differences between the TSS free group, unilateral TSS group, and bilateral TSS group. Based on single factor linear regression, the relationships between BA-TSS and blood flow parameters were analyzed.Results:There was no statistically significant difference in various blood flow parameters between the group with and without transverse sinus dysplasia (all P>0.05). When using 1/3, 1/2, and 2/3 as thresholds, there was no statistically significant difference in various blood flow parameters between the non TSS group, unilateral TSS group, and bilateral TSS group (all P>0.05). BA-TSS was linearly positively correlated with cerebral blood flow (β=0.986, 95% CI 0.108-1.865, P=0.028), but not linearly correlated with return blood flow 1, 2, and 3 (all P>0.05). The degree of BA-TSS was linearly negatively correlated with return blood flow 1/cerebral blood flow (β=-0.001, 95% CI -0.002-0, P=0.009) and return blood flow 2/cerebral blood flow (β=-0.001, 95% CI -0.002-0, P=0.018), but not with return blood flow 3/cerebral blood flow ( P=0.076). Conclusion:The BA-TSS degree in normal adults is positively correlated with cerebral blood inflow and negatively correlated with the proportion of venous sinus outflow.