To evaluate the application value of the Peyton four-step teaching method in the standardized training of intestinal ultrasound and compare it with traditional teaching methods, so as to provide an optimized approach for clinical ultrasound training.

Objective To analyze the risk factors for upper arm ports(UAP)related infections and develop a nomogram for predicting the UAP related infections.Methods Patients(n=6 028)with UAP between 2014 and 2023 in Renji Hospital,Shanghai Jiao Tong University School of Medicine were included and assigned to a training set(n=4 219)or a validation set(n=1 809).Least Absolute Shrinkage and Selection Operator(LASSO)regression were built and non-zero factors were screened out.Multivariate logistic regression was performed for these non-zero factors to screen significant factors out for constructing a prediction model.The performance of the model was evaluated by the area under curve(AUC)of the receiver operating characteristic(ROC)curves,calibration curves,the decision curve analysis(DCA)curve,and clinical impact curves(CICs)in both training set and validation set.Results The model incorporated gender,venous access,venous status,catheter-related thrombosis(CRT),and diameter of catheter.The model performed well.The AUC of ROC was 0.801 in the training set and 0.746 in the validation set.The calibration curve was close to the ideal curve,indicating good discriminative ability of the model.The DCA curve suggested that the model could help make beneficial clinical decisions when the risk assessment value was 30％-41％.CICs proved that the model had good clinical value.Conclusions A model was successfully constructed to predict UAP-related infections.The brachial/basilic vein and 5F catheter was proposed as the first choice.Thicker catheter diameter,male,CRT,abnormal venous status,and axillary vein approach may increase the risk of UAP related infection.

Objective To analyze the risk factors for upper arm ports(UAP)related infections and develop a nomogram for predicting the UAP related infections.Methods Patients(n=6 028)with UAP between 2014 and 2023 in Renji Hospital,Shanghai Jiao Tong University School of Medicine were included and assigned to a training set(n=4 219)or a validation set(n=1 809).Least Absolute Shrinkage and Selection Operator(LASSO)regression were built and non-zero factors were screened out.Multivariate logistic regression was performed for these non-zero factors to screen significant factors out for constructing a prediction model.The performance of the model was evaluated by the area under curve(AUC)of the receiver operating characteristic(ROC)curves,calibration curves,the decision curve analysis(DCA)curve,and clinical impact curves(CICs)in both training set and validation set.Results The model incorporated gender,venous access,venous status,catheter-related thrombosis(CRT),and diameter of catheter.The model performed well.The AUC of ROC was 0.801 in the training set and 0.746 in the validation set.The calibration curve was close to the ideal curve,indicating good discriminative ability of the model.The DCA curve suggested that the model could help make beneficial clinical decisions when the risk assessment value was 30％-41％.CICs proved that the model had good clinical value.Conclusions A model was successfully constructed to predict UAP-related infections.The brachial/basilic vein and 5F catheter was proposed as the first choice.Thicker catheter diameter,male,CRT,abnormal venous status,and axillary vein approach may increase the risk of UAP related infection.

Objective:To investigate the feasibility and value of nomogram based on base line clinical and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics for pretreatment predicting the depth of myometrial invasion and tumor grade of endometrioid endometrial carcinoma (EEC).Methods:Preoperative baseline clinical characteristics and DCE-MRI characteristics of 194 EEC patients were prospectively collected at Obstetrics and Gynecology Hospital, Fudan University from October 2020 to January 2022 and used as a training set. Univariate analysis was conducted to compare baseline clinical characteristics and DCE-MRI quantitative parameters [including tumor volume, and mean, median, and standard deviation of volume transfer constant (K trans), rate constant (K ep), extravascular extracellular volume fraction (V e), and initial area under the enhancement curve (iAUC)] between patients with deep myometrial invasion (DMI) and those with superficial myometrial invasion (SMI), as well as between high-grade and low-grade EEC. Multivariate logistics regression analysis was used to identify independent predictors for the construction of nomogram. An independent external testing set comprising 127 EEC patients was retrospectively collected from Zhongshan Hospital, Fudan University and Zhongshan Hospital, Fudan University (Xiamen Branch). The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used for evaluating the model′s predictive performance and clinical net benefit, respectively. Results:(1) The depth of myometrial invasion: univariate analysis showed that in the training set, the EEC patients with DMI differed significantly from those with SMI in clinical characteristics including higher proportion of postmenopausal state and overweight [body mass index (BMI)≥25 kg/m2], and abnormal levels of serum cancer antigen (CA) 125, CA 199, and human epididymis protein 4 (HE4), and in DCE-MRI quantitative parameters including tumor volume, and median, mean, and standard deviation of K trans, median of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, CA 199, tumor volume, and mean of iAUC were independent predictors of the depth of myometrial invasion, and constructed the nomogram (recorded as Nomogram_1), achieving an AUC of 0.861 (95% CI: 0.803-0.919) in the training set. In the independent external testing set, the AUC was 0.876 (95% CI: 0.815-0.938), with corresponding sensitivity of 82.0%, specificity of 80.7%, accuracy of 81.1%, positive predictive value (PPV) of 65.3%, and negative predictive value (NPV) of 91.0% for predicting DMI. (2) The EEC grade: univariate analysis showed that in the training set, high-grade EEC patients differed significantly from low-grade EEC in clinical characteristics including patient′s age, the proportion of postmenopausal state and overweight, and abnormal levels of serum CA 125, and in DCE-MRI quantitative parameters including tumor volume, median, mean, and standard deviation of K trans, median and mean of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, tumor volume, and median of V e emerged as independent predictors of EEC grade, and constructed the nomogram (recorded as Nomogram_2), achieving an AUC of 0.845 (95% CI: 0.786-0.893) in the training set. While in the external testing set, the AUC was 0.819 (95% CI: 0.744-0.894), with corresponding sensitivity of 72.4%, specificity of 72.4%, accuracy of 72.4%, PPV of 43.8%, and NPV of 89.9% for predicting high-grade EEC. (3) The DCA curves demonstrated that both Nomogram_1 and Nomogram_2 yielded obvious positive clinical net benefits across a wide range of threshold probabilities. Conclusion:The nomogram based on pretreatment clinical and DCE-MRI characteristics has the potential to noninvasive predict the depth of myometrial invasion and grade of EEC, providing valuable reference information for clinical management decision-making.

Objective:To investigate the feasibility and value of nomogram based on base line clinical and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) characteristics for pretreatment predicting the depth of myometrial invasion and tumor grade of endometrioid endometrial carcinoma (EEC).Methods:Preoperative baseline clinical characteristics and DCE-MRI characteristics of 194 EEC patients were prospectively collected at Obstetrics and Gynecology Hospital, Fudan University from October 2020 to January 2022 and used as a training set. Univariate analysis was conducted to compare baseline clinical characteristics and DCE-MRI quantitative parameters [including tumor volume, and mean, median, and standard deviation of volume transfer constant (K trans), rate constant (K ep), extravascular extracellular volume fraction (V e), and initial area under the enhancement curve (iAUC)] between patients with deep myometrial invasion (DMI) and those with superficial myometrial invasion (SMI), as well as between high-grade and low-grade EEC. Multivariate logistics regression analysis was used to identify independent predictors for the construction of nomogram. An independent external testing set comprising 127 EEC patients was retrospectively collected from Zhongshan Hospital, Fudan University and Zhongshan Hospital, Fudan University (Xiamen Branch). The area under the receiver operating characteristic curve (AUC) and decision curve analysis (DCA) were used for evaluating the model′s predictive performance and clinical net benefit, respectively. Results:(1) The depth of myometrial invasion: univariate analysis showed that in the training set, the EEC patients with DMI differed significantly from those with SMI in clinical characteristics including higher proportion of postmenopausal state and overweight [body mass index (BMI)≥25 kg/m2], and abnormal levels of serum cancer antigen (CA) 125, CA 199, and human epididymis protein 4 (HE4), and in DCE-MRI quantitative parameters including tumor volume, and median, mean, and standard deviation of K trans, median of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, CA 199, tumor volume, and mean of iAUC were independent predictors of the depth of myometrial invasion, and constructed the nomogram (recorded as Nomogram_1), achieving an AUC of 0.861 (95% CI: 0.803-0.919) in the training set. In the independent external testing set, the AUC was 0.876 (95% CI: 0.815-0.938), with corresponding sensitivity of 82.0%, specificity of 80.7%, accuracy of 81.1%, positive predictive value (PPV) of 65.3%, and negative predictive value (NPV) of 91.0% for predicting DMI. (2) The EEC grade: univariate analysis showed that in the training set, high-grade EEC patients differed significantly from low-grade EEC in clinical characteristics including patient′s age, the proportion of postmenopausal state and overweight, and abnormal levels of serum CA 125, and in DCE-MRI quantitative parameters including tumor volume, median, mean, and standard deviation of K trans, median and mean of V e, as well as median, mean, and standard deviation of iAUC (all P<0.05). Multivariate analysis showed that the patient′s menstrual status, BMI, tumor volume, and median of V e emerged as independent predictors of EEC grade, and constructed the nomogram (recorded as Nomogram_2), achieving an AUC of 0.845 (95% CI: 0.786-0.893) in the training set. While in the external testing set, the AUC was 0.819 (95% CI: 0.744-0.894), with corresponding sensitivity of 72.4%, specificity of 72.4%, accuracy of 72.4%, PPV of 43.8%, and NPV of 89.9% for predicting high-grade EEC. (3) The DCA curves demonstrated that both Nomogram_1 and Nomogram_2 yielded obvious positive clinical net benefits across a wide range of threshold probabilities. Conclusion:The nomogram based on pretreatment clinical and DCE-MRI characteristics has the potential to noninvasive predict the depth of myometrial invasion and grade of EEC, providing valuable reference information for clinical management decision-making.

Objective To evaluate the value of an optimized three-dimensional inversion-recovery with real reconstruction (3D-real IR) sequence with a longer repetition time (TR, 16 000 ms) based on modulated flip angle technique in refocused imaging with extended echo train (MATRIX) in the endolymphatic hydrops (EH) imaging after intratympanic gadolinium (Gd) administration, and to compare it with a conventional 3D-real IR based on the turbo spin echo (TSE) sequence. Methods From July 2021 to November 2022, twenty-seven patients received both the conventional and optimized 3D-real IR sequences after bilateral intratympanic Gd administration. Images of the two sequences were qualitativly evaluated and compared. Contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and area ratio of endolymph against the total lymphatic space from the two sequences were measured and compared. Results 14(25.9%) ears with insufficient contrast for the EH diagnosis on the conventional sequence were clearly displayed on the optimized sequence. Image score, CNR and SNR of the optimized sequence were significantly higher than those of the conventional sequence (P ＜ 0.001). The scanning time of two sequences was similar. The area ratio of endolymph against the total lymphatic space in the cochlear was significantly higher on the conventional 3D-real IR than that on the optimized 3D-real IR (P ＜ 0.001); there was no statistical difference in the vestibule between the two sequences. Conclusions Compared with conventional sequence, optimized 3D-real IR sequence with a longer TR may be better for evaluation of EH after intratympanic Gd administration.

Objective To investigate the value of artificial intelligence-assisted compressed sensing (ACS) technology for intravenous gadolinium contrast-enhanced magnetic resonance imaging of the inner ear using three-dimensional fluid-attenuated inversion recovery (3D-FLAIR) sequence. Methods The patients received gadolinium contrast-enhanced magnetic resonance imaging using ACS and united compressed sensing (uCS) 3D-FLAIR at Zhongshan Hospital, Fudan University from January to November 2024 were prospectively enrolled. The repetition time was 16 000 ms, and acquisition time was 6 min 40 s and 10 min 24 s in ACS 3D-FLAIR and uCS 3D-FLAIR, respectively. The images on the two sequences were evaluated independently by two radiologists. The image quality of the two sequences was subjectively evaluated and compared. The signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were compared between the two sequences. The grading consistencies using two sequences and between the two doctors were analyzed. Results There was no statistically difference in subjective score of image quality between the two sequences. SNR and CNR of the ACS 3D-FLAIR sequence were significantly higher than those of the uCS 3D-FLAIR sequence (P＜0.001). The kappa values of grades of cochlear and vestibular endolymphatic hydrops were 0.942 and 0.888 using two sequences (P＜0.001). The kappa values of grades of cochlear and vestibular endolymphatic hydrops using the ACS 3D-FLAIR sequence between the two doctors were 0.784 and 0.831, respectively (P＜0.001); the kappa values of grades of cochlear and vestibular endolymphatic hydrops using uCS 3D-FLAIR sequence between the two doctors were 0.725 and 0.756, respectively (P＜0.001). Conclusions ACS 3D-FLAIR could provide higher SNR and CNR than uCS 3D-FLAIR, and is more suitable for intravenous gadolinium contrast-enhanced magnetic resonance imaging of the inner ear; the endolymphatic hydrops grades using ACS 3D-FLAIR is similar to use uCS 3D-FLAIR.

Objective To analyze the risk factors for catheter-related thrombosis(CRT)in the upper arm infusion port(UAP)and to construct a machine-learning prediction model.Methods A total of 6028 patients,who received UAP implantation at Shanghai Renji Hospital of China from February 2014 to February 2023,were enrolled in this study.The patients were divided into training set(n=4 219)and validation set(n=1 809).Six machine-learning prediction models,including Least Absolute Shrinkage and Selection Operator(LASSO)regression,random forest,decision tree,neural network,XGBoost and logistic,were constructed,and the model having best performance was selected as the optimal model.SHapely Additive exPlanations(SHAP)analysis was used to explain the neural network model,and DALEXtra package was used to explain the continuous variables.Results The neural network model was chosen as the final model.The variables,in order of the degree of importance from high to low,included sex,the diameter of catheter,catheter tip confirmation method,the length of catheter,inpatient or outpatient status,history of central venous catheter implantation,the length of subcutaneous tunnel,age,body mass index(BMI),primary tip displacement,and left or right venous approach.The learning curve,i.e.the area under curve(AUC)of the receiver operating characteristic(ROC)curve,for the training set was＞0.6,and the Delong testing and Bootstrap Methods Test showed that the neural network model performed well(P＜0.05).The Kolmogorov-Smirnov plot(KS plot)value was 0.313 5,indicating that the model had the good ability of discrimination.The clinical impact curve(CIC)assessment revealed that the model had good clinical value.Conclusion The machine-learning prediction model of upper arm infusion port with CRT has been successfully constructed.For minimizing the risk of CRT,it is recommended to prioritize the use of 5 F diameter catheters,adopt left-sided venous approach and positioning the tip of the catheter based on anatomical measurements,besides,the catheter length should be not shorter than 36.56 cm,and the subcutaneous tunnel length should not be less than 5 cm.The basic features associated with higher CRT risk include age of 50-65 years,BMI being between 18.69 kg/m2 and 20.81 kg/m2 or between 23.68 kg/m2 and 23.94 kg/m2 and male.

Tuberous sclerosis complex(TSC)is a rare genetic disease that can lead to benign dysplasia in multiple organs such as the skin, brain, eyes, oral cavity, heart, lungs, kidneys, liver, and bones. Its main symptoms include epilepsy, intellectual disabilities, skin depigmentation, and facial angiofibromas, whilst incidence is approximately 1 in 10 000 to 1 in 6000 newborns. This case presents a middle-aged woman who initially manifested with epilepsy and nodular depigmentation. Later, she developed a lower abdominal mass, elevated creatinine, and severe anemia. Based on clinical features and whole exome sequencing, the primary diagnosis was confirmed as TSC. Laboratory and imaging examinations revealed that the lower abdominal mass originated from the uterus. CT-guided biopsy pathology and surgical pathology suggested a combination of leiomyoma and abscess. With the involvement of multiple organs and various complications beyond the main diagnosis, the diagnostic and therapeutic process for this patient highlights the importance of rigorous clinical thinking and multidisciplinary collaboration in the diagnosis and treatment of rare and challenging diseases.

Objective To observe the value of 5.0T MR susceptibility weighted imaging(SWI)for displaying cerebral small veins and detecting cerebral microbleeds(CMB).Methods Head MR examinations were prospectively performed using both 3.0T and 5.0T MR scanner in 30 stroke patients suspected caused by cerebral small vessel disease.The image quality,effect of displaying cerebral small veins and detecting CMB were compared between 3.0T and 5.0T SWI.Results The image quality scores,signal-to-noise ratios,contrast-to-noise ratios,scores of displaying deep cerebral veins and subcortical veins,the counts of detecting CMB and iron deposition on cortical surface of 5.0T SWI were all higher than those of 3.0T SWI(all P＜0.05).High consistency of CMB positions was found between 3.0T and 5.0T SWI(Kappa=1.0).Conclusion The effect of 5.0T MR SWI for displaying cerebral small veins and detecting cerebral microbleeds were better than 3.0T MR SWI,which could be used to assess stroke caused by cerebral small vascular disease.