Objective To analyze the computed tomography(CT)imaging features of urachal carcinoma and evaluate its diagnostic value.Methods The clinical data of 20 patients with urachal carcinoma confirmed by surgery and pathology,who were admitted to The First Affiliated Hospital of Naval Medical University from Dec.2012 to Dec.2022,were collected.Seventeen of the 20 patients underwent enhanced CT urography and 3 underwent pelvic CT plain scan+enhanced scan.After scanning,multiplanar reconstruction was performed on the post-processing workstation.The general data,clinical symptoms,CT imaging findings,pathological data,and prognosis of the patients were analyzed and summarized.Results The patients included 16 males and 4 females,aged 27 to 75 years old,with a median age of 61.50(41.50,71.25)years old.The tumors were all located in the anterior wall of the bladder,along the urachus,with a maximum diameter of 1.72-5.55 cm and a median maximum diameter of 3.34(2.48,3.71)cm.Fourteen cases had cystic-solid lesions and 6 had solid lesions.In the cystic-solid lesions,9 cases showed the"upper cystic and lower solid"sign on the sagittal plane.Calcification was noted in 17 cases.After enhanced scanning,18 cases showed progressive enhancement,and 2 cases showed"fast in and fast out"enhancement.Tumor invasion extended beyond the urachus and/or bladder muscle layer in 19 cases.At the end of follow-up,3 cases had recurrence,2 had metastasis,5 had no recurrence after surgery,3 died,and 7 were lost to follow-up.Conclusion Urachal carcinoma has certain characteristic manifestations on CT imaging.Reconstructing the sagittal plane with enhanced CT scanning and multiplanner reformation can help preoperative diagnosis and prognostic evaluation of urachal carcinoma.

Pancreatic extracorporeal shock wave lithotripsy(P-ESWL),a non-invasive treatment,is widely accepted worldwide as the preferred option for pancreatic duct stone(PDS)treatment.P-ESWL provides significant relief of painful symptoms and improves patients' quality of life through efficient lithotripsy and catheter removal.Although there is risk of post-operative complications such as pancreatitis,the overall incidence is low and can be further minimized by effective management strategies.It is worth noting that computed tomography-based quantitative analysis and radiomics prediction model provide a scientific basis for personalized P-ESWL,heralding more precise and efficient treatment in the future.P-ESWL for treating PDS will be further improved by future multi-center and large-sample studies,as well as by the integration of artificial intelligence and machine learning algorithms,which may lead to significant therapeutic effects and improvements in patients' quality of life.

Objective To explore the correlation between stroke etiology and clinical and imaging features in patients with acute ischemic stroke(AIS)due to large vessel occlusion treated by intravascular thrombectomy.Methods A total of 213 patients with AIS and endovascular embolectomy in our hospital from Oct.2016 to Jun.2018 were enrolled retrospectively.According to the etiological classification criteria of Trial of Org 10172 in Acute Stroke Treatment(TOAST),there were 116 cases of cardioembolism and 97 cases of non-cardioembolism.Multivariate logistic regression analysis was used to screen the clinical and imaging characteristics for identifying cardioembolism and non-cardioembolism.Results Compared with non-cardioembolism AIS,cardioembolism AIS was associated with higher NIHSS scores(adjusted odds ratio[OR]=1.09,95%confidence interval[95%CI]1.01-1.18,P=0.02),atrial fibrillation(adjusted OR=76.46,95%CI 26.75-218.51,P＜0.01),absence of hypertension(adjusted OR=0.32,95%CI 0.12-0.84,P=0.02),antiplatelet drug use(adjusted OR=5.03,95%CI 1.22-20.63,P=0.03),shorter onset-to-puncture time(adjusted OR=0.998,95%CI 0.996-1.000,P=0.04),and presence of hyperdense artery sign(HAS)(adjusted OR=4.45,95%CI 1.47-13.49,P=0.01).Conclusion There are some differences in clinical and imaging characteristics between patients with cardioembolism and non-cardioembolism AIS.The occurrence of HAS suggests a higher probability of cardioembolism in AIS patients.

Objective To identify risk factors for pathological upgrading after radical prostatectomy in patients with biopsy-confirmed International Society of Urological Pathology(ISUP)grade 1 prostate cancer and to develop a predictive nomogram.Methods A total of 256 patients with ISUP grade 1 prostate cancer diagnosed by biopsy and undergoing radical prostatectomy in The First Affiliated Hospital of Naval Medical University between Jan.2017 and May 2024 were retrospectively enrolled.Clinical,imaging,and biopsy data were collected.Independent predictors were identified using univariate and multivariate binary logistic regression,and a nomogram model was constructed.Model performance was evaluated using receiver operating characteristic curve,clinical impact curve,and decision curve analysis.The stability of the model was evaluated by Hosmer-Lemeshow test.Results Multivariate binary logistic regression analysis revealed that the number of positive puncture cores(odds ratio[OR]=1.80),prostate imaging and reporting data system(PI-RADS)score(OR=1.88),and prostate specific antigen density(PSAD)stage(OR=1.43)were independent predictors of pathological upgrading(all P＜0.01).The area under curve(AUC)value of the nomogram model based on the above 3 predictors was 0.82(95％confidence interval 0.77-0.87).Decision curve analysis demonstrated favourable clinical utility within a threshold probability range of 0.01-0.99.Clinical impact curve analysis showed that at a threshold probability of 0.40,the model could avoid 45 unnecessary interventions(12％reduction in false-positive rate)with a net clinical benefit of 0.46.The Hosmer-Lemeshow test indicated good model fit(P=0.45).Conclusion The constructed nomogram model can accurately predict the risk of pathological upgrading after radical prostatectomy in patients with ISUP grade 1 prostate cancer,providing a quantitative tool to support individualized decision-making for active surveillance.

BACKGROUND:Establishing a nomogram prediction model for postoperative pulmonary infection in hip fractures and taking early intervention measures is crucial for improving patients'quality of life and reducing medical costs. OBJECTIVE:To construct a nomogram risk prediction model of postoperative pulmonary infection in elderly patients with hip fracture,and provide theoretical basis for feasible prevention and early intervention. METHODS:Case data of 305 elderly patients with hip fractures who underwent surgical treatment at Wuxi Traditional Chinese Medicine Hospital Affiliated to Nanjing University of Chinese Medicine between January and October 2020(training set)were retrospectively analyzed.Using univariate and multivariate logistic regression analysis and Hosmer-Lemeshow goodness of fit test,receiver operating characteristic curve was utilized to analyze the diagnostic predictive efficacy of independent risk factors and joint models for postoperative pulmonary infections.Tools glmnet,pROC,and rms in R Studio software were applied to construct a nomogram model for predicting the risk of postoperative pulmonary infection in elderly patients with hip fractures,and calibration curves were further drawn to verify the predictive ability of the nomogram model.Receiver operating characteristic curves,calibration curves,and decision curves were analyzed for 133 elderly patients with hip fractures(validation set)receiving surgery at the same hospital from November 2022 to March 2023 to further predict the predictive ability of the nomogram model. RESULTS AND CONCLUSION:(1)The postoperative pulmonary infection rate in elderly patients with hip fractures in this group was 9.18%(28/305).(2)Single factor and multivariate analysis,as well as forest plots,showed that preoperative hospitalization days,leukocyte count,hypersensitive C-reactive protein,and serum sodium levels were independent risk factors(P<0.05).The Hosmer-Lemeshow goodness of fit test showed good fit(χ2=4.57,P=0.803).Receiver operating characteristic curve analysis was conducted on the independent risk factors and their joint models mentioned above,and the differentiation of each independent risk factor and joint model was good,with statistical significance(P<0.05).(3)The graphical calibration method,C-index,and decision curve were used to validate the nomogram prediction model.The predicted calibration curve was located between the standard curve and the acceptable line,and the predicted risk of the nomogram model was consistent with the actual risk.(4)The validation set used receiver operating characteristic curve,graphic calibration method,and decision curve to validate the prediction model.The results showed good consistency with clinical practice,indicating that the model had a good fit.The nomogram risk prediction model constructed for postoperative pulmonary infection in elderly patients with hip fractures has good predictive performance.The use of the nomogram risk prediction model can screen high-risk populations and provide a theoretical basis for early intervention.

Objective To observe the feasibility of inducible costimulatory(ICOS)target in mice adjuvant-induced arthritis(AIA)models.Methods Twenty BALB/c mice were injected with equal dose of complete Freund's adjuvant(AIA group,n=10)or phosphate buffered saline(control group,n=10)into the right back paws.The second day after injection,ICOS-IRD680 mAb probes were injected in AIA group,while IgG-IRD680 mAb probes were injected in control group through tail vein,respectively.The fluorescent intensity ratio of the right and left paw based on near-infrared fluorescence imaging 24 and 48 h later were compared between groups.The total RNA of mice were extracted for transcriptome sequencing,and differentially expressed genes(DEG)were screened and analyzed.Primary T cells were extracted from the spleen of mice in control group,then magnetic negative T cells were sorted.Activated T cells were stimulated and induced using phoboxylate/ionomycin,the expression level of ICOS protein on the surface of activated T cells were detected,and the safety of probe was also evaluated.Results The expression of ICOS gene in AIA group was significantly up-regulated,and the proportion of T cells was higher than that in control group.ICOS tented to distribute in FoxP3+ regulatory T cells,CD8+T cells and CD4+T cells.The purity of CD3+T cells before and after magnetic negative T cells was 65.31％ and 90.14％,respectively.The proportion of CD4+T cells before and after activated was 7.14％ and 31.20％,respectively,and the mean fluorescent intensity of ICOS protein in activated CD4+T cells(586±25)was significantly higher than that in non-activated CD4+T cells(161±31)(t=25.390,P＜0.001).Twenty-four and 48 h after probe injection,the fluorescent intensity ratio of the right paw/left paw in AIA group was higher than that in control group(t=34.600,P＜0.001;t=23.380,P＜0.001).Compared with control group,no significant pathological change of heart,liver nor kidney tissues of mice in AIA group was detected,while no significant difference of glutamic-pyruvic transaminase,glutamic-oxaloacetic transaminase nor creatinine was found between groups(all P＞0.05).Conclusion ICOS target was safe and feasible for mice AIA models.

Objective:To investigate the value of machine learning model based on MRI in predicting the abundance of tumor infiltrating CD 8+ T cell and prognosis of pancreatic cancer patients. Methods:The clinical data of 156 patients with pathological confirmed pancreatic cancer who underwent pre-operative MRI within 7 days before surgery in the First Affiliated Hospital of Naval Medical University from January 2017 to April 2018 was retrospectively analyzed. According to the international consensus on the predictive model, a total of 116 patients from January to December 2017 were included in the training set, and a total of 40 patients from January to April 2018 were included in the validation set. With the overall survival of patients as the outcome variable, X-Tile software was used to obtain cut-off values of the percentage of CD 8+ T cells, and all patients were divided into CD 8+ T-high and -low groups. The clinical, pathological and radiological features were compared between two groups. 3D slicer software was used to draw the region of interest in each layer of the primary MR T 1- and T 2-weighted imaging, arterial phase, portal venous phase, and delayed phase images for tumor segmentation. Python package was applied to extract the radiomics features of pancreatic tumors after segmentation and the extracted features were reduced and chosen using the least absolute shrinkage and selection operator (Lasso) logistic regression algorithm. Lasso logistic regression formula was applied to calculate the rad-score. The extreme gradient boosting (XGBoost) were used to construct the machine learning predicted model. The models′ performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value. Results:The cut-off value of the CD 8+ T-cell level was 19.09% as determined by the X-tile program. Patients in the high CD 8+ T cell group had a longer median survival than those in the low CD 8+ T cell group (25.51 month vs 22.92 month, P=0.007). The T stage in the training set and tumor size in the validation set significantly differed between the groups (all P value <0.05). A total of 1 409 radiomics features were obtained, and 19-selected features associated with the level of CD 8+ T cell were determined after being reduced by the Lasso logistic regression algorithm. The rad-score was significantly lower in the CD 8- high group (median: -0.43; range: -1.55 to 0.65) than the CD 8- low group (median: 0.22; range: -0.68 to 2.54, P<0.001). The prediction model combined the radiomics features and tumor size. In the training set, the AUC, sensitivity, specificity, accuracy, and positive and negative predictive value were 0.90 (95% CI 0.85-0.95), 75.47%, 90.48%, 0.84, 0.87, and 0.81. In the validation set, the AUC, sensitivity, specificity, accuracy, and positive and negative predictive value were 0.79 (95% CI 0.63-0.96), 90.00%, 80.00%, 0.85, 0.82, and 0.89. The predictive model can accurately distinguish patients with high and low CD 8+ T cells in pancreatic cancer. Conclusions:The radiomics-based machine learning model is valuable in predicting the CD 8+ T cells infiltrating level in pancreatic cancer patients, which could be useful in identifying potential patients who can benefit from immunotherapies.

Objective:To develop and verify a predictive model based on CT characteristics for predicting infected walled-off necrosis (IWON) in MSAP and SAP patients.Methods:The clinical and CT data of 1 322 patients diagnosed as MSAP and SAP according to the 2012 Atlanta revised diagnostic criteria in the First Affiliated Hospital of Naval Medical University from January 2015 to December 2020 were continuously collected. Finally, 126 patients who underwent enhanced CT scans within 3 days after admission and percutaneous catheter drainage of WON during hospitalization were enrolled. Among them, there were 63 MSAP and 63 SAP patients. According to the results of the culture from drainage fluid, the patients were divided into sterile walled-off necrosis group (SWON group, n=31) and infected walled-off necrosis group (IWON group, n=95). Patients were divided into training set (18 patients with SWON and 74 patients with IWON from January 2015 to December 2018) and validation set (13 patients with SWON and 21 patients with IWON from January 2019 to December 2020). Univariate and multivariate logistic regression analysis were performed to establish a model for predicting IWON. The model was visualized as a nomogram. The receiver operating characteristic curve (ROC) was drawn. The predictive efficacy of the model was evaluated by the area under the curve (AUC), sensitivity, specificity and accuracy, and the clinical application value was judged by decision curve analysis (DCA). Results:Univariate regression analysis showed that age, etiology, WON with bubble sign and the lowest CT value of WON were significantly associated with IWON. Multivariate logistic regression analysis showed that older age, biliary acute pancreatitis, WON with bubble sign, and the greater minimum CT value of WON were independent predictors for IWON. The formula for the prediction model was 0.12+ 0.01 age-0.75 hyperlipidemia-1.62 alcoholic-2.62 other causes+ 19.18 WON bubble sign+ 0.10 minimum CT value of WON. The AUC, sensitivity, specificity, and accuracy of the model were 0.85 (95% CI 0.76-0.94), 67.57%, 88.89%, and 71.74% in the training set and 0.78(95% CI0.62-0.94), 66.67%, 84.62%, and 73.53% in the validation set, respectively. The decision analysis curve showed that when the nomogram differentiated IWON from SWON at a rate greater than 0.38, using the nomogram could benefit the patients. Conclusions:The prediction model established based on CT characteristics might non-invasively and accurately predict the presence or absence of IWON in MSAP and SAP patients, and provide a basis for guiding treatment and evaluating prognosis.

Objective:To analyze the medical imaging in misdiagnosing serous cystic neoplasm(SCN) of the pancreas with pancreatic duct dilatation as other pancreatic lesions.Methods:Data of 21 patients with SCN and pancreatic duct dilatation who underwent surgical resection from January 2011 to November 2021 at the First Affiliated Hospital of Naval Medical University were retrospectively analyzed. There were 9 males and 12 females with ages ranging from 25 to 74, mean ± s. d. (57.4±13.4) years. The clinical features, surgical treatments, CT and MRI imaging features, and misdiagnosis were analyzed.Results:Of 11 patients who presented with abdominal pain, 1 patient had backache, 1 patient was jaundice, 1 patient had weight loss, 1 patinet had fatigue and 6 patients were asymptomatic. Ten patients were operated using pancreaticoduodenectomy, 8 distal pancreatectomy, 2 segmental pancreatectomy and 1 total pancreatectomy. For 11 patients, the lesion was located in the head of pancreas, and for 10 patients in the body and tail of pancreas. The tumor size was 23.0-92.0 (45.8±17.8) mm. All 21 patients had upstream pancreatic duct dilatation but no downstream pancreatic duct dilatation. The inner diameter of the pancreatic duct was 4.0-11.0(7.1±2.0) mm. Of 13 patients showed a low signal intensity on T 1-weighted imaging, 18 patients showed a markedly high signal intensity on T 2-weighted imaging, 13 patients showed no limitation on diffusion weighted imaging. Among the 11 patients who underwent CT examination, 5 patients were diagnosed to have intraductal papillary mucinous neoplesm (IPMN), 3 SCN, 1 pancreatic neuroendocrine tumor, 1 pancreatic cancer and 1 cyst. The misdiagnotic rate of CT was 72.7% (8/11). Among the 18 patients who underwent MRI examination, 9 patients were diagnosed to have IPMN, 3 mucinous cystic neoplasm, 3 SCN, 2 pancreatic cancer and 1 solid pseudopapillary tumor. The misdiagnosis rate of MRI was 83.3% (15/18). Conclusion:SCN with pancreatic duct dilatation was easily misdiagnosed as IPMN or other pancreatic solid tumors. The difference between SCN with pancreatic duct dilatation and IPMN was that the downstream pancreatic duct of SCN was normal. SCN showed a markedly high signal intensity on T 2-weighted imaging and no limitation on diffusion weighted imaging, which can help to distinguish SCN from other pancreatic solid tumors.

Objective:To investigate the clinical and imaging features of pancreatic intraductal oncocytic papillary neoplasm (IOPN).Methods:From January 2011 to August 2021, at the First Affiliated Hospital (Changhai Hospital) of Naval Medical University, 12 patients pathologically diagnosed with pancreatic IOPN after surgical resection were enrolled. Before operation, all patients underwent plain and enhanced computed tomography (CT) or magnetic resonance imaging (MRI). The clinical data (general conditions, main complaints, tumor related indicators and past medical history), CT and MRI features, surgical methods and pathologic results of the 12 patients with pancreatic IOPN were retrospectively analyzed. Descriptive method was used for statistical analysis.Results:Among 12 pancreatic IOPN patients, there were 7 males and 5 females, aged (54.0±13.0) years old (ranged from 31 to 75 years old). The symptoms were abdominal pain in 3 cases, jaundice in 1 case and 8 cases were detected during regular health checkups. Serum carbohydrate antigen 19-9 increased in 3 cases and carcinoembryonic antigen increased in 2 cases. One pancreatic IOPN patient with pancreatitis history and 3 pancreatic IOPN patients with diabetes history. Six cases were with the lesions located in the head of pancreas, 5 cases were located in the body and tail of pancreas and 1 case were diffused in the all the pancreas. Five cases were branch duct type, 2 cases were main duct type and 5 cases were mixed duct type. Ten pancreatic IOPN patients presented cystic or cystic-solid tumor, the maximum diameter (range) of the tumor was (50.3±31.1) mm (28 to 127 mm). The cyst walls of 6 patients were thickened and those of 9 patients were found with enhanced mural nodule or solid component, and none of them were growing outside the cystic wall. Two patients presented solid tumor located in the dilated pancreatic duct, and the maximum diameter (range) of the tumor was (25.5±0.5) mm (25 to 26 mm). The solid tumor demonstrated as slightly lower density on plain CT scan, lower signal on T1-weighted MRI imaging, high signal on T2-weighted MRI imaging, and limited diffusion on diffusion weighted imaging, and mild enhancement after CT and MRI enhanced scan. The main pancreatic duct dilated in 11 cases, and the inner diameter (range) was (10.5±8.1) mm (3 to 28 mm). The pancreatic parenchymal of 4 pancreatic IOPN patients was atrophy, 4 patients with calcification and 1 patient with lymphadenopathy. None of the 12 pancreatic IPON patients had peripheral blood vessel and tissue invasion. Six cases were received pancreaticoduodenectomy, 4 cases were underwent distal pancreatectomy, 2 cases underwent total pancreatectomy. The pathological classification of 7 pancreatic IOPN patients was invasive carcinoma, 4 cases were with high-grade dysplasia and 1 case with low-grade dysplasia.Conclusion:The clinical features of pancreatic IOPN are atypical and the imaging findings are mostly solid or cystic-solid tumor, pancreatic duct dilation, solid component of tumor located in the dilated pancreatic duct, and no peripheral tissue invasion.