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 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 explore the imaging features of intraductal pancreatic neuro-endocrine tumor (PNET).Methods:The retrospective and descriptive study was conducted. The clinicopathological data of 17 patients with intraductal PNET who were admitted to the First Affiliated Hospital of Naval Medical University (Changhai Hospital of Shanghai) from January 2013 to October 2020 were collected. There were 7 males and 10 females, aged (47±13)years. Preoperative contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI) of the pancreas was performed on patients. Observation indicators: (1) imaging features of intraductal PNET, including ① imaging features of CT and ② imaging features of MRI; (2) treatment and histopathological examination of intraductal PNET. Measurement data with normal distribution were described as Mean± SD and count data were described as absolute numbers. Results:(1) Imaging features of intraductal PNET. ① Imaging features of CT: 17 patients underwent preoperative contrast-enhanced CT of pancreas. There were 9 cases with tumor located in the head of the pancreas, 5 cases with tumor located in the neck of the pancreas and 3 cases with tumor located in the body and tail of the pancreas. The tumor diameter of the 17 patients was (8.7±2.5)mm, with a range of 5.2?15.5 mm. The tumor shape was round-like in the 17 patients. All the 17 patients showed isodensity on plain CT and markedly enhancement in arterial, venous and portal phases on enhanced CT. The degree of enhancement of tumor was higher than surrounding normal pancreatic parenchyma. All tumors of 17 patients were located at the truncation of main pancreatic duct (MPD) dilation, showing abrupt change in caliber of MPD without the "beak sign". The diameter of dilated MPD was (11.4±5.3)mm, with a range of 4.5?22.5 mm. Other imaging manifestations of the 17 patients included 11 cases with pancreatic parenchymal atrophy, 1 case with retention cyst, 1 case with choledochal dilation, 1 case with calcification, and all cases without cystic degeneration or hemorrhage. ② Imaging features of MRI: preoperative contrast-enhanced MRI was performed in 14 patients. Five cases showed slightly low signal but 9 cases showed unclear on T1-weighted imaging. Five cases showed low signal, 2 cases showed slightly high signal but 7 cases showed unclear on T2-weighted imaging. Of the 14 patients, 9 cases showed diffusion limited on diffusion weighted imaging and 5 cases showed unlimited diffusion. Nine cases showed marked enhancement in tumor higher than in normal pancreatic parenchyma, but 5 cases were unclear on contrast-enhanced MRI. (2) Treatment and histopathological exmination of intraductal PNET: all the 17 patients underwent surgical treatment, including 9 cases with pancreaticoduodenectomy, 4 cases with distal pancreatectomy and splenectomy, 4 cases with pancreatic segmentectomy. Postoperative histopatho-logical examination results showed 10 cases of G1 and 7 cases of G2, including 1 case of G2 with lymph node metastasis, 1 case of G2 with lymph node and liver metastasis. The pathological gross showed that the tumor body was mainly located in the pancreatic duct and blocked the pancreatic duct, with upstream pancreatic dilation. There were pancreatic acinar atrophy and fibrous tissue hyperplasia. The tumor was grayish-yellow or brownish red, solid, medium in texture and well-defined with the surrounding tissues. Microscopically, the tumor of 17 patients was mainly located in the pancreaic duct and invaded into surrounding pancreatic parenchyma. The cells of tumor were polygonal with a central nucleus, but the mitosis was rare. The cytoplasm was eosinophilic or hyaline. The tumor stroma was mainly collagen fiber with abundant capillary network.Conclusions:The imaging features of intraductal PNET are small size, marked enhancement on contrast-enhanced CT and MRI. The tumor obstructs the MPD with distal MPD dilation and pancreatic parenchyma atrophy.

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 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.

Objective:To investigate the imaging features of pancreatic mucinous cystic tumor (MCN) based on the European evidence-based guidelines on pancreatic cystic neoplasms and risk factors influencing tumor property.Methods:The retrospective case-control study was con-ducted. The clinicopathological data of 109 pancreatic MCN patients who were admitted to the First Affiliated Hospital of Naval Medical University (Changhai Hospital of Shanghai) from March 2011 to April 2021 were collected. There were 5 males and 104 females, aged (49±15)years. There were 97 cases with benign tumors and 12 cases with malignant tumors. Observation indicators: (1) clinical characteristics of MCN patients with different tumor properties; (2) imaging features of MCN patients with different tumor properties; (3) multivariate analysis of factors affecting evaluating tumor pro-perties of MCN. Measurement data with normal distribution were represented as Mean± SD, and comparison between groups was conducted using the t test. Measurement data with skewed distri-bution were represented as M(range), and comparison between groups was conducted using the Mann-Whitney U test. Count data were described as absolute numbers, and comparison between groups was performed using the chi-square test or Fisher exact probability. Comparison of ordinal data was analyzed using the non-parameter rank sum test. Statistically significant indicators in clinical and imaging characteristics were included in multivariate analysis. Multivariate analysis was performed by the Logistic regression model forward method. Results:(1) Clinical characteristics of MCN patients with different tumor properties. Gender(male, female), age, body mass index (BMI), cases with clinical symptoms (asymptomatically physical findings, abdominal pain and distension, emaciation, jaundice, pancreatitis, onset diabetes), cases with CA19-9 (<37 U/mL, ≥37 U/mL), cases with carcinoembryonic antigen (<5.0 μg/L, ≥5.0 μg/L), cases with surgical methods (pancreatoduo-denectomy, pancreatectomy of body and tail, segmental pancreatectomy), cases with tumor location (head of pancreas, tail of pancreas) were 4, 93, (47±14)years, (22±3)kg/m 2, 56, 35, 2, 1, 11, 5, 89, 8, 96, 1, 2, 90, 5, 4, 93 in the 97 cases with benign tumors, versus 1, 11, (59±17)years, (23±3)kg/m 2, 4, 4, 1, 0, 3, 2, 5, 7, 7, 5, 0, 12, 0, 0, 12 in the 12 cases with malignant tumors, showing significant differences in age, CA19-9 and carcinoembryonic antigen ( t=?2.69, χ2=22.57, 26.54, P<0.05) and showing no significant difference in gender, BMI, clinical symptoms, surgical methods and tumor location ( P>0.05) between them. (2) Imaging features of MCN patients with different tumor pro-perties. Of the 109 patients with pancreatic MCN, 85 cases underwent computed tomography (CT) plain and contrast-enhanced scan of pancreas, and 81 cases underwent magnetic resonance imaging (MRI) plain and contrast-enhanced scan of pancreas. There were 57 cases underwent both CT and MRI plain and contrast-enhanced scan of pancreas. Cases with tumor location (head of pancreas, tail of pancreas), cases with cyst morphology (circular, lobulated), cases with cyst diameter (<4 cm, ≥4 cm), diameter of cyst, cases with thickening of capsule wall, cases with calcification of capsule wall, cases with enhancing mural nodule of capsule wall, cases with pancreatic duct dilatation were 4, 93, 69, 28, 32, 65, 4.7(range, 3.3?6.8)cm, 38,20, 4, 13 in the 97 cases with benign tumors, versus 0, 12, 7, 5, 4, 8, 6.8(range, 3.3.?9.6)cm, 10, 2, 6, 4 in the 12 cases with malignant tumors, showing significant differences in thickening of capsule wall and enhancing mural nodule of capsule wall ( χ2=6.75, 21.75, P<0.05) and showing no significant difference in cyst morphology, cyst diameter, diameter of cyst, calcification of capsule wall and pancreatic duct dilatation ( P>0.05) between them. (3) Multivariate analysis of factors affecting evaluating tumor properties of pancreatic MCN. Result of multivariate analysis showed that age, carcinoembryonic antigen and mural nodule of capsule wall were independent factors affecting tumor properties of MCN ( odds Ratio=1.09, 19.67, 63.57, 95% confidence intervals as 1.01?1.18, 1.07?361.49, 4.07?993.49, P<0.05). Conclusions:Thickening of capsule wall and enhancing mural nodule of capsule wall are imaging features of patients with pancreatic MCN. Age, carcinoembryonic antigen and mural nodule of capsule wall are independent factors affecting tumor properties of pancreatic MCN.

Objective:To analyze the MRI findings of solid pseudopapilloma of the pancreas (SPTs) and nonfunctional pancreatic neuroendocrine tumors (PNETs), and to establish and verify the prediction model of SPTs and PNETs.Methods:The clinical and MRI data of 142 patients with SPTs and 137 patients with PNETs who underwent surgical resection and were confirmed by pathology in the First Affiliated Hospital of Naval Medical University from January 2013 to December 2020 were collected continuously. Age, gender, body mass index (BMI), lesion size, location, shape, boundary, cystic change, T 1WI signal, T 2WI signal, enhancement peak phase, whether the enhancement degree was higher than that of pancreatic parenchyma in the enhancement peak phase, enhancement pattern, whether pancreatic duct and common bile duct were dilated, whether the pancreas shrank, and whether it invaded adjacent organs and vessels were recorded. According to the international consensus on prediction model modeling, patients were divided into training set (106 SPTs and 100 PNETs between January 2013 and December 2018), and validation set (36 SPTs and 37 PNETs between January 2019 and December 2020). The above characteristics of patients in training and validation set were analyzed by univariate and multivariate logistic regression, and a prediction model was established to distinguish SPTs and PNETs, and then visualized as a nomogram. The receiver operating characteristic curve (ROC) of the nomogram of training set and verification set was drawn, and the area under the curve (AUC), sensitivity, specificity and accuracy were calculated to evaluate the prediction efficiency of the model, and the clinical application value of the prediction model was evaluated by decision curve analysis (DCA). Results:Univariate regression analysis showed that there were significant differences on age, gender, lesion size, shape, cystic change, T 1WI signal, peak phase of enhancement, degree of enhancement in peak phase, pattern of enhancement and invasion of adjacent organs between SPTs group and PNETs group (all P value <0.05). Multivariate regression analysis showed that the older age, male patients, the smaller lesion, no high signal on T 1WI, the enhancement peak phase located in arterial phase or venous phase, and the enhancement degree in peak phase higher than that of pancreatic parenchyma were the six independent predictors of PNETs. The prediction model was established by using these six factors and visualized as a nomogram. The formula for predicting PNETs probability was 4.31+ 1.13×age+ 1.31×tumor size-1.29×female-4.18×high T 1WI signal+ 1.28×the enhancement degree higher than that of pancreatic parenchyma -4.69 ×enhancement peak in delay phase. The prediction model was visualized as a nomogram. The AUC values in the training set and validation set were 0.99(95% CI0.977-1.000) and 0.97 (95% CI 0.926-1.000), respectively. The sensitivity, specificity and accuracy in the training set are 98.00%, 94.34% and 96.12% and in the validation set were 86.49%, 97.22% and 91.78% respectively. The results of decision curve analysis show that the prediction model can accurately diagnose SPTs and PNETs. Conclusions:The prediction model established in this study can accurately differentiate SPTs from PNETs, and can provide important information for clinical decision and prognosis.

Objective:To investigate the MRI features of intraductal papillary mucinous tumor (IPMN) of the pancreas and establish a prediction model for predicting the malignancy risk.Methods:The clinical data of 260 IPMN patients who underwent MRI and pathological confirmed in the First Affiliated Hospital of Naval Medical University from October 2012 to April 2020 were retrospectively analyzed. According to the pathological results, all patients were divided into benign group (including IPMN with low-grade dysplasia) and malignant group (including IPMN with high grade dysplasia and invasive carcinoma). According to international consensus of prediction model modeling, patients were divided into training set and validation set in chronological order. A prediction model was developed based on a training set consisting of 193 patients (including 117 patients with benign IPMN and 76 patients with malignant IPMN) between October 2012 and April 2019, and the model was validated in 67 patients (including 40 patients with benign IPMN and 27 patients with malignant IPMN) between May 2019 and April 2020. The multivariable logistic regression model was adopted to identify the independent predictive factors for IPMN malignancy and establish and visualized a nomogram. The ROC was drawn and AUC was calculated. The decision curve analysis was used to evaluate its clinical usefulness.Results:The IPMN type, cyst size, thickened cyst wall, mural nodule size, diameter of main pancreatic duct (MPD) and the abrupt change in the caliber of the MPD with distal pancreatic atrophy in the training set and validation set, and jaundice and lymphadenopathy in the training set were significantly different between benign group and malignant group ( P<0.05). The multivariable logistic regression model of characteristics included the jaundice, cyst size, mural nodule size ≥5 mm, the abrupt change in caliber of the MPD with distal pancreatic atrophy were independent risk factors for IPMN maligancy. The model for predicting IPMN malignancy was -0.35+ 2.28×(jaundice)+ 1.57×(mural nodule size ≥5 mm)+ 2.92×(the abrupt change in caliber of the MPD with distal pancreatic atrophy)-1.95×(cyst <3 cm)-1.05×(cyst≥3 cm). The individualized prediction nomogram using these predictors of the malignant IPMN achieved an AUC of 0.85 (95% CI 0.79-0.91) in the training set and 0.84 (95% CI 0.74-0.94) in the validation set. The sensitivity, specificity and accuracy of the training set were 72.37%, 85.47% and 80.31%, respectively. The sensitivity, specificity and accuracy of the validation set were 81.48%, 75.00% and 77.61%, respectively. The decision curve analysis demonstrated that when the IPMN malignancy rate was >0.16, the nomogram diagnosing IPMN could benefit patients more than the strategy of considering all the patients as malignancy or non-malignancy. Conclusions:The nomogram based on MRI features can accurately predict the risk of malignant IPMN, and can be used as an effective predictive tool to provide more accurate information for personalized diagnosis and treatment of patients.