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 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.

Objective:To explore the application value of single source dual energy CT (DECT) scanning technique in improving the image quality of the pancreas.Methods:Imaging data of 21 patients with normal pancreas and 36 patients with pancreas related diseases in the First Affiliated Hospital of Naval Medical University from July 2021 to August 2021 were collected. All the patients first underwent multi-slice CT (MDCT) scan with no-contrast, and then dynamic enhanced MDCT scan. And the DECT scan was used in the delay period. Virtual single energy images (VMI, 40~100keV) of normal pancreas and mixed energy images of pancreatic lesions (PI, 80 and 140kVp) were obtained. The regions of interest (ROI) of fat on abdominal wall, normal pancreas and abdominal aorta were delineated, the CT values and standard deviation (SD) of each ROI were measured and recorded, and the pancreatic signal-to-noise ratio (SNR) and contrast-to-noise ratio (SNR) of each energy image were calculated. The objective index and subjective score of VMI(40-100keV) and PI (80kVp and 140kVp) with iodine (water) base map and VMI best CNR were compared between groups. The correlation between VMI(40-100keV) and PI(80, 140kVp) with iodine (water) base map and VMIbest CNR was analyzed by univariate regression.Results:In VMI(40-100keV) of normal pancreas, the highest SNR value was VMI best CNR and iodine (water) base map, and the highest CNR values were VMI 60keV and iodine (water) base map. There were significant differences on SNR and CNR values between different energy VMI and iodine (water) base map ( P<0.05). Among the four images of PI 80kVp, PI 140kVp, VMI best CNR and iodine (water) base map for pancreatic lesions, the SNR and CNR values of iodine (water) base map were the highest. The SNR and CNR values of VMI best CNR were higher than those of PI 80kVp, and the differences were statistically significant ( P<0.05). The lesion significance and edge sharpness score of iodine (water) base map was the highest, which was better than other groups; the lesion significance and edge sharpness score of VMI best CNR was better than PI 140kVp, and the differences were statistically significant ( P<0.05). The results of univariate regression analysis showed that the SNR values of PI 80kVp, PI 140kVp and VMI best CNR for pancreatic lesions were positively correlated with those of the iodine (water) base map ( P<0.05), the CNR values of PI 140kVp and VMI best CNR images were positively correlated with the iodine (water) base map ( P<0.05), and the SNR and CNR values of PI 140kVp were positively correlated with VMI best CNR ( P<0.05). Conclusions:VMI with different energy and iodine (water) base maps can be obtained by single source DECT enhanced scanning of pancreas related diseases. The VMI best CNR was the best among all VMIs, while the SNR and CNR values of iodine (water) base maps were the highest in all images. The VMI best CNR and iodine (water) base maps can improve the image quality of pancreas related diseases.

Objective:To investigate the relationship between the perineural invasion score based on multidetector computed tomography (MDCT) and extrapancreatic perineural invasion (EPNI) in pancreatic ductal adenocarcinoma (PDAC).Methods:The clinical, radiological, and pathological data of 374 patients pathologically diagnosed as pancreatic cancer who underwent radical resection in the First Affiliated Hospital of Naval Medical University from March 2018 to May 2020 were analyzed retrospectively. Patients were divided into EPNI negative group ( n=111) and EPNI positive group (n=263) based on the pathological presence of EPNI. The perineural invasion score was performed for each patient based on radiological images. Univariate and multivariate logistic regression models were used to analyze the association between the perineural invasion score based on MDCT and EPNI in PDAC. Results:There were significant statistical differences between EPNI negative group and positive group on both pathological characteristics (T stage, N stage, invasion of common bile duct, and positive surgical margin) and radiological characteristics (tumor size, vascular invasion, lymph node metastasis, perineural invasion score based on MDCT, pancreatic border, parenchymal atrophy, invasion of duodenum, invasion of spleen and splenic vein and invasion of common bile duct) (all P value <0.05). Univariate analysis revealed that the tumor size, vascular invasion, lymph node metastasis, perineural invasion score based on MDCT, pancreatic border, pancreatic atrophy, invasion of duodenum, invasion of spleen and splenic vein and invasion of common bile duct were independently associated with EPNI. Multivariate analyses revealed that the perineural invasion based on MDCT was an independent risk factor for EPNI in pancreatic cancer (score=1, OR=2.93, 95% CI 1.61-5.32, P<0.001; score=2, OR=5.92, 95% CI 2.68-13.10, P<0.001). Conclusions:The perineural invasion score based on MDCT was an independent risk factor for EPNI in pancreatic cancer and can be used as an evaluation indicator for preoperative prediction of EPNI in PDAC.

Objective:To explore the differential diagnosis of pancreatic acinar cell carcinoma (PACC) and pancreatic ductal adenocarcinoma (PDAC) based on multidetector computed tomography (MDCT) features.Methods:The clinical, pathological and MDCT imaging data of 26 patients with pathologically confirmed PACC and 145 patients with pathologically confirmed PDAC who underwent MDCT from November 2013 to April 2021 were retrospectively studied. The differences of MDCT features including tumor location, tumor size, common pancreatic duct and bile duct dilatation, pancreatitis, lymph node metastasis, cyst, pancreatic parenchyma atrophy, duodenal involvement, bile ductal and vascular involvement between the two groups were compared. Univariate analysis and multivariate analysis by logistic regression models were performed to identify the independent predictive factors for PACC.Results:The tumor size, bile duct dilatation, lymph node metastasis, pancreatic parenchyma atrophy and vascular involvement were significantly different between PACC group and PDAC group (all P value<0.05). Multivariate analysis revealed that the tumor size ( OR=1.07, 95% CI 1.028-1.15, P=0.001), lymph node metastasis ( OR=0.23, 95% CI 0.065-0.800, P=0.02), pancreatic parenchyma atrophy ( OR=0.15, 95% CI 0.048-0.490, P=0.002) were closely associated with PACC. Conclusions:The tumor size, bile duct dilatation, lymph node metastasis, pancreatic parenchyma atrophy and vascular involvement evaluated by MDCT had a certain value in differentiating PACC from PDAC, and the tumor size, lymph node metastasis and pancreatic parenchyma atrophy were independent predictors for the diagnosis of PACC.

Objective:To summarize the MRI features of intrapancreatic accessory spleen (IPAS) and G1 grade pancreatic neuroendocrine neoplasms (PNENs), and clarify the radiological features for differential diagnosis.Methods:The data of 11 patients with IPAS confirmed by surgical pathology or 99mTc thermal denatured red blood cell imaging and 9 patients with G1 grade PNENs confirmed by surgical pathology in the tail of pancreas from January 2013 to December 2019 admitted in First Affiliated Hospital of Navy Medical University were retrospectively analyzed. MRI features of IPAS group and PNENs group, including shape, size, whether it protruded beyond the contour of the pancreas, cystic degeneration, plain scan of T 2WI, DWI signal, multistage enhancement mode, false capsule, etc. were studied and compared. Results:There was significantly statistical difference between the two groups in the terms of contour protrusion, T 2WI and DWI signals, multistage enhancement, and pseudomembrane (all P< 0.05). Protruded lesion was more common in the PNENs group (9/9 cases) than in the IPAS group (3/11). The T 2WI and DWI signals of lesions in the PNENs group were slightly higher than those in the IPAS group, and the proportion of high T 2WI and DWI signal lesions in the PNENs group was 6/9 cases and 4/9 cases, respectively, while the proportion of high T 2WI and DWI signal lesions in the IPAS group was 0/11 cases. Multistage enhancement of lesions in the PNENs group was more likely to be consistent (6/9), while lesions in the IPAS group were more inconsistent (10/11). In the PNENs group, all lesions showed false envelope after enhancement (9/9), while in the IPAS group, no false envelope was observed after enhancement (0/11). Conclusions:The presence of protruded lesions, the characteristics of T 2WI and DWI signals, the mode of multiphase enhancement and the false envelope were essential signs for differentiating IPAS and G1 grade PNENs.

Objective: To discuss the imaging features of solid-cystic pancreatic neuroendocrine tumors (PNEN). Methods: CT and MRI data of 38 pathologically diagnosed solid-cystic PNEN admitted in Changhai Hospital affiliated with Navy Medical University were retrospectively analyzed. The tumor location, major axis, shape, boundary, solid and cystic proportion, enhancement pattern, condition of cholangiopancreatic duct, vascular invasion, lymph nodes and organs metastasis were recorded, and the imaging features of PNEN were analyzed and summarized. Results: Of 38 PNEN patients, only one case had two lesions including one solid lesion and one solid-cystic lesion, and 37 cases had only one solid cystic lesion including 6 with mainly cystic component and 31 with solid-cystic mixture. 22 of 38 lesions were located in head or neck of pancreas, and 16 were in body or tail of pancreas. The minimum of major axis was 1.1 cm, and the maximum was 13.3 cm, and the average was 5.5 cm. There were 23 round-like tumors, 2 oval tumors, and 13 irregular lesions; 25 lesions with clear margin, 13 with unclear margin. CT scan detected iso- to hypodense lesions, and speckled, nodular, cambered or eggshell calcification in 10 cases. The lesions were mainly manifested as low signal in T₁WI, which were as inhomogeneous high signal and fluid high signal in T₂WI. The solid component of all the lesions was strengthened at different degree after enhancement. 25 lesions showed obvious enhancement that was higher than that of normal pancreatic parenchyma. 13 lesions had no significant contrast enhancement that was similar to or lower than pancreatic parenchyma. 8 patients had mild dilations of main pancreatic duct and 1 case had mild dilation of common bile duct and intrahepatic bile duct. 5 cases were associated with the atrophy of pancreatic parenchyma with different degrees. 5 cases had adjacent organ infiltration, 3 cases had liver metastases and 3 cases had lymph node metastasis, 1 case had celiac axis, splenic artery and superior mesenteric vein invasion. 6 cases were associated with pancreatogenous portal hypertension. Conclusions Solid-cystic pancreatic neuroendocrine tumors can be easily misdiagnosed as other tumors of pancreas. Analyzing imaging characteristics and clinical data can be expected to improve diagnostic accuracy.

Objective To investigate the imaging features in CT/MR of pancreatic neuroendocrine tumors(PNETs) with multiple lesions and further deepen the understanding of this disease .Methods A retrospective review of 12 PNETs patients′radiological data with pancreatic tumors′numbers≥2 and confirmed by surgery or fine needle aspiration biopsy in Changhai Hospital were conducted .Five cases underwent pancreatic CT plain and enhanced scan , 2 cases underwent MRI plain and enhanced scan , and 5 cases underwent both CT and MRI scan .Results There were totally 46 lesions in 12 patients.There were 29 (63.0%) lesions located in the pancreatic head and neck , and 17(37.0%) lesions located in body and tail of pancreas.The sizes of the lesions ranged from 0.8 to 9.5 cm,and the median size was 2.9 cm.Forty-four (95.7%) of the tumors was round or oval , and 2 ( 4.3%) was lobulated;44 ( 95.7%) mass solid and 2 (4.3%) was cystic.CT plain scan detected punctate , crescent or nodular calcification in 8(17.4%) lesions;enhanced scan found 42 lesions(91.4%) were markedly enhanced in the arterial phase , 2 lesions (4.3%) were markedly enhanced in the pancreatic phase;2 lesions (4.3%) were slightly enhanced and the degree of enhancement was lower than that of the normal pancreas .Four cases (33.3%) had dilatation of pancreatic duct and/or the bile duct, 4 cases (33.3%) had distant organ metastasis, 2 cases (16.7%) had lymph node metastasis, and 3 cases (25.0%) had vascular invasion .Conclusions PNETs can be multiple and vary in the size.Most of the lesions are round or oval solid lesions and the malignant signs for organ metastasis can be found occasionally .In dynamic enhanced scanning , the obvious enhancement of the solid portion in the tumor and the higher enhancement degree than that of normal pancreas is the main characteristic .

Objective To explore the CT findings of benign and malignant pancreatic neuroendocrine tumors and improve its diagnostic accuracy.Methods The clinical information and enhanced CT findings of 96 cases with pathologically-proved pancreatic neuroendocrine tumors were retrospectively reviewed.The CT findings were evaluated by several factors,which included tumor size,morphology,location,internal composition,calcification,separation,bile duct and pancreatic duct dilation and CT value.Results All cases were divided into benign or malignant according to pathological grades,and benign group involved 40 cases with 41 lesions,while malignant group involved 56 cases with 59 lesions.The size of malignant lesions was significantly larger than that of benign lesions (median size 6.0 cm vs 2.2 cm),the shape of the lesions was irregular,and was mainly cystic solid,and mottling,curve shape,clumps calcification was present,then the bile duct and pancreatic duct was mild to moderately dilated,and the difference between the two groups was statistically significant (P ＜0.05).But the difference of tumor location,separation was not significant.45.76％ (27/59) of the malignant lesions reached the peak value in arterial phase,and 44.07％ (26/59) reached the peak value in venous phase;while 68.29％ (28/41) of the benign lesions reached the peak value in arterial phase,and 31.71％ (13/41) reached the peak value in venous phase.The CT values of malignant lesions in plain CT scanning,arterial phase,venous phase,balance phase were (39.02 ±7.53),(121.20 ± 54.73),(125.25 ± 40.77),(101.41 ± 28.68) Hu,while they were (41.49 ± 8.59),(144.73 ± 53.95),(157.05 ±44.72),(121.02 ±29.80) Hu in benign group.In plain CT scanning,the difference of CT value between malignant and benign lesions was not significant;but in the enhanced phase,the CT value of malignant lesions was significantly lower than that of benign lesions,and the difference was statistically significant (P ＜ 0.05).Conclusions The lesion with its size ≥ 3.0 cm,irregnlar morphology,cystic necrosis,calcification,pancreatic and bile duct dilatation is suggestive of malignancy tumor.The average CT values of malignant group are lower than those of the benign group in arterial,venous and balance phases.