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1.
Article in Chinese | WPRIM | ID: wpr-991192

ABSTRACT

Objective:To develop and validate the models based on mixed enhanced computed tomography (CT) radiomics and deep learning features, and evaluate the efficacy for differentiating pancreatic adenosquamous carcinoma (PASC) from pancreatic ductal adenocarcinoma (PDAC) before surgery.Methods:The clinical data of 201 patients with surgically resected and histopathologically confirmed PASC (PASC group) and 332 patients with surgically resected histopathologically confirmed PDAC (PDAC group) who underwent enhanced CT within 1 month before surgery in the First Affiliated Hospital of Naval Medical University from January 2011 to December 2020 were retrospectively collected. The patients were chronologically divided into a training set (treated between January 2011 and January 2018, 156 patients with PASC and 241 patients with PDAC) and a validation set (treated between February 2018 and December 2020, 45 patients with PASC and 91 patients with PDAC) according to the international consensus on the predictive model. The nnU-Net model was used for pancreatic tumor automatic segmentation, the clinical and CT images were evaluated, and radiomics features and deep learning features during portal vein phase were extracted; then the features were dimensionally reduced and screened. Binary logistic analysis was performed to develop the clinical, radiomics and deep learning models in the training set. The models' performances were determined by area under the ROC curve (AUC), sensitivity, specificity, accuracy, and decision curve analysis (DCA).Results:Significant differences were observed in tumor size, ring-enhancement, upstream pancreatic parenchymal atrophy and cystic degeneration of tumor both in PASC and PDAC group in the training and validation set (all P value <0.05). The multivariable logistic regression analysis showed the tumor size, ring-enhancement, dilation of the common bile duct and upstream pancreatic parenchymal atrophy were associated with PASC significantly in the clinical model. The ring-enhancement, dilation of the common bile duct, upstream pancreatic parenchymal atrophy and radiomics score were associated with PASC significantly in the radiomics model. The ring-enhancement, upstream pancreatic parenchymal atrophy and deep learning score were associated with PASC significantly in the deep learning model. The diagnostic efficacy of the deep learning model was highest, and the AUC, sensitivity, specificity, and accuracy of the deep learning model was 0.86 (95% CI 0.82-0.90), 75.00%, 84.23%, and 80.60% and those of clinical and radiomics models were 0.81 (95% CI 0.76-0.85), 62.18%, 85.89%, 76.57% and 0.84 (95% CI 0.80-0.88), 73.08%, 82.16%, 78.59% in the training set. In the validation set, the area AUC, sensitivity, specificity, and accuracy of deep learning model were 0.78 (95% CI 0.67-0.84), 68.89%, 78.02% and 75.00%, those of clinical and radiomics were 0.72 (95% CI 0.63-0.81), 77.78%, 59.34%, 65.44% and 0.75 (95% CI 0.66-0.84), 86.67%, 56.04%, 66.18%. The DCA in the training and validation sets showed that if the threshold probabilities were >0.05 and >0.1, respectively, using the deep learning model to distinguish PASC from PDAC was more beneficial for the patients than the treat-all-patients as having PDAC scheme or the treat-all-patients as having PASC scheme. Conclusions:The deep learning model based on CT automatic image segmentation of pancreatic neoplasm could effectively differentiate PASC from PDAC, and provide a new non-invasive method for confirming PASC before surgery.

2.
Article in Chinese | WPRIM | ID: wpr-1023192

ABSTRACT

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.

3.
Article in Chinese | WPRIM | ID: wpr-930981

ABSTRACT

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.

4.
Article in Chinese | WPRIM | ID: wpr-931275

ABSTRACT

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.

5.
Article in Chinese | WPRIM | ID: wpr-956994

ABSTRACT

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.

6.
Chinese Journal of Digestion ; (12): 458-463, 2022.
Article in Chinese | WPRIM | ID: wpr-958334

ABSTRACT

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.

7.
Article in Chinese | WPRIM | ID: wpr-990595

ABSTRACT

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.

8.
Chinese Journal of Digestion ; (12): 699-704, 2021.
Article in Chinese | WPRIM | ID: wpr-912227

ABSTRACT

Objective:To explore the differences in clinical and imaging features between pancreatic adenosquamous carcinoma (PASC) and pancreatic ductal adenocarcinoma (PDAC).Methods:The clinical data, imaging and pathological data of 171 patients pathologically diagnosed with PASC after surgical resection (PASC group) (from February 2011 to October 2020, 148 patients from the First Affiliated Hospital of Naval Medical University and 23 patients from the Second Affiliated Hospital of Zhejiang University School of Medicine) and 100 patients pathologically diagnosed with PDAC after surgical resection (PDAC group) (from January to June, 2018, at the First Affiliated Hospital of Naval Medical University) were retrospectively analyzed. Computed tomography and magnetic resonance imaging features were analyzed by two associate chief physician of department of radiology. Independent sample t test, rank sum test, chi-square test or Fisher exact probability test were used for statistical analysis. Multivariate logistic regression analysis was used to analyze independent predictors of PASC. Results:The longest diameter of tumor of PASC group was larger than that of PDAC group (35.0 mm (28.0 mm to 45.0 mm) vs. 29.5 mm (23.0 mm to 36.0 mm)), and the rates of cystic necrosis, ring-enhancement, normal distal main pancreatic duct and normal pancreatic parenchyma of PASC group were higher than those of PDAC group (62.0%, 106/171 vs. 12.0%, 12/100; 66.1%, 113/171 vs. 25.0%, 25/100; 52.0%, 89/171 vs. 12.0%, 12/100; 70.2%, 120/171 vs. 29.0%, 29/100, respectively); and the differences were statistically significant ( Z=-4.001, χ2=72.183, 42.612, 43.284 and 43.221, all P<0.01). The results of multivariate logistic regression analysis showed that the cystic necrosis, ring-enhancement, normal distal main pancreatic duct and normal pancreatic parenchyma were indenpendent predictors of PASC (odds ratio=10.083, 2.361, 3.086 and 2.632, 95% confidence interval 8.736 to 11.639, 2.096 to 2.660, 2.605 to 3.656 and 2.267 to 3.057, all P<0.01); and the sensitivity for PASC diagnosis was 62.0%, 66.1%, 51.7% and 70.3%, respectively; the specificity was 88.0%, 75.0%, 88.0% and 71.0%, respectively; the positive predictive value was 89.3%, 81.9%, 88.1% and 80.5%, respectively. Conclusions:PASC and PDAC have similar clinical features. The imaging features of cystic necrosis, ring-enhancement, normal distal main pancreatic duct and normal pancreatic parenchyma are independent predictive factors of PASC.

9.
Article in Chinese | WPRIM | ID: wpr-931265

ABSTRACT

Objective:To accurately identify the relationship between the arterial radiomics score (rad-score) and pathologic superior mesenteric vein (SMV) resection margin in patients with pancreatic head cancer.Methods:The clinical data of 181 patients with pathologically confirmed pancreatic head cancer, who underwent multi-slice computed tomography (MDCT) within one month of resection in the First Affiliated Hospital of Naval Medical University between January 2016 and December 2018 were collected. Based on the pathology of SMV resection margin, the patients were divided into SMV negative margin group ( n=127) and SMV positive margin group ( n=54). 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 CT arterial images for tumor segmentation. Rython 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 calculated the arterial rad-score. Univariate and multivariate logistic regression models were used to analyze the association between the arterial rad-score and SMV resection margin. ROC was drawn and AUC, sensitivity, specificity and accuracy for diagnosing the SMV resection margin were calculated. The clinical usefulness of arterial rad-score for diagnosing SMV resection margin was determined by decision curve analysis (DCA). Results:There were statistical differences on LVSI and the touching angle of tumor and SMV/portal vein (PV) between SMV negative margin group and SMV positive margin group (all P<0.001). A total of 1 029 arterial radiomics CT features were obtained, and 14-selected arterial phase features associated with SMV resection margin were determined after being reduced by the Lasso logistic regression algorithm. Univariate analysis showed that the arterial radiomics score, LVSI, the touching angle of tumor and SMV/PV were all correlated with SMV resection margin (all P<0.001). Multivariate analyses confirmed that patients with high arterial radiomics score had a 3.63-fold risk of positive resection margin compared with that with low arterial radiomics score, and a higher arterial rad-score was associated with a higher risk of SMV positive resection margin ( P<0.0001). At the cut-off value of -0.711, AUC of the arterial rad-score for diagnosing SMV resection margin was 0.838, and the sensitivity, specificity and accuracy was 77.8%, 75.6% and 76.24%. Decision curve analysis demonstrated that the percentage of the arterial radiomics score for predicting the positive SMV resection margin was >0.02, and the application of the arterial radiomics score could benefit the patients. Conclusions:The arterial rad-score was strongly correlated with SMV resection margin of pancreatic cancer, and can accurately predict SMV resection margin and provide a new tool for preoperative noninvasive evaluation of the SMV resection margin.

10.
Article in Chinese | WPRIM | ID: wpr-931266

ABSTRACT

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.

11.
Article in Chinese | WPRIM | ID: wpr-931267

ABSTRACT

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.

12.
Article in Chinese | WPRIM | ID: wpr-931268

ABSTRACT

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.

13.
Article in Chinese | WPRIM | ID: wpr-931269

ABSTRACT

Objective:To develop a visualized nomogram with a predictive value to differentiate mass-forming chronic pancreatitis (MFCP) from pancreatic ductal adenocarcinoma (PDAC) patients with chronic pancreatitis (CP) history.Methods:The clinical and radiological data of 5 433 CP patients acoording to the Asia-Pacific Diagnostic Criteria between February 2011 and February 2021 in the First Affiliated Hospital of Naval Medical University were retrospectively analyzed, and 71 PDAC patients with CP history and 67 MFCP who underwent surgery or biopsy and pathologically confirmed were eventually enrolled. The training set included 44 patients with MFCP and 59 patients with PDAC who were diagnosed between February 2011 and April 2018. The validation set consisted of 23 patients with MFCP and 12 patients with PDAC who were diagnosed between May 2018 and February 2021. Univariate and multivariate logistic regression analyses were performed to develop a prediction model for PDAC and MFCP, and the model was visualized as a nomogram. ROC was used to evaluate the predictive efficacy of the nomogram, and the clinical usefulness was judged by decision curve analysis.Results:The univariate analysis showed that a significant association with pancreatic cancer were observed for the duct-to-parenchyma ratio ≥0.34, pancreatic duct cut-off, pancreatic portal hypertension, arterial CT attenuation, portal venous CT attenuation, delayed CT attenuation, and vascular invasion in both the training and validation cohorts, but the duct-penetrating sign in the training cohort only. The multivariable logistic regression analysis showed that statistically significant differences (all P value <0.05) existed in cystic degeneration, a duct-to-parenchyma ratio ≥0.34, the duct-penetrating sign, pancreatic portal hypertension and arterial CT attenuation between the two cohorts. The above parameters were selected for the logistic regression model. The predicted model=3.65-2.59×cystic degeneration+ 1.26×duct-to-parenchyma ratio≥0.34-1.40×duct-penetrating sign+ 1.36×pancreatic portal hypertension-0.05×arterial CT attenuation. Area under the curve, sensitivity, specificity and accuracy of the model-based nomogram were 0.87 (95 CI 0.80-0.94), 89.0%, 75.0% and 83.5% in the training cohort, and 0.94 (95 CI 0.82-0.99), 91.7%, 100% and 97.1% in the validation cohort, respectively. Decision curve analysis showed that when the nomogram differentiated MFCP from PDAC patients with CP history at a rate of 0.05-0.85, the application of the nomogram could benefit the patients. Conclusions:The nomogram based on CT radiological features accurately differentiated MFCP from PDAC patients with CP history and provide reference for guiding the treatment and judging the prognosis.

14.
Article in Chinese | WPRIM | ID: wpr-931270

ABSTRACT

Objective:To develop and validate a visualized computed tomography nomogram for differentiating focal-type autoimmune pancreatitis (fAIP) from pancreatic ductal adenocarcinoma (PDAC).Methods:This retrospective review included 42 consecutive patients with fAIP diagnosed according to the International Consensus Diagnostic Criteria and 242 consecutive patients with PDAC confirmed by pathology between January 2011 and December 2018 in the First Affiliated Hospital of Naval Medical University. Among them, 209 consecutive patients (25 fAIP and 184 PDAC) were enrolled in the development cohort; Seventy-five consecutive patients (17 fAIP and 58 PDAC) were enrolled in the validation cohort. CT image characteristics, including lesion location, size, enhancement mode and degree of mass enhancement in portal vein phase, pancreatic parenchymal atrophy, main pancreatic duct dilation, common bile duct dilation, cyst, acute obstructive pancreatitis, and vascular invasion were compared. Univariate and multivariate analysis were used to screen the independent predictive factors for fAIP and PDAC, based on which the nomogram was constructed and visualized. The receiver operating characteristic curve (ROC) was drawn and area under the curve (AUC) was calculated to evaluate the differential efficacy of the nomogram. The clinical usefulness of the nomogram was evaluated by decision curve analysis.Results:There were statistically significant differences on common bile duct dilation and the mode and degree of enhancement in portal phase between fAIP group and PDAC group in training set and validation set ( P<0.05). Univariate regression analysis showed that common bile duct dilation and degree of mass enhancement in portal vein were closely correlated with fAIP and PDAC phase between the two groups in training set and validation set; mass enhancement mode in portal vein phase and main pancreatic duct dilation were closely correlated with fAIP and PDAC in training set. Multivariate logistic regression analysis showed that common biliary duct dilatation ( OR=0.26, 95% CI 0.06-1.10, P=0.07), main pancreatic duct dilation ( OR=9.46, 95% CI 1.60-56.04, P<0.01) and mass mild hyper-enhancing in portal vein phase ( OR=0.003, 95% CI 0.0003-0.0278, P<0.0001) were the three independent predictors for fAIP and PDAC. Thus, the equation for predicting the probability of PDAC was 4.51-1.33× no dilatation of the common bile duct+ 2.25× the main pancreatic duct dilated-5.84× mass mild hyper-enhancing during the portal phase. The individualized prediction nomogram using these predictors of the fAIP achieved an AUC of 0.97 (95% CI 0.95-0.99) in the development set and 0.97(95% CI0.94-1.00) in the validation set. The sensitivity, specificity and accuracy of the model were 87.5%, 100% and 89% in the training set; and 94.83%, 94.12% and 94.67% in the validation set, respectively. The decision curve analysis demonstrated that the nomogram was clinically useful when the nomogram differentiated fAIP and PDAC at a rate of >0.2. Conclusions:The nomogram based on common bile duct dilation, main pancreatic duct dilation and mass enhancement in portal vein phase can be used as a useful tool for predicting fAIP and PDAC and provide valuable evidence for clinical decision.

15.
Article in Chinese | WPRIM | ID: wpr-931271

ABSTRACT

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.

16.
Article in Chinese | WPRIM | ID: wpr-931272

ABSTRACT

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.

17.
Article in Chinese | WPRIM | ID: wpr-931273

ABSTRACT

Objective:To verify the predictive value of fully-automated 3D volume segmentation of CT images for the overall survival prognosis of resectable pancreatic ductal adenocarcinoma (PDAC).Methods:From July 2018 to March 2019, the clinical data of 198 cases of resectable PDAC were continuously collected in the First Affiliated Hospital of Naval Medical University. According to the level of carbohydrate antigen(CA)19-9 and carcinoembryonic antigen(CEA), the patient were divided into low CA19-9 group(≤210 U/ml ), high CA19-9 group (>210 U/ml ), normal CEA group (<5 ng/ml ) and high CEA group (≥5 ng/ml). Using our fully-automated segmentation tool developed in the early stage, images at the plain phase and portal phase were matched to those at the late artery phase by taking the artery phase as the matching target to establish UNet model; and the PDAC tumor and pancreatic glands were three-dimensionally segmented to estimate the tumor 3D volume. Univariate and multivariate logistic regression analysis were performed to compare the tumor 3D volume with the common preoperative risk factors (tumor 2D long diameter, CA19-9 level, CEA level, etc.) in predicting the patients′ survival. C-index was used to estimate the accuracy for predicting the survival. Receiver operating characteristics curve (ROC) was drawn and AUC was calculated to evaluate the accuracy for predicting the 1-year and 2-year overall survival and the influence of CA19-9 and CEA level on the patients′ overall survival.Results:Univariate logistic analysis showed that age, tumor 3D volume, tumor location, CA19-9 and CEA level were correlated with the patients′ overall survival. Multivariate logistic analysis showed that tumor 3D volume, CA199 and CEA were correlated with the overall survival. Among them, tumor 3D volume was most strongly correlated with the overall survival ( HR=2.25, 95% CI1.49-3.39, P<0.0001). The prognostic C-index of automatic 3D tumor volume, tumor long diameter, serum CEA and CA19-9 was 0.667(95% CI0.617-0.717), 0.637(0.583-0.691), 0.593(0.527-0.659) and 0.585(0.526-0.644), respectively. The AUCs of 3D tumor volume, tumor location, tumor long diameter, serum CEA and CA19-9 for predicting 1-year and 2-year survival were 0.726 and 0.698, 0.562 and 0.562, 0.703 and 0.660, 0.583 and 0.624, 0.602 and 0.609 respectively. C-index and AUC of tumor 3D volume was significantly better than those of the other common preoperative risk factors, and the difference was statistically significant (all P value <0.05). The survival of patients with large tumor 3D volume was greatly poorer than that of patients with small tumor 3D volume in low CA19-9 group, high CA19-9 group, normal CEA group and high CEA group, and the differences were all statistically significant ( HR=2.27, 95% CI 1.39-3.72; HR=2.42, 95% CI1.23-4.74; HR=2.08, 95% CI1.07-4.06; HR=2.67, 95% CI1.63-4.38, all P value <0.01). And the automatic 3D volume was the strongest predictor for the survival in high CA19-9 group. Conclusions:The tumor 3D volume obtained by automatic CT segmentation was an objective and reliable prognostic biomarker, which can supplement the established preoperativel risk factors and was expected to guide the personalized choice of neoadjuvant therapy.

18.
Article in Chinese | WPRIM | ID: wpr-908470

ABSTRACT

The particularity of pancreatic anatomical location, the complexity of secretory function, and the diversity of pathology lead to complex imaging findings of pancreatic tumors. The common pancreatic tumors include pancreatic ductal adenocarcinoma, solid pseudopaillary neo-plasm, neuroendocrine neoplasm, intraductal papillary mucinous neoplasm, serous cystadenoma and mucinous cystic neoplasm. Atypical imaging findings are important reasons for misdiagnosis. Based on relevant clinical experiences, the authors analyze and summarize the atypical imaging findings of six kinds of common pancreatic tumors, aiming to improve radiologists and clinicians comprehensive understanding of pancreatic tumors.

19.
Article in Chinese | WPRIM | ID: wpr-908793

ABSTRACT

Objective:To investigate the imaging features of undifferentiated pancreatic carcinoma (UCOGCP) with osteoclast-like giant cells.Methods:CT and MRI data of 4 pathologically diagnosed UCOGCP patients admitted in the First Affiliated Hospital of Naval Medical University from December 2014 to January 2019 were retrospectively analyzed. The tumor location, major length, shape, border, density or signal, capsule, calcification, hemorrhage, cystic degeneration, degree of enhancement, as well as the presence or absence of pancreatic duct dilatation, pancreatic parenchymal atrophy, peripheral vascular invasion, lymph node and organ metastasis were recorded.Results:Of 4 UCOGCP patients, 1 case had the mass located in head of pancreas, 2 cases in body of pancreas , and 1 in tail of pancreas. The length of tumor ranged from 3.3 cm to 13.0 cm, and the average was 8.8 cm.3 cases were round-like, and 1 was irregular; 2 tumors were well defined with capsules, 2 with unclear border. 4 cases showed solid-cystic masses, 3 of which had cystic separation. 4 cases showed heterogeneous low density on unenhanced CT, and 1 case had spotted calcification. The solid component of the mass was mild enhanced on enhanced CT, and partial solid component of the mass showed obvious enhancement in 2 cases. 2 cases showed mixed low signal on T 1WI, 1 of which had small patchy high signal indicating hemorrhage. 2 cases showed mixed high signal on T 2WI, and high signal on DWI. 2 cases had major pancreatic duct dilation. 1 case had pancreatic parenchyma atrophy. 1 case had descending duodenum invasion. 3 cases had peripheral vascular invasion, including portal vein, splenic artery, and splenic vein. 1 case had tumor thrombosis in the portal vein and splenic vein. 1 case was associated with pancreatogenous portal hypertension. Conclusions:The imaging features of UCOGCP showed a large solid-cystic mass with hemorrhage and calcification. The solid component of the mass was mild enhanced and the partially solid component was obviously enhanced. The combination of its imaging characteristics and clinical data can improve the accuracy of diagnosis.

20.
Article in Chinese | WPRIM | ID: wpr-908794

ABSTRACT

Objective:To analyze the clinical and pathological features and gene mutations of pancreatic acinar cell carcinoma (PACC).Methods:Clinical data of 34 patients with PACC admitted to the Department of Pancreatic Surgery of the First Affiliated Hospital of Naval Medical University from December 2009 to July 2018 were retrospectively analyzed to summarize its clinical characteristics, and the expressions of α1-ACT, CaM5.2, Syn and CgA in pancreatic tumor tissues were detected by immunohistochemistry. Next-generation gene sequencing technology was used to detect gene mutations in tumor specimens.Results:Among the 34 PACC patients, 23(68%) were males and 11(32%) were females; the age ranged from 25 to 75 years, with an average age of 54 years. The first symptom was abdominal pain or distension in 21 cases (62%), skin or scleral yellow staining in 4 cases(12%), and 9 cases(26%) were found in routine physical examination. BMI was 17.6-34.0 kg/m 2, of which 3 cases (9%) were <18.5 kg/m 2, 23 cases (68%) were 18.5-24.0 kg/m 2, and 8 cases (23%) were >24.0 kg/m 2. Preoperative examination showed elevated CA19-9 in 7 cases (20.6%), elevated CEA in 3 cases (8.8%), and elevated AFP in 7 cases (20.6%). Blood amylase was 16-247 U/L, with an average of 80 U/L. Enhanced CT showed that the lesion was irregular in shape, showing inhomogeneity and slightly low density, with areas of cystic degeneration and necrosis. The tumor was located in the head of the pancreas in 14 cases (41%), the body and tail of the pancreas in 19 cases (56%), and the neck of the pancreas in 1 case (3%). The largest tumor diameter was 1.5-15.5 cm, with an average of 5.4 cm. Postoperative pathologic stage I was confirmed in 4 cases (12%), stage Ⅱ in 14 cases (41%), stage Ⅲ in 14 cases (41%) and stage Ⅳ in 2 cases (6%). Immunohistochemical results showed that both α1-ACT and CaM5.2 were positively expressed (100%). Syn was positive in 8 cases (23.5%) and CgA was positive in 6 cases (17.6%). Ki-67 index was from 9% to 70%, with an average of 41%. Gene sequencing of pancreatic tumor tissue from 6 patients showed BRCA2 mutation in 2 patients (7155C>G), K-ras mutation in 1 patient (35G>T), RET mutation in 1 patient (200G>A), and LKB1 mutation (234G>T) in 1 patient, and one double mutation of K-ras and RET (35G>A, 1 798C>T). 30 patients were followed up, and the median survival was 38.3 months. Conclusions:PACC was a rare pancreatic tumor with no specific clinical manifestations. The positive expression rates of α1-ACT and CAM5.2 in tumor tissues were 100%. BRCA2, K-ras, RET and LKB1 were common gene mutations.

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