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Objective:To investigate the application value of CT and MRI imageomics based on machine learning method in the diagnosis of pancreatic cancer.Methods:The clinical data of 62 patients with surgically resected and pathologically confirmed pancreatic cancer, who underwent enhanced CT scan, MRI plain or enhanced scan in Shanghai General Hospital between January 2014 and December 2021 were collected. According to the chronological order of surgery, 49 patients from January 2014 to December 2020 were enrolled in the training set and 13 patients from January 2021 to December 2021 were enrolled in the validation set. 3D-slicer 4.8.1 software was used to draw the region of interest in each layer of CT and MRI images for cancerous and paracancerous tissue segment. Image features were extracted by Python and the optimal feature set from the training set data was obtained by using Lasso regression model. The machine learning decision tree model was constructed. The receiver operating characteristic curve(ROC) curve was drawn, and the area under the curve (AUC) was calculated to evaluate the value of these three kinds of imageomics models in the diagnosis of pancreatic cancer.Results:The 1 767 CT features and 1 674 MRI features were obtained from enhanced CT scan, MRI plain scan and enhanced MRI scan, respectively. For the differential diagnosis model of cancerous tissue and paracancerous tissue, the enhanced CT scan data model obtained the optimal feature set involving 6 features, the MRI plain scan model obtained the optimal feature set involving 16 features, and the enhanced MRI scan model obtained the optimal feature set involving 15 features. The diagnostic model based on enhanced CT scan had an AUC of 0.98 in the training set and 1 in the verification group. The AUC of the MRI plain scan and enhanced MRI scan models in both the training set and the validation set was 1. The specificity and sensitivity of machine learning decision tree model based on the three kinds of imageomics models in the diagnosis of cancerous tissue and paracancerous tissue were 100%. For the differential diagnosis model of splenic artery wrapping, the enhanced CT scan model didn′t obtain the optimal features and had no diagnostic efficacy. The MRI plain scan model and enhanced MRI scan model obtained the optimal feature set involving 5 and 4 features, respectively. The AUC of the MRI plain scan model in the training set and the validation set were 0.862 and 0.750, respectively, with diagnostic sensitivity of 93.8% and 50.0%, and specificity of 78.6% and 100%, respectively. The AUC of the enhanced MRI scan model in the training set and the validation set were 0.950 and 0.861, respectively, with diagnostic sensitivity of 90.0% and 93.6%, and specificity of 100% and 78.6%, respectively.Conclusions:Based on the radiomics of CT enhanced, MRI plain scan and enhanced MRI scan, the machine learning diagnostic model has an accuracy of more than 90% in differentiating pancreatic cancer from paracancerous tissue. For the differentiation of splenic artery wrapping in pancreatic cancer, the diagnostic model based on enhanced MRI scan haS the best diagnostic efficiency.
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Objective:To investigate the effects of NOD-like receptor protein 3(NLRP3) inflammasome activation on the proliferation, migration and extracellular matrix desposition of activated pancreatic stellate cells(PSCs).Methods:The rat PSCs were isolated, cultured and identified, and were divided into control group or LPS group based on the pretreatment with LPS (10 μg/ml for 24 hours) or without. The expression of NLRP3 inflammasome associated molecules in PSCs culture medium was detected by ELISA. The PSCs with NLRP3 inhibition were constructed by shRNA carrying lentivirus infection and were divided into LPS+ negative control group and LPS+ lentivirus group based on whether the cells were treated with LPS and infected by lentivirus or not. The alteration in cell proliferation and migration were detected by CCK-8 kit and transwell chamber method. The expression of extracellular matrix α-SMA and collagen in PSCs was detected by immunofluorescence staining and the expression of TGF-β mRNA was analyzed by RT-qPCR.Results:The cytoplasm of PSCs which were cultured for 24 hours was rich in bright annular lipid droplets, and the cells expressed desmin. After 7 days of culture, the cell became larger in size, the lipid droplets basically disappeared, and the cells were activated and expressed α-SMA. The expression of caspase-1, IL-1β and IL-18 in the supernatant of PSCs culture medium in LPS group were significantly higher than those in control group (1.55±0.04 vs 0.65±0.03), (2.02±0.04 vs 1.05±0.05) and (1.70±0.05 vs 0.97±0.03), respectively. After inhibiting by lentivirus infection, the expression of NLRP3 in the lentivirus group (0.25±0.04) was significantly lower than that in negative control group (0.68±0.05). In control group, LPS group, LPS+ negative control group and LPS+ lentivirus group, the A490 values was 0.61±0.02, 1.15±0.06, 0.96±0.05, and 0.56±0.01, respectively; the migrating PSCs number was (64.12±4.58), (121.67±8.02), (111.67±4.67) and (69.67±8.08)/HF, respectively; the relative expression of α-SMA was 0.78±0.05, 4.12±0.04, 3.81±0.06 and 0.88±0.05, respectively; the relative expression of collagen was 0.65±0.03, 3.43±0.02, 2.67±0.02 and 0.48±0.03, respectively; and the expression of TGF-β mRNA was 0.22±0.03, 0.89±0.01, 0.86±0.03 and 0.43±0.02, respectively. The A490 value, the migrating cells number, the expression of α-SMA, collagen and the expression of TGF-β mRNA in LPS group and LPS+ negative control group was significantly higher than those in control group and LPS+ lentivirus group, and all the differences were statistically significant (all P value <0.05). Conclusions:NLRP3 inflammasome activation may accelerate the extracellular matrix deposition and pancreatic fibrogenesis by promoting PSCs proliferation and migration ability via regulating the biological functions.
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ObjectiveTo study the characteristics and the impact of lymph node metastasis on radical resection for pancreatic head cancer to provide a theoretical basis for lymphadenectomy in radical resection.To study the reliability of using a surgical microscope to detect lymph nodes in radically resected specimens of pancreatic head cancer.MethodsLymph nodes in the specimens after radical pancreaticoduodeneetomy (pancreaticoduodenectomy + D2 regional lymphadenectomy) were identified using a surgical microscope and they were grouped using the JPS standard.The position and the frequency of the lymph nodes retrieved,and their association with other clinicopathologic factors were analysed.The results were compared with the data published in 2004 on 46 patients to evaluate the reliability of using a surgical microscope.ResultsLymph node metastasis was detected histopathologically in 101 patients (67.3％).The median number of lymph nodes retrieved in the specimens as detected using the surgical microscope was 38.2.The most commonly involved lymph node groups were No.13 (64.5％),No.14 (51.7％),No.17 (38.6％),No.12 (25.8％),No.16 (20.8％).Lymph node metastasis was significantly associated with tumour T stage,tumour invasion and differentiation,preoperative serum level of CA19-9 and CA72-4,but not with patient age,sex,or tumour location.There were no significant differences between the results and the data of the previous study in 2004.ConclusionsExtended lymphadenectomy is necessary because extensive lymph node metastasis was common.Surgical microscopy is an effective and reliable method to detect lymph nodes in resected specimens of pancreatic head cancer for accurate pathologic staging.