The Application And Advantage Of Pathologic Large Section In Pathological Diagnosis Of Pancreatic Tumor

Background:The pathological large section is a new pathological technique that has been proposed in recent years and has been initially applied in prostate cancer,breast cancer,and gastric cancer.In particular,the guiding significance for evaluating the effect of neoadjuvant therapy for gastric cancer and breast cancer has made the pathological large slice.Applications are getting more and more attention and attention from people.Its advantage is that it can integrate normal tissues,diseased tissues,and multiple cutting margins on one slice in an integrated and comprehensive manner,which is conducive to comparison with image images and improves comprehensive and essential understanding of diseases.Because the anatomy of the prostate and mammary glands is relatively simple and easy to draw,the types of tumors and tumor components are relatively few.Currently,the application of large slices,especially in comparison with images and artificial intelligence,is the prostate and breast.The application in pancreatic tumors is currently a gap in the country and even in the world.Not only is there a small number of units to master this technology,but because pancreatic tumor itself is difficult to perform,the adjacent structure is also complicated and the peripancreatic fat tissue is more difficult to slice.At present,the treatment method for pancreatic resection specimens is routine pathology and production,taking into account the adjacent relationship has a selective take 6 or more pancreatic tumor tissue dehydration embedded film,although the final pathological diagnosis based on routine pathology,but This method of harvesting destroys the integrity of the tumor and fails to provide a complete picture of the tumor's overall appearance and its relationship with the adjacent tissue.In the distribution of tumors,whether multifocal distribution or leaping growth,peripheral ductal intraepithelial neoplasia,the accurate judgment of each margin(especially the pancreatic duct gland uncinate process margin)can not be well observed.This project is based on the advantage of pancreatic cancer in Changhai Hospital and the mastery of pre-slicing techniques.It is a groundbreaking exploration of the extraction and production techniques of large slices of pancreatic pathology and the use of whole slide imaging(WSI)technology.All large slice scans were analysed and stored.Through comparative analysis of digital slices and gross and conventional pathological sections,we found that pathological large slices were comprehensively observed in pancreatic tumors,pathological information extraction and follow-up and imaging histology comparisons.The development of artificial intelligence has important application value.On this basis,we attempted to explore the application value and advantages of large sections in the diagnosis of pancreatic neuroendocrine tumors by combining large sections of pathology with Ki-67 immunohistochemical stainingNeuroendocrine neoplasms is a kind of relatively rare and heterogeneous tumor with neuroendocrine differentiation,which is common in the gastrointestinal tract,lung and pancreas.Pancreatic neuroendocrine neoplasms(PNEN)is the second common type of pancreatic tumor,second only to pancreatic duct adenocarcinoma,which accounts for 2%~5% of all pancreatic tumors.Epidemiological data in the United States show that the annual incidence of PNEN in the country is about 0.22/10 million,and the incidence of the disease has been on the rise in recent years.Although the incidence of PNEN is low,corresponding biological characteristics are significant different from those of pancreatic ductal adenocarcinoma.How to achieve accurate treatment to guide treatment has been a hot topic in recent years in the field of pathology,as well as surgical experts and scholars.With the continuous demonstration of the statistical results of large scale and multi-center samples and the continuous exploration of the essence of the tumor,an updated standard has been established for PNEN by WHO in 2017,which is divided into three categories:1 Highly differentiated pancreatic neuroendocrine tumors:PNET G1(Ki-67 index<3%,mitotic figure<2/10HPF);PNET G2(Ki-67 index of between 3%~20%,mitotic figure of between 2~20/10HPF);PNET G3(mitotic figure>20/HPF,Ki-67 index>20%);2poorly differentiated pancreatic neuroendocrine carcinoma: PNEC G3(mitotic figure>20/HPF,Ki-67 index>20%);small cell type PNEC and large cell type PNEC;3mixed neuroendocrine-nonneuroendocrine neoplasms(MiNEN).Ki-67 index is considered to be the most reliable factor in the evaluation of PNEN's prognosis,and WHO strongly recommends an accurate assessment of the proportion of Ki-67 positive cells.There is a great difference in the clinical treatment and the prognosis of PNEN patients with different pathological diagnosis.Accurate calculation of Ki-67 index is a concern for every pathologist.However,in actual operation,there is a risk of mix-classification for PNEN due to the high heterogeneity of PNEN,poor repeatability and low accuracy of count by naked eyes.Objective:In this study,the application of pathological large sections in the diagnosis of pancreatic tumors was first explored to explore its advantages.On this basis,pathological large sections were combined with Ki-67 immunohistochemical staining and compared with small-section Ki-67 staining.To explore the application value and advantages of large slice Ki-67 immunohistochemical staining in the diagnosis of pancreatic neuroendocrine tumors.Methods:We collected 502 cases of pancreatic specimens removed from the First Affiliated Hospital of the Second Military Medical University from April 2016 to September 2017.We also performed routine sampling and large section sampling.All large slices were made by WSI digital slices.The comparative analysis of digital slices and gross and conventional pathological sections explored the value of large slices in the pathological diagnosis of pancreatic tumors.On this basis,we performed Ki-67 immunohistochemical staining on 23 specimens of pathologically confirmed PNEN.Furthermore,according to the neuroendocrine tumor classification issued by WHO in 2017,the number of Ki-67 nuclear positive tumor cells in 2,000 tumor cells was counted in at least 10 hot-spots,followed by the calculation of Ki-67 index.Subsequently,the proportion of NET G1,NET G2,NET G3 and NEC G3 was statistically analyzed,and related clinicopathological data were arranged.In addition,the consistency of experimental data obtained by the two methods was compared and analyzed.All statistical analysis was achieved by SPSS 22 software.Results:1.Through the comparative analysis of 502 large pathological sections and routine pathological sections,it is found that the pathological large sections have obvious advantages in the diagnosis and application of pancreatic tumors,and can fully and comprehensively display normal tissues and diseased tissues in the eyes of physicians,and can greatly reduce missed diagnosis and misdiagnosis;2.The proliferation activity of PNEN in different regions is quite different.Large pancreas sections can obtain the data of the largest surface of the tumor.Accurately identifying multiple high proliferative areas to avoid misdiagnosis,especially in the determination of NET G2 tumors with high proliferative activity.The superiority is reflected in the successful detection of NET G3 tumors misdiagnosed as NET G2.Conclusion:The large slice technique has great advantages and application prospects in the precise pathological diagnosis of pancreatic tumors.It can greatly reduce missed diagnosis and misdiagnosis.The large slice technique combined with Ki-67 immunohistochemical detection can accurately classify PNEN and guide clinical practice correctly.Treatment and prognosis,and the discovery of new issues and new thinking,provide a new way to further reveal the nature of the tumor.It is recommended that this technology be promoted nationwide,and at the same time it will promote the promotion of the overall level of pathology in China.