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| 1 | Artificial intelligence in pathological evaluation of gastrointestinal cancers显示文摘The integration of artificial intelligence(AI)has shown promising benefits in many fields of diagnostic histopathology,including for gastrointestinal cancers(GCs),such as tumor identification,classification,and prognosis prediction.In parallel,recent evidence suggests that AI may help reduce the workload in gastrointestinal pathology by automatically detecting tumor tissues and evaluating prognostic parameters.In addition,AI seems to be an attractive tool for biomarker/genetic alteration prediction in GC,as it can contain a massive amount of information from visual data that is complex and partially understandable by pathologists.From this point of view,it is suggested that advances in AI could lead to revolutionary changes in many fields of pathology.Unfortunately,these findings do not exclude the possibility that there are still many hurdles to overcome before AI applications can be safely and effectively applied in actual pathology practice.These include a broad spectrum of challenges from needs identification to cost-effectiveness.Therefore,unlike other disciplines of medicine,no histopathology-based AI application,including in GC,has ever been approved either by a regulatory authority or approved for public reimbursement.The purpose of this review is to present data related to the applications of AI in pathology practice in GC and present the challenges that need to be overcome for their implementation. | Anil Alpsoy Aysen Yavuz Gulsum Ozlem Elpek | 2021 | Artificial Intelligence in Gastroenterology2021，2，6： | 0 |
| 2 | Artificial intelligence applications in predicting the behavior of gastrointestinal cancers in pathology显示文摘Recent research has provided a wealth of data supporting the application of artificial intelligence(AI)-based applications in routine pathology practice.Indeed,it is clear that these methods can significantly support an accurate and rapid diagnosis by eliminating errors,increasing reliability,and improving workflow.In addition,the effectiveness of AI in the pathological evaluation of prognostic parameters associated with behavior,course,and treatment in many types of tumors has also been noted.Regarding gastrointestinal system(GIS)cancers,the contribution of AI methods to pathological diagnosis has been investigated in many studies.On the other hand,studies focusing on AI applications in evaluating parameters to determine tumor behavior are relatively few.For this purpose,the potential of AI models has been studied over a broad spectrum,from tumor subtyping to the identification of new digital biomarkers.The capacity of AI to infer genetic alterations of cancer tissues from digital slides has been demonstrated.Although current data suggest the merit of AI-based approaches in assessing tumor behavior in GIS cancers,a wide range of challenges still need to be solved,from laboratory infrastructure to improving the robustness of algorithms,before incorporating AI applications into real-life GIS pathology practice.This review aims to present data from AI applications in evaluating pathological parameters related to the behavior of GIS cancer with an overview of the opportunities and challenges encountered in implementing AI in pathology. | Aysen Yavuz Anil Alpsoy Elif Ocak Gedik Mennan Yigitcan Celik Cumhur Ibrahim Bassorgun Betul Unal Gulsum Ozlem Elpek | 2022 | Artificial Intelligence in Gastroenterology2022，3，5： | 0 |
| 3 | Correlation of hepatitis B surface antigen expression with clinicopathological and biochemical parameters in liver biopsies: A comprehensive study显示文摘BACKGROUND Chronic viral B hepatitis(CHB)is a potentially life-threatening liver disease that may progress to liver failure and cirrhosis.Currently,although combinations of different laboratory methods are used in the follow-up and treatment of CHB,the failure of these procedures in some cases has led to the necessity of developing new approaches.In CHB,the intrahepatic expression pattern of viral antigens,including hepatitis B surface antigen(HBsAg),is related to different phases of inflammation.However,many studies have focused on the intracytoplasmic properties of HBsAg staining,and HBsAg positivity in liver tissue has not been evaluated by objective quantitative methods.AIM To investigate the relationship of image analysis-based quantitative HBsAg expression and its staining patterns with clinicopathological factors and treatment in CHB.METHODS A total of 140 liver biopsies from treatment-naïve cases with CHB infection were included in this study.Following diagnosis,all patients were treated with entecavir(0.5 mg)and followed up at three-month intervals.The percentage of immunohistochemical HBsAg(p-HBsAg)expression in the liver was determined in whole tissue sections of biopsies from each case by image analysis.The immunohistochemical staining pattern was also evaluated separately according to 3 different previously defined classifications.RESULTS A positive correlation between p-HBsAg and serum levels of hepatitis B virus(HBV)DNA and HBsAg was observed(P<0.001).The p-HBsAg value was significantly higher in younger patients than in older patients.When the groups were categorized according to the hepatitis B e antigen(HBeAg)status in HBeAgpositive cases,p-HBsAg was correlated with HBV DNA,hepatitis activity index(HAI)and fibrosis scores(P<0.001).In this group,p-HBsAg and HBsAg expression patterns were also correlated with the viral response(VR)and the serological response(SR)(P<0.001).Multivariate analysis revealed that p-HBsAg was an independent predictor of either VR or SR(P<0.001).In HBeAg-negative patients,although HBsAg expression patterns were correlated with both HAI and fibrosis,no relationship was observed among p-HBsAg,clinicopathological factors and VR.CONCLUSION In pretreatment liver biopsies,the immunohistochemical determination of HBsAg expression by quantitative methods,beyond its distribution within the cell,may be a good predictor of the treatment response,especially in HBeAg-positive cases. | Anil Alpsoy Haydar Adanir Zeynep Bayramoglu Gulsum Ozlem Elpek | 2022 | World Journal of Hepatology2022，14，1： | 0 |
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