Gastric cancer is the fifth most common cancer and in 2018,it was the third most common cause of cancer-related deaths worldwide.Endoscopic advances continue to be made for the diagnosis and management of both early g...Gastric cancer is the fifth most common cancer and in 2018,it was the third most common cause of cancer-related deaths worldwide.Endoscopic advances continue to be made for the diagnosis and management of both early gastric cancer and premalignant gastric conditions.In this review,we discuss the epidemiology and risk factors of gastric cancer and emphasize the differences in early vs latestage gastric cancer outcomes.We then discuss endoscopic advances in the diagnosis of early gastric cancer and premalignant gastric lesions.This includes the implementation of different imaging modalities such as narrow-band imaging,chromoendoscopy,confocal laser endomicroscopy,and other experimental techniques.We also discuss the use of endoscopic ultrasound in the diagnosis and staging of early gastric cancer.We then discuss the endoscopic advances made in the treatment of these conditions,including endoscopic mucosal resection,endoscopic submucosal dissection,and hybrid techniques such as laparoscopic endoscopic cooperative surgery.Finally,we comment on the current suggested recommendations for surveillance of both gastric cancer and its premalignant conditions.展开更多
Factor analysis which studies correlation matrices is an effective means of data reduction whoseinference on the correlation matrix typically requires the number of random variables, p, to berelatively small and the s...Factor analysis which studies correlation matrices is an effective means of data reduction whoseinference on the correlation matrix typically requires the number of random variables, p, to berelatively small and the sample size, n, to be approaching infinity. In contemporary data collection for biomedical studies, disease surveillance and genetics, p > n limits the use of existingfactor analysis methods to study the correlation matrix. The motivation for the research herecomes from studying the correlation matrix of log annual cancer mortality rate change for p = 59cancer types from 1969 to 2008 (n = 39) in the U.S.A. We formalise a test statistic to perform inference on the structure of the correlation matrix when p > n. We develop an approach based ongroup sequential theory to estimate the number of relevant factors to be extracted. To facilitateinterpretation of the extracted factors, we propose a BIC (Bayesian Information Criterion)-typecriterion to produce a sparse factor loading representation. The proposed methodology outperforms competing ad hoc methodologies in simulation analyses, and identifies three significant underlying factors responsible for the observed correlation between cancer mortality ratechanges.展开更多
文摘Gastric cancer is the fifth most common cancer and in 2018,it was the third most common cause of cancer-related deaths worldwide.Endoscopic advances continue to be made for the diagnosis and management of both early gastric cancer and premalignant gastric conditions.In this review,we discuss the epidemiology and risk factors of gastric cancer and emphasize the differences in early vs latestage gastric cancer outcomes.We then discuss endoscopic advances in the diagnosis of early gastric cancer and premalignant gastric lesions.This includes the implementation of different imaging modalities such as narrow-band imaging,chromoendoscopy,confocal laser endomicroscopy,and other experimental techniques.We also discuss the use of endoscopic ultrasound in the diagnosis and staging of early gastric cancer.We then discuss the endoscopic advances made in the treatment of these conditions,including endoscopic mucosal resection,endoscopic submucosal dissection,and hybrid techniques such as laparoscopic endoscopic cooperative surgery.Finally,we comment on the current suggested recommendations for surveillance of both gastric cancer and its premalignant conditions.
基金This work was supported by National Institutes of Health Grants[grant number RO1 CA95747][grant number P01CA134294-01002].
文摘Factor analysis which studies correlation matrices is an effective means of data reduction whoseinference on the correlation matrix typically requires the number of random variables, p, to berelatively small and the sample size, n, to be approaching infinity. In contemporary data collection for biomedical studies, disease surveillance and genetics, p > n limits the use of existingfactor analysis methods to study the correlation matrix. The motivation for the research herecomes from studying the correlation matrix of log annual cancer mortality rate change for p = 59cancer types from 1969 to 2008 (n = 39) in the U.S.A. We formalise a test statistic to perform inference on the structure of the correlation matrix when p > n. We develop an approach based ongroup sequential theory to estimate the number of relevant factors to be extracted. To facilitateinterpretation of the extracted factors, we propose a BIC (Bayesian Information Criterion)-typecriterion to produce a sparse factor loading representation. The proposed methodology outperforms competing ad hoc methodologies in simulation analyses, and identifies three significant underlying factors responsible for the observed correlation between cancer mortality ratechanges.
