Shah, M. A. et al. Molecular classification of gastric cancer: A new paradigm.
Sharma, H., Zerbe, N., Klempert, I., Hellwich, O. & Hufnagl, P. Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology.
The aim of this study is to standardize a morphologic classification of gastric sleeve based on water-soluble contrast upper gastrointestinal series, and to determine possible clinical implications.
A simplified narrow-band imaging (NBI) endoscopy classification of gastric precancerous and cancerous lesions was derived and validated in a multicenter study.
The Japanese classification of gastric carcinoma defines NEC as a special type in the histological classification of gastric tumors and considers NEC to be either small cell type or large cell type [2].
The status of gastric cancer was cT4a SE N2H0P0CY1 cStageIV (based on Japanese classification of gastric carcinoma [7]).
The histopathological examination showed pStage IA gastric cancer according to the Japanese Classification of Gastric Carcinoma, 14th edition [7].
A few viable cancer cells were found in the tumor nest, so histological effect by the CRT was evaluated as grade 2 according to the Japanese classification of gastric carcinoma [7].
* Japanese classification of gastric carcinoma; ** pTNM classification.
The macroscopic classification was determined according to the Japanese Classification of Gastric Cancer with TNM staging [ 3]. *Japanese classification of gastric carcinoma, modified.
§Japanese Classification of Gastric Carcinoma (3rd English edition) proposed by the Japanese Gastric Cancer Association JGCAA).
