Goseki histological grading of gastric cancer is an important predictor of outcome.
TNM (tumour, node, metastases) staging has thus far been the most important guide to prognosis in patients with gastric cancer. Histological grading, in contrast, has not provided any additional information. Recently a novel grading system based on tubular differentiation and mucus production has been proposed, which was correlated with patterns of tumour spread found at necropsy. This study set out to assess its value as a determinant of survival after gastric resection. In a consecutive series of 211 patients who had potentially curative resection for gastric cancer, five histological grading systems were assessed: the Lauren type, the WHO type, degree of differentiation, the type of tumour border, and the lymphocytic response to the tumour and compared with the Goseki grading (I-IV). When T and N stage were taken into account, using Cox's proportional hazards model, only the Goseki grading added further to the ability to predict survival. The proportional hazards ratios were: node negative v node positive 6.5 T1 v T3 2.45; Goseki I v Goseki IV 3.1. Five year survival of patients with mucus rich (Goseki II and IV) T3 tumours was significantly worse than that of patients with mucus poor (Goseki I and III) T3 tumours (18% v 53%, p < 0.003). Goseki grading identifies subgroups of patients with a poorer prognosis than is predicted by TNM staging alone. It could prove useful in the selection of patients for adjuvant therapy after potentially curative resection for gastric cancer.
Academic Department of Surgery, General Infirmary at Leeds.
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Martin
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Dixon
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Sue-Ling
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Axon
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Johnston
D000368:Aged; D002471:Cell Transformation, Neoplastic / Q000473:pathology; D005260:Female; D006801:Humans; D008207:Lymphatic Metastasis; D008297:Male; D009093:Mucus / Q000378:metabolism; D009367:Neoplasm Staging; D011379:Prognosis; D016016:Proportional Hazards Models; D013274:Stomach Neoplasms / Q000401:mortality* / Q000473:pathology* / Q000601:surgery; D013997:Time Factors