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胃癌患者螺旋CT Lauren 分型与磷酸酶蛋白的表达
引用本文:李辉,高剑波,陈学军,郭华,杨学华,周志刚. 胃癌患者螺旋CT Lauren 分型与磷酸酶蛋白的表达[J]. 郑州大学学报(医学版), 2007, 42(5): 864-866
作者姓名:李辉  高剑波  陈学军  郭华  杨学华  周志刚
作者单位:河南省人民医院放射科,郑州,450003;郑州大学第一附属医院放射科,郑州,450052
摘    要:目的:将胃癌螺旋CT(SCT) Lauren 分型与磷酸酶基因(PTEN)的表达相对照,探讨胃癌SCT表现的分子病理学基础.方法:对83例进展期胃癌患者行SCT平扫及三期动态增强扫描后进行Lauren分型.所有病例均于检查后1周内手术.术后标本行HE染色进行病理学诊断,并与SCT的分型行一致性检验,采用免疫组织化学方法对SCT分型后的标本进行PTEN蛋白检测.结果:①83例胃癌病理学诊断肠型39例,弥漫型44例;SCT诊断肠型40例,弥漫型43例,SCT对胃癌Lauren分型的诊断正确率为96.3%,二者分型基本一致(Kappa=0.928,P<0.05).②弥漫型与肠型胃癌组织中PTEN蛋白阳性表达率分别为32.6%和62.5%,2者差异有统计学意义(P<0.05).结论:SCT增强扫描与分子生物学相结合可提高对胃癌侵袭、转移和预后评估的准确性.

关 键 词:胃癌  螺旋CT  磷酸酶基因  Lauren
修稿时间:2006-08-04

Correlation between Lauren classification by spiral CT and expression of PTEN in gastric carcinoma tissue
LI Hui,GAO Jianbo,CHEN Xuejun,GUO Hua,YANG Xuehua,ZHOU Zhigang. Correlation between Lauren classification by spiral CT and expression of PTEN in gastric carcinoma tissue[J]. Journal of Zhengzhou University: Med Sci, 2007, 42(5): 864-866
Authors:LI Hui  GAO Jianbo  CHEN Xuejun  GUO Hua  YANG Xuehua  ZHOU Zhigang
Abstract:Aim:To compare Lauren classification by SCT with expressions of PTEN in gastric carcinoma tissue so as to investigate the molecular pathologic basement of SCT features of gastric carcinoma.Methods:A total of 83 patients with gastric carcinoma accepted operation within one week after SCT scan.The expression of PTEN in specimens was detected using immunohistochemistry.Results:The accuracy of SCT for Lauren classification was 96.3%.The pathological typing results was consistent with the Lauren classification results by SCT(Kappa=0.928,P<0.05).In the 83 cases of gastric carcinomas,the PTEN-positive expression rates in the diffused and intestinal gastric carcinoma were 32.6% and 62.5%,and the differences were statistically significant(P<0.05).Conclusion:Enhanced SCT scan combined with pathological technique might improve diagnostic accuracy of invasion and metastasis and prognosis in gastric carcinoma.
Keywords:gastric carcinoma   spiral computed tomography    PTEN    Lauren
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