Suppr超能文献

卵巢癌患者生存的流式细胞术预后因素:一项5年随访研究。

Flow-cytometric prognostic factors for the survival of patients with ovarian carcinoma: a 5-year follow-up study.

作者信息

Volm M, Kleine W, Pfleiderer A

机构信息

Department of Experimental Pathology, German Cancer Research Center, Heidelberg.

出版信息

Gynecol Oncol. 1989 Oct;35(1):84-9. doi: 10.1016/0090-8258(89)90018-8.

Abstract

Fresh surgical specimens of 37 patients with previously untreated ovarian carcinomas were investigated by means of flow cytometry. The aim of the study was to look for cellular prognostic factors, in addition to the well-known clinical prognostic factors, of survival time for these patients. All patients underwent chemotherapy after surgery, and all patients had a minimum of 5 years of follow-up. Patients with diploid or near-diploid tumors (DNA index less than or equal to 1.25) survived significantly longer than those with aneuploid tumors (DNA index greater than 1.25, P = 0.02). Patients whose tumors showed a high proportion of SG2M phase cells (greater than 17%) or a low proportion of G0/G1 phase cells had shorter survival times than those with tumors with a low proportion of SG2M phase tumor cells (less than or equal to 17%, P = 0.01) or a high proportion of G0/G1 phase tumor cells. There is no significant relationship between cytometric data and stage. Different surgical procedures, cytostatic treatment, histological tumor type, and differentiation had no significant effects on the survival time of patients in this study. Thus, the data from this study demonstrate strong cytometric prognostic factors of the survival of patients with ovarian carcinomas.

摘要

采用流式细胞术对37例未经治疗的卵巢癌患者的新鲜手术标本进行了研究。本研究的目的是寻找除了这些患者众所周知的临床预后因素之外的细胞预后因素。所有患者术后均接受化疗,且所有患者至少随访5年。二倍体或近二倍体肿瘤(DNA指数小于或等于1.25)的患者生存时间明显长于非整倍体肿瘤(DNA指数大于1.25,P = 0.02)的患者。肿瘤显示SG2M期细胞比例高(大于17%)或G0/G1期细胞比例低的患者生存时间比SG2M期肿瘤细胞比例低(小于或等于17%,P = 0.01)或G0/G1期肿瘤细胞比例高的患者短。细胞计量学数据与分期之间无显著关系。不同的手术方式、细胞抑制治疗、组织学肿瘤类型和分化程度对本研究中患者的生存时间无显著影响。因此,本研究数据表明卵巢癌患者生存存在很强的细胞计量学预后因素。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验