Suppr超能文献

[Clinical significance of galactosyltransferase associated with tumor (GAT), a new tumor marker for ovarian cancer--with special reference to the discrimination between ovarian cancer and endometriosis].

作者信息

Nozawa S, Udagawa Y, Ito K, Nishimura H, Yakushiji M, Shiota M, Noda K, Yajima M, Ohkura H, Murae M

机构信息

Dept. of Obstetrics and Gynecology, School of Medicine, Keio University.

出版信息

Gan To Kagaku Ryoho. 1994 Mar;21(4):507-16.

PMID:8129392
Abstract

We examined GAT, a newly developed tumor marker, in serum samples collected from 1,503 females in six institutions: 387 healthy females, 1,052 patients with gynecological diseases including 311 ovarian cancers, and 64 with nongynecological diseases. Based on the mean value + 2 SD for the healthy females, the cut-off value was set at 16 U/ml. The positive rate of GAT was 2.6% for the healthy females, 7.1% for patients with benign ovarian tumor, 5.6% for those with endometriosis, 47.9% for those with ovarian cancers, 9.3% for those with cervical cancer, and 13.3% for those with endometrial cancer. The false-positive rate of GAT for endometriosis was very low compared with that of the other markers such as CA 125, CA 602, CA 54/61, CA 72-4, STN, SLX examined in this study. The positive predictive value between ovarian cancer and endometriosis was the highest with GAT among the evaluated markers. In the cases in which the CA 125 (CA 602) value is relatively low, discrimination between ovarian cancer and endometriosis is difficult, because these cases include many patients with endometriosis. GAT showed the highest positive predictive value in such cases, so GAT proved to play a complementary role with CA 125 (CA 602). Combination assay with GAT and CA 54/61/CA 72-4/STN or SLX showed higher diagnostic efficiency between ovarian cancer and endometriosis. These results suggest the usefulness of GAT for discrimination between ovarian cancer and endometriosis.

摘要

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