Suppr超能文献

[类固醇受体在乳腺癌中的长期预后作用]

[Long-term prognostic role of steroid receptors in cancer of the breast].

作者信息

Broët P, Pichon M F, Magdelenat H, Delarue J C, Spyratos F, Basuyau J P, Saez S, Rallet A, Courrière P, Millon R, Asselain B

机构信息

Unité de biostatistiques, Institut Curie, Paris, France.

出版信息

Bull Cancer. 1998 Apr;85(4):347-52.

PMID:9752299
Abstract

We screened for the prognostic value of estrogen receptor (ER) and progesterone receptor (PR) through a multicentric study of 2,257 operable breast cancer patients who did not received adjuvant therapy. Three hundred and seven local-regional recurrences, 105 metachronous contralateral breast cancer, 589 metastases and 537 deaths from cancer had been diagnosed with a median follow-up of 8.5 years. A total of 69% of the tumors were ER positive and 54% PR positive. For statistical analysis, 1,665 patients were studied because of complete clinical and biological data. In univariate analysis, ER and PR status were of prognostic value for the metastases-free interval (MFI) and the overall survival (OS). In multivariate analysis (Cox proportional hazard model), only the ER status showed a significant difference between positive and negative groups regarding the MFI and OS. By using Cox regression model with time-dependent covariates, we show that the predictive value of ER status of the primary tumor decreases by approximately 20% per year, losing its significance after 8 years of follow-up. These results show that ER and PR status have a relatively limited predictive value and their major interest remain in the domain of therapeutic decision.

摘要

我们通过对2257例未接受辅助治疗的可手术乳腺癌患者进行多中心研究,筛查雌激素受体(ER)和孕激素受体(PR)的预后价值。在中位随访8.5年期间,已诊断出307例局部区域复发、105例异时性对侧乳腺癌、589例转移和537例癌症死亡。总共69%的肿瘤为ER阳性,54%为PR阳性。由于具备完整的临床和生物学数据,对1665例患者进行了统计分析。单因素分析中,ER和PR状态对无转移生存期(MFI)和总生存期(OS)具有预后价值。多因素分析(Cox比例风险模型)中,仅ER状态在MFI和OS的阳性和阴性组之间显示出显著差异。通过使用带有时间依赖性协变量的Cox回归模型,我们发现原发性肿瘤的ER状态预测价值每年下降约20%,随访8年后失去其显著性。这些结果表明,ER和PR状态的预测价值相对有限,它们的主要意义仍在于治疗决策领域。

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