Suppr超能文献

磁共振成像-预后因素与乳腺癌分子分类之间的相关性。

Correlation between MR imaging - prognosis factors and molecular classification of breast cancers.

作者信息

Alili C, Pages E, Curros Doyon F, Perrochia H, Millet I, Taourel P

机构信息

Department of Radiology, Lapeyronie Hospital, Montpellier University Hospitals, 34295 Montpellier, France.

Department of Pathological Anatomy, Montpellier University Hospitals, 34295 Montpellier, France.

出版信息

Diagn Interv Imaging. 2014 Feb;95(2):235-42. doi: 10.1016/j.diii.2014.01.002. Epub 2014 Feb 10.

Abstract

The molecular classification of breast cancers defines subgroups of cancer with different prognoses and treatments. Each molecular type representing the intrinsic signature of the cancer corresponds to a histological profile incorporating hormone receptors, HER2 status and the proliferation index. This article describes the correlations between this molecular classification obtained in routine clinical practice using histological parameters and MRI. It shows that there is a specific MRI profile for triple-negative cancers: distinct demarcation, regular edges, hyperintensity on T2 weighted signals and, particularly, a crown enhancement. It is important for the radiologist to understand this molecular classification, firstly because of the relatively suggestive appearance of triple-negative basal-like cancers in the molecular classification, secondly, and particularly, as cancers in patients with the BRCA1 mutation are often triple-negative meaning that the criteria for reading the MRI needs to be tailored to this feature of the cancers, and finally because the efficacy of MRI in assessing response to neoadjuvant chemotherapy depends on the molecular class of cancer treated.

摘要

乳腺癌的分子分类可定义出具有不同预后和治疗方法的癌症亚组。每种代表癌症内在特征的分子类型都对应一种包含激素受体、HER2状态和增殖指数的组织学特征。本文描述了在常规临床实践中使用组织学参数获得的这种分子分类与MRI之间的相关性。研究表明,三阴性乳腺癌具有特定的MRI特征:边界清晰、边缘规则、T2加权信号呈高信号,尤其是呈冠状强化。放射科医生了解这种分子分类很重要,首先是因为在分子分类中三阴性基底样癌的表现相对具有提示性,其次,特别是因为携带BRCA1突变患者的癌症通常为三阴性,这意味着MRI的解读标准需要根据癌症的这一特征进行调整,最后是因为MRI评估新辅助化疗反应的效能取决于所治疗癌症的分子类别。

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