Suppr超能文献

[乳腺实质X线图像的改进分类]

[Improved classification of x-ray images of the breast parenchyma].

作者信息

Berzin S A, Demidov S M, Glushko L S, Merkulov E V, Samanov V S

出版信息

Vopr Onkol. 1991;37(4):491-4.

PMID:1887649
Abstract

Classification after J. Wolfe is the most popular classification of mammographic images of the breast to date. It distinguishes four (N1, P1, P2 and DY) types of breast parenchyma according to size of a shadow observed. P2 (subtotal) and DY (total involvement) types imply the highest risk of cancer. The study showed that criteria of the types distinguished are indistinct making it difficult to identify true high risk conditions. It is suggested that J. Wolfe's classification be detailed further, viz. conditions with and without nodular structures (N+ and N0) should be distinguished as well as types with homogeneous and nonhomogeneous non-nodular structures (P1a, P1b, P2a, P2b, etc.). The analysis of 167 mammograms of verified cancer and precancer showed the modified classification to more clearly identify various types of breast parenchyma and to more reliably define those carrying the highest risk for cancer.

摘要

迄今为止,基于J. 沃尔夫(J. Wolfe)的分类是乳腺钼靶图像最常用的分类方法。它根据观察到的阴影大小区分四种(N1、P1、P2和DY)类型的乳腺实质。P2(部分)和DY(完全累及)类型意味着最高的癌症风险。研究表明,所区分类型的标准不明确,难以识别真正的高风险情况。建议进一步细化J. 沃尔夫的分类,即区分有和无结节结构的情况(N+和N0)以及有均匀和不均匀非结节结构的类型(P1a、P1b、P2a、P2b等)。对167例经证实的癌症和癌前病变的乳腺钼靶图像分析表明,改良后的分类能够更清晰地识别各种类型的乳腺实质,并更可靠地确定那些患癌风险最高的情况。

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验