Literature DB >> 33822251

[Updated WHO classification of tumors of the breast: the most important changes].

Annette Lebeau1,2, Carsten Denkert3.   

Abstract

The development of the WHO classification of tumors of the breast is driven by new knowledge from research whose translation into daily practice is considered clinically relevant. The fifth edition represents an update of the fourth edition and essentially follows the previously known systematics. The histologic features of the lesions continue to form the basis of the classification in the update. This also applies to the definition of invasive tumor types. However, several new molecular classifications as well as additional prognostic and predictive factors are presented and discussed, which improve prognosis estimation and therapy decisions. This paper aims to present the main changes in the current WHO classification. These include the revised definition of mixed invasive carcinomas, the introduction of new special invasive entities (tall cell carcinoma with reversed polarity, mucinous cystadenocarcinoma), the deletion of special invasive types and their classification as variants of invasive carcinoma, NST (no special type, including medullary, lipid-rich, glycogen-rich, among others), the typing of primary neuroendocrine neoplasms of the breast by analogy with other organ systems, changes in the dignity criteria of phyllodes tumors, and the revised subtyping of lobular carcinoma in situ (LCIS). In addition to improvements in the fifth edition of the classification, flaws are also highlighted. A section is devoted to new molecular parameters.

Entities:  

Keywords:  Lobular neoplasia; Neuroendocrine tumors; Papillary neoplasia; Phyllodes tumor; Prognosis

Mesh:

Year:  2021        PMID: 33822251     DOI: 10.1007/s00292-021-00934-9

Source DB:  PubMed          Journal:  Pathologe        ISSN: 0172-8113            Impact factor:   1.011


  1 in total

1.  Assessment of Ki67 in Breast Cancer: Updated Recommendations From the International Ki67 in Breast Cancer Working Group.

Authors:  Torsten O Nielsen; Samuel C Y Leung; David L Rimm; Andrew Dodson; Balazs Acs; Sunil Badve; Carsten Denkert; Matthew J Ellis; Susan Fineberg; Margaret Flowers; Hans H Kreipe; Anne-Vibeke Laenkholm; Hongchao Pan; Frédérique M Penault-Llorca; Mei-Yin Polley; Roberto Salgado; Ian E Smith; Tomoharu Sugie; John M S Bartlett; Lisa M McShane; Mitch Dowsett; Daniel F Hayes
Journal:  J Natl Cancer Inst       Date:  2021-07-01       Impact factor: 13.506

  1 in total
  1 in total

1.  Machine learning-based diagnostic evaluation of shear-wave elastography in BI-RADS category 4 breast cancer screening: a multicenter, retrospective study.

Authors:  Yi Tang; Minjie Liang; Li Tao; Minjun Deng; Tianfu Li
Journal:  Quant Imaging Med Surg       Date:  2022-02
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.