Literature DB >> 9097055

The Tabár classification of mammographic parenchymal patterns.

I T Gram1, E Funkhouser, L Tabár.   

Abstract

The purpose of this study was to describe one method of classification, based on anatomic-mammographic correlations, developed by Tabár. We also wanted to examine how the mammograms categorized as low- and high-risk according to Tabár and Wolfe criteria related to each other and to three selected risk factors for breast cancer. The study materials are based on questionnaires and mammograms from 3,640 Norwegian women, aged 40-56 years, participating in the third Tromsö study. The mammograms were categorized into five groups. Line drawings and their pathologic correlates of the five patterns are described in detail. The Tabár classification is based on anatomic-mammographic correlations, following three-dimensional (thick slice technique) histopathologic-mammographic comparisons, rather than simple pattern reading (Wolfe classification). For analysis patterns I-III (Tabár) and N1 and P1 (Wolfe) were grouped into low-risk groups and patterns IV and V (Tabár) and P2 and DY Wolfe) into high-risk groups. The overall agreement on high-risk versus low risk for the two classifications was 54% with a kappa-value of 0.22. The study displays that the strength of association between high-risk mammographic patterns and the three selected risk factors parity, number of children and age at first birth is of greater magnitude when the Tabár instead of the Wolfe classification is applied. More patients are needed to compare the classification directly with the risk of cancer. This study indicates that further development of the classification of mammograms may increase the usefulness of mammographic patterns in research and clinical practice.

Entities:  

Mesh:

Year:  1997        PMID: 9097055     DOI: 10.1016/s0720-048x(96)01138-2

Source DB:  PubMed          Journal:  Eur J Radiol        ISSN: 0720-048X            Impact factor:   3.528


  43 in total

Review 1.  Clinical and epidemiological issues in mammographic density.

Authors:  Valentina Assi; Jane Warwick; Jack Cuzick; Stephen W Duffy
Journal:  Nat Rev Clin Oncol       Date:  2011-12-06       Impact factor: 66.675

2.  Estimation of percentage breast tissue density: comparison between digital mammography (2D full field digital mammography) and digital breast tomosynthesis according to different BI-RADS categories.

Authors:  A S Tagliafico; G Tagliafico; F Cavagnetto; M Calabrese; N Houssami
Journal:  Br J Radiol       Date:  2013-09-12       Impact factor: 3.039

3.  Computing mammographic density from a multiple regression model constructed with image-acquisition parameters from a full-field digital mammographic unit.

Authors:  Lee-Jane W Lu; Thomas K Nishino; Tuenchit Khamapirad; James J Grady; Morton H Leonard; Donald G Brunder
Journal:  Phys Med Biol       Date:  2007-07-30       Impact factor: 3.609

4.  Breast density estimation from high spectral and spatial resolution MRI.

Authors:  Hui Li; William A Weiss; Milica Medved; Hiroyuki Abe; Gillian M Newstead; Gregory S Karczmar; Maryellen L Giger
Journal:  J Med Imaging (Bellingham)       Date:  2016-12-28

5.  Prediction of reader estimates of mammographic density using convolutional neural networks.

Authors:  Georgia V Ionescu; Martin Fergie; Michael Berks; Elaine F Harkness; Johan Hulleman; Adam R Brentnall; Jack Cuzick; D Gareth Evans; Susan M Astley
Journal:  J Med Imaging (Bellingham)       Date:  2019-01-31

6.  Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment.

Authors:  Yuanjie Zheng; Brad M Keller; Shonket Ray; Yan Wang; Emily F Conant; James C Gee; Despina Kontos
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

7.  Parenchymal pattern in women with dense breasts. Variation with age and impact on screening outcomes: observations from a UK screening programme.

Authors:  Laura Ward; S Heller; S Hudson; L Wilkinson
Journal:  Eur Radiol       Date:  2018-05-28       Impact factor: 5.315

8.  Convolutional Neural Network Based Breast Cancer Risk Stratification Using a Mammographic Dataset.

Authors:  Richard Ha; Peter Chang; Jenika Karcich; Simukayi Mutasa; Eduardo Pascual Van Sant; Michael Z Liu; Sachin Jambawalikar
Journal:  Acad Radiol       Date:  2018-07-31       Impact factor: 3.173

9.  Mammographic density and markers of socioeconomic status: a cross-sectional study.

Authors:  Zoe Aitken; Kate Walker; Bernardine H Stegeman; Petra A Wark; Sue M Moss; Valerie A McCormack; Isabel dos Santos Silva
Journal:  BMC Cancer       Date:  2010-02-09       Impact factor: 4.430

10.  Evaluation of mammographic density patterns: reproducibility and concordance among scales.

Authors:  Macarena Garrido-Estepa; Francisco Ruiz-Perales; Josefa Miranda; Nieves Ascunce; Isabel González-Román; Carmen Sánchez-Contador; Carmen Santamariña; Pilar Moreo; Carmen Vidal; Mercé Peris; María P Moreno; Jose A Váquez-Carrete; Francisca Collado-García; Francisco Casanova; María Ederra; Dolores Salas; Marina Pollán
Journal:  BMC Cancer       Date:  2010-09-13       Impact factor: 4.430

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.