Literature DB >> 16513904

BI-RADS classification for management of abnormal mammograms.

Margaret M Eberl1, Chester H Fox, Stephen B Edge, Cathleen A Carter, Martin C Mahoney.   

Abstract

The Breast Imaging Reporting and Data System (BI-RADS), developed by the American College of Radiology, provides a standardized classification for mammographic studies. This system demonstrates good correlation with the likelihood of breast malignancy. The BI-RADS system can inform family physicians about key findings, identify appropriate follow-up and management and encourage the provision of educational and emotional support to patients.

Entities:  

Mesh：

Year:  2006        PMID： 16513904     DOI： 10.3122/jabfm.19.2.161

Source DB:  PubMed          Journal:  J Am Board Fam Med        ISSN： 1557-2625            Impact factor:   2.657


  36 in total

1.  The influence of clinicopathological features on the predictive accuracy of conventional breast imaging in determining the extent of screen-detected high-grade pure ductal carcinoma in situ.

Authors:  L Hayward; R S Oeppen; A V Grima; G T Royle; C M Rubin; R I Cutress
Journal:  Ann R Coll Surg Engl       Date:  2011-07       Impact factor: 1.891

2.  Mapping LIDC, RadLex™, and lung nodule image features.

Authors:  Pia Opulencia; David S Channin; Daniela S Raicu; Jacob D Furst
Journal:  J Digit Imaging       Date:  2011-04       Impact factor: 4.056

3.  Identification of a beta-casein-like peptide in breast nipple aspirate fluid that is associated with breast cancer.

Authors:  Edward R Sauter; Wade Davis; Wenyi Qin; Sarah Scanlon; Brian Mooney; Karen Bromert; William R Folk
Journal:  Biomark Med       Date:  2009-10       Impact factor: 2.851

4.  Organization Communication Factors and Abnormal Mammogram Follow-up: a Qualitative Study Among Ethnically Diverse Women Across Three Healthcare Systems.

Authors:  Jazmine D Kenny; Leah S Karliner; Karla Kerlikowske; Celia P Kaplan; Ana Fernandez-Lamothe; Nancy J Burke
Journal:  J Gen Intern Med       Date:  2020-06-29       Impact factor: 5.128

5.  Automated annotation and classification of BI-RADS assessment from radiology reports.

Authors:  Sergio M Castro; Eugene Tseytlin; Olga Medvedeva; Kevin Mitchell; Shyam Visweswaran; Tanja Bekhuis; Rebecca S Jacobson
Journal:  J Biomed Inform       Date:  2017-04-18       Impact factor: 6.317

6.  A deep learning framework for efficient analysis of breast volume and fibroglandular tissue using MR data with strong artifacts.

Authors:  Tatyana Ivanovska; Thomas G Jentschke; Amro Daboul; Katrin Hegenscheid; Henry Völzke; Florentin Wörgötter
Journal:  Int J Comput Assist Radiol Surg       Date:  2019-03-06       Impact factor: 2.924

7.  Feasibility of single-voxel MRS measurement of apparent diffusion coefficient of water in breast tumors.

Authors:  C A Corum; A D McIntosh; P J Bolan; M Nelson; A L Snyder; N J Powell; J Boyum; T H Emory; D Yee; T M Tuttle; L I Everson; M Garwood
Journal:  Magn Reson Med       Date:  2009-05       Impact factor: 4.668

8.  Locus-specific databases and recommendations to strengthen their contribution to the classification of variants in cancer susceptibility genes.

Authors:  Marc S Greenblatt; Lawrence C Brody; William D Foulkes; Maurizio Genuardi; Robert M W Hofstra; Magali Olivier; Sharon E Plon; Rolf H Sijmons; Olga Sinilnikova; Amanda B Spurdle
Journal:  Hum Mutat       Date:  2008-11       Impact factor: 4.878

9.  How to improve your breast cancer program: Standardized reporting using the new American College of Radiology Breast Imaging-Reporting and Data System.

Authors:  Haydee Ojeda-Fournier; Judy Q Nguyen
Journal:  Indian J Radiol Imaging       Date:  2009 Oct-Dec

Review 10.  Ultrasound as an Adjunct to Mammography for Breast Cancer Screening: A Health Technology Assessment.

Authors: 
Journal:  Ont Health Technol Assess Ser       Date:  2016-07-01
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.