Literature DB >> 27236612

Association between Breast Parenchymal Complexity and False-Positive Recall From Digital Mammography Versus Breast Tomosynthesis: Preliminary Investigation in the ACRIN PA 4006 Trial.

Shonket Ray1, Lin Chen1, Brad M Keller1, Jinbo Chen2, Emily F Conant1, Despina Kontos3.   

Abstract

RATIONALE AND
OBJECTIVES: We investigate associations between measures of mammographic parenchymal complexity and false-positive (FP) recall from screening with digital mammography (DM) versus digital breast tomosynthesis (DBT).
MATERIALS AND METHODS: We retrospectively analyzed data from 541 women recruited by the American College of Radiology Imaging Network 4006 trial, designed to evaluate callback and detection rates from screening with DM versus combined DM and DBT. Of these, 68 and 56 were FPs based on DM alone versus the combined DM/DBT readings, respectively. Mammographic complexity was quantified with computerized texture analysis and percent density. Logistic regression was performed to evaluate associations between extracted features and FP recall, after adjusting for age and number of previous benign biopsies. Odds ratios and area under the curve (AUC) of the receiver operating characteristic were used to assess association strength.
RESULTS: For DM, age, previous benign biopsies and texture features of correlation, inverse difference moment, sum average, and sum variance were deemed as significant predictors (P <.05) of FP recall, with an AUC = 0.77. For DBT, age was the only significant predictor of FP recall with AUC = 0.64. Using exploratory receiver operating characteristic thresholds for which no true-positives would be missed, a potential FP reduction of 23.5% and 8.9% was demonstrated, respectively, for DM alone versus DM/DBT.
CONCLUSION: Measures of breast complexity measured on 2D digital mammograms are indicative of the likelihood for FP recall from screening with DM, and could help identify women who could benefit from supplemental screening, including DBT.
Copyright © 2016 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Digital mammography; breast cancer risk assessment; digital breast tomosynthesis; false-positive recall; texture analysis

Mesh:

Year:  2016        PMID: 27236612      PMCID: PMC4958584          DOI: 10.1016/j.acra.2016.02.019

Source DB:  PubMed          Journal:  Acad Radiol        ISSN: 1076-6332            Impact factor:   3.173


  26 in total

1.  Computerized analysis of mammographic parenchymal patterns for assessing breast cancer risk: effect of ROI size and location.

Authors:  Hui Li; Maryellen L Giger; Zhimin Huo; Olufunmilayo I Olopade; Li Lan; Barbara L Weber; Ioana Bonta
Journal:  Med Phys       Date:  2004-03       Impact factor: 4.071

2.  Analysis of parenchymal texture with digital breast tomosynthesis: comparison with digital mammography and implications for cancer risk assessment.

Authors:  Despina Kontos; Lynda C Ikejimba; Predrag R Bakic; Andrea B Troxel; Emily F Conant; Andrew D A Maidment
Journal:  Radiology       Date:  2011-07-19       Impact factor: 11.105

3.  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

4.  Personalizing mammography by breast density and other risk factors for breast cancer: analysis of health benefits and cost-effectiveness.

Authors:  John T Schousboe; Karla Kerlikowske; Andrew Loh; Steven R Cummings
Journal:  Ann Intern Med       Date:  2011-07-05       Impact factor: 25.391

5.  Effect of age and breast density on screening mammograms with false-positive findings.

Authors:  C D Lehman; E White; S Peacock; M J Drucker; N Urban
Journal:  AJR Am J Roentgenol       Date:  1999-12       Impact factor: 3.959

6.  Breast patterns as an index of risk for developing breast cancer.

Authors:  J N Wolfe
Journal:  AJR Am J Roentgenol       Date:  1976-06       Impact factor: 3.959

7.  Predicting the cumulative risk of false-positive mammograms.

Authors:  C L Christiansen; F Wang; M B Barton; W Kreuter; J G Elmore; A E Gelfand; S W Fletcher
Journal:  J Natl Cancer Inst       Date:  2000-10-18       Impact factor: 13.506

8.  Parenchymal texture analysis in digital breast tomosynthesis for breast cancer risk estimation: a preliminary study.

Authors:  Despina Kontos; Predrag R Bakic; Ann-Katherine Carton; Andrea B Troxel; Emily F Conant; Andrew D A Maidment
Journal:  Acad Radiol       Date:  2009-03       Impact factor: 3.173

Review 9.  The benefits and harms of breast cancer screening: an independent review.

Authors: 
Journal:  Lancet       Date:  2012-10-30       Impact factor: 79.321

10.  Twenty five year follow-up for breast cancer incidence and mortality of the Canadian National Breast Screening Study: randomised screening trial.

Authors:  Anthony B Miller; Claus Wall; Cornelia J Baines; Ping Sun; Teresa To; Steven A Narod
Journal:  BMJ       Date:  2014-02-11
View more
  1 in total

1.  Factors associated with false-positive mammography at first screen in an Asian population.

Authors:  Peh Joo Ho; Chek Mei Bok; Hanis Mariyah Mohd Ishak; Li Yan Lim; Jenny Liu; Fuh Yong Wong; Kee Seng Chia; Min-Han Tan; Wen Yee Chay; Mikael Hartman; Jingmei Li
Journal:  PLoS One       Date:  2019-03-11       Impact factor: 3.240

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.