Literature DB >> 25734553

Reliability of automated breast density measurements.

Olivier Alonzo-Proulx1, Gordon E Mawdsley, James T Patrie, Martin J Yaffe, Jennifer A Harvey.   

Abstract

PURPOSE: To estimate the reliability of a reference standard two-dimensional area-based method and three automated volumetric breast density measurements by using repeated measures.
MATERIALS AND METHODS: Thirty women undergoing screening mammography consented to undergo a repeated left craniocaudal examination performed by a second technologist in this prospective institutional review board-approved HIPAA-compliant study. Breast density was measured by using an area-based method (Cumulus ABD) and three automated volumetric methods (CumulusV [University of Toronto], Volpara [version 1.4.5; Volpara Solutions, Wellington, New Zealand), and Quantra [version 2.0; Hologic, Danbury, Conn]). Discrepancy between the first and second breast density measurements (Δ1-2) was obtained for each algorithm by subtracting the second measurement from the first. The Δ1-2 values of each algorithm were then analyzed with a random-effects model to derive Bland-Altman-type limits of measurement agreement.
RESULTS: Variability was higher for Cumulus ABD and CumulusV than for Volpara or Quantra. The within-breast density measurement standard deviations were 3.32% (95% confidence interval [CI]: 2.65, 4.44), 3.59% (95% CI: 2.86, 4.48), 0.99% (95% CI: 0.79, 1.33), and 1.64% (95% CI: 1.31, 1.39) for Cumulus ABD, CumulusV, Volpara, and Quantra, respectively. Although the mean discrepancy between repeat breast density measurements was not significantly different from zero for any of the algorithms, larger absolute breast density discrepancy (Δ1-2) values were associated with larger breast density values for Cumulus ABD and CumulusV but not for Volpara and Quantra.
CONCLUSION: Variability in a repeated measurement of breast density is lowest for Volpara and Quantra; these algorithms may be more suited to incorporation into a risk model. (©) RSNA, 2015

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Year:  2015        PMID: 25734553     DOI: 10.1148/radiol.15141686

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  29 in total

1.  Trends in Clinical Breast Density Assessment From the Breast Cancer Surveillance Consortium.

Authors:  B L Sprague; K Kerlikowske; E J A Bowles; G H Rauscher; C I Lee; A N A Tosteson; D L Miglioretti
Journal:  J Natl Cancer Inst       Date:  2019-06-01       Impact factor: 13.506

2.  Effect of Bazedoxifene and Conjugated Estrogen (Duavee) on Breast Cancer Risk Biomarkers in High-Risk Women: A Pilot Study.

Authors:  Carol J Fabian; Lauren Nye; Kandy R Powers; Jennifer L Nydegger; Amy L Kreutzjans; Teresa A Phillips; Trina Metheny; Onalisa Winblad; Carola M Zalles; Christy R Hagan; Merit L Goodman; Byron J Gajewski; Devin C Koestler; Prabhakar Chalise; Bruce F Kimler
Journal:  Cancer Prev Res (Phila)       Date:  2019-08-16

3.  Breast tissue density change after oophorectomy in BRCA mutation carrier patients using visual and volumetric analysis.

Authors:  Augustin Lecler; Ariane Dunant; Suzette Delaloge; Delphine Wehrer; Tania Moussa; Olivier Caron; Corinne Balleyguier
Journal:  Br J Radiol       Date:  2018-01-05       Impact factor: 3.039

4.  Automated mammographic density measurement using Quantra™: comparison with the Royal Australian and New Zealand College of Radiology synoptic scale.

Authors:  Inez Yeo; Judith Akwo; Ernest Ekpo
Journal:  J Med Imaging (Bellingham)       Date:  2020-05-29

5.  Comparison of Clinical and Automated Breast Density Measurements: Implications for Risk Prediction and Supplemental Screening.

Authors:  Kathleen R Brandt; Christopher G Scott; Lin Ma; Amir P Mahmoudzadeh; Matthew R Jensen; Dana H Whaley; Fang Fang Wu; Serghei Malkov; Carrie B Hruska; Aaron D Norman; John Heine; John Shepherd; V Shane Pankratz; Karla Kerlikowske; Celine M Vachon
Journal:  Radiology       Date:  2015-12-22       Impact factor: 11.105

Review 6.  Artificial Intelligence for Mammography and Digital Breast Tomosynthesis: Current Concepts and Future Perspectives.

Authors:  Krzysztof J Geras; Ritse M Mann; Linda Moy
Journal:  Radiology       Date:  2019-09-24       Impact factor: 11.105

7.  Digital Breast Tomosynthesis guided Near Infrared Spectroscopy: Volumetric estimates of fibroglandular fraction and breast density from tomosynthesis reconstructions.

Authors:  Srinivasan Vedantham; Linxi Shi; Kelly E Michaelsen; Venkataramanan Krishnaswamy; Brian W Pogue; Steven P Poplack; Andrew Karellas; Keith D Paulsen
Journal:  Biomed Phys Eng Express       Date:  2015-10-27

8.  Variation in Mammographic Breast Density Assessments Among Radiologists in Clinical Practice: A Multicenter Observational Study.

Authors:  Brian L Sprague; Emily F Conant; Tracy Onega; Michael P Garcia; Elisabeth F Beaber; Sally D Herschorn; Constance D Lehman; Anna N A Tosteson; Ronilda Lacson; Mitchell D Schnall; Despina Kontos; Jennifer S Haas; Donald L Weaver; William E Barlow
Journal:  Ann Intern Med       Date:  2016-07-19       Impact factor: 25.391

Review 9.  Breast density implications and supplemental screening.

Authors:  Athina Vourtsis; Wendie A Berg
Journal:  Eur Radiol       Date:  2018-09-25       Impact factor: 5.315

10.  Automated and Clinical Breast Imaging Reporting and Data System Density Measures Predict Risk for Screen-Detected and Interval Cancers: A Case-Control Study.

Authors:  Karla Kerlikowske; Christopher G Scott; Amir P Mahmoudzadeh; Lin Ma; Stacey Winham; Matthew R Jensen; Fang Fang Wu; Serghei Malkov; V Shane Pankratz; Steven R Cummings; John A Shepherd; Kathleen R Brandt; Diana L Miglioretti; Celine M Vachon
Journal:  Ann Intern Med       Date:  2018-05-01       Impact factor: 25.391

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