Literature DB >> 32396041

Evaluation of LIBRA Software for Fully Automated Mammographic Density Assessment in Breast Cancer Risk Prediction.

Aimilia Gastounioti1, Christine Damases Kasi1, Christopher G Scott1, Kathleen R Brandt1, Matthew R Jensen1, Carrie B Hruska1, Fang F Wu1, Aaron D Norman1, Emily F Conant1, Stacey J Winham1, Karla Kerlikowske1, Despina Kontos1, Celine M Vachon1.   

Abstract

Background The associations of density measures from the publicly available Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) software with breast cancer have primarily focused on estimates from the contralateral breast at the time of diagnosis. Purpose To evaluate LIBRA measures on mammograms obtained before breast cancer diagnosis and compare their performance to established density measures. Materials and Methods For this retrospective case-control study, full-field digital mammograms in for-processing (raw) and for-presentation (processed) formats were obtained (March 2008 to December 2011) in women who developed breast cancer an average of 2 years later and in age-matched control patients. LIBRA measures included absolute dense area and area percent density (PD) from both image formats. For comparison, dense area and PD were assessed by using the research software (Cumulus), and volumetric PD (VPD) and absolute dense volume were estimated with a commercially available software (Volpara). Density measures were compared by using Spearman correlation coefficients (r), and conditional logistic regression (odds ratios [ORs] and 95% confidence intervals [CIs]) was performed to examine the associations of density measures with breast cancer by adjusting for age and body mass index. Results Evaluated were 437 women diagnosed with breast cancer (median age, 62 years ± 17 [standard deviation]) and 1225 matched control patients (median age, 61 years ± 16). LIBRA PD showed strong correlations with Cumulus PD (r = 0.77-0.84) and Volpara VPD (r = 0.85-0.90) (P < .001 for both). For LIBRA, the strongest breast cancer association was observed for PD from processed images (OR, 1.3; 95% CI: 1.1, 1.5), although the PD association from raw images was not significantly different (OR, 1.2; 95% CI: 1.1, 1.4; P = .25). Slightly stronger breast cancer associations were seen for Cumulus PD (OR, 1.5; 95% CI: 1.3, 1.8; processed images; P = .01) and Volpara VPD (OR, 1.4; 95% CI: 1.2, 1.7; raw images; P = .004) compared with LIBRA measures. Conclusion Automated density measures provided by the Laboratory for Individualized Breast Radiodensity Assessment from raw and processed mammograms correlated with established area and volumetric density measures and showed comparable breast cancer associations. © RSNA, 2020 Online supplemental material is available for this article.

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Year:  2020        PMID: 32396041      PMCID: PMC7325699          DOI: 10.1148/radiol.2020192509

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


  26 in total

1.  Mammographic density and the risk and detection of breast cancer.

Authors:  Norman F Boyd; Helen Guo; Lisa J Martin; Limei Sun; Jennifer Stone; Eve Fishell; Roberta A Jong; Greg Hislop; Anna Chiarelli; Salomon Minkin; Martin J Yaffe
Journal:  N Engl J Med       Date:  2007-01-18       Impact factor: 91.245

2.  Mammographic breast density decreases after bariatric surgery.

Authors:  Austin D Williams; Alycia So; Marie Synnestvedt; Colleen M Tewksbury; Despina Kontos; Meng-Kang Hsiehm; Lauren Pantalone; Emily F Conant; Mitchell Schnall; Kristoffel Dumon; Noel Williams; Julia Tchou
Journal:  Breast Cancer Res Treat       Date:  2017-06-28       Impact factor: 4.872

3.  Mammographic Breast Density Assessment Using Automated Volumetric Software and Breast Imaging Reporting and Data System (BIRADS) Categorization by Expert Radiologists.

Authors:  Christine N Damases; Patrick C Brennan; Claudia Mello-Thoms; Mark F McEntee
Journal:  Acad Radiol       Date:  2015-10-26       Impact factor: 3.173

4.  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

5.  Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation.

Authors:  Brad M Keller; Diane L Nathan; Yan Wang; Yuanjie Zheng; James C Gee; Emily F Conant; Despina Kontos
Journal:  Med Phys       Date:  2012-08       Impact factor: 4.071

6.  Breast Cancer Risk Associations with Digital Mammographic Density by Pixel Brightness Threshold and Mammographic System.

Authors:  Tuong L Nguyen; Yoon-Ho Choi; Ye K Aung; Christopher F Evans; Nhut H Trinh; Shuai Li; Gillian S Dite; Myeong-Seong Kim; Patrick C Brennan; Mark A Jenkins; Joohon Sung; Yun-Mi Song; John L Hopper
Journal:  Radiology       Date:  2017-10-16       Impact factor: 11.105

7.  Diagnostic performance of digital versus film mammography for breast-cancer screening.

Authors:  Etta D Pisano; Constantine Gatsonis; Edward Hendrick; Martin Yaffe; Janet K Baum; Suddhasatta Acharyya; Emily F Conant; Laurie L Fajardo; Lawrence Bassett; Carl D'Orsi; Roberta Jong; Murray Rebner
Journal:  N Engl J Med       Date:  2005-09-16       Impact factor: 91.245

8.  Breast parenchymal patterns in processed versus raw digital mammograms: A large population study toward assessing differences in quantitative measures across image representations.

Authors:  Aimilia Gastounioti; Andrew Oustimov; Brad M Keller; Lauren Pantalone; Meng-Kang Hsieh; Emily F Conant; Despina Kontos
Journal:  Med Phys       Date:  2016-11       Impact factor: 4.071

9.  Comparison of percent density from raw and processed full-field digital mammography data.

Authors:  Celine M Vachon; Erin Ee Fowler; Gail Tiffenberg; Christopher G Scott; V Shane Pankratz; Thomas A Sellers; John J Heine
Journal:  Breast Cancer Res       Date:  2013-01-04       Impact factor: 6.466

10.  Impact of type of full-field digital image on mammographic density assessment and breast cancer risk estimation: a case-control study.

Authors:  Marta Cecilia Busana; Amanda Eng; Rachel Denholm; Mitch Dowsett; Sarah Vinnicombe; Steve Allen; Isabel Dos-Santos-Silva
Journal:  Breast Cancer Res       Date:  2016-09-26       Impact factor: 6.466

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  3 in total

1.  Fully Automated Volumetric Breast Density Estimation from Digital Breast Tomosynthesis.

Authors:  Aimilia Gastounioti; Lauren Pantalone; Christopher G Scott; Eric A Cohen; Fang F Wu; Stacey J Winham; Matthew R Jensen; Andrew D A Maidment; Celine M Vachon; Emily F Conant; Despina Kontos
Journal:  Radiology       Date:  2021-09-14       Impact factor: 11.105

2.  Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment.

Authors:  Omid Haji Maghsoudi; Aimilia Gastounioti; Christopher Scott; Lauren Pantalone; Fang-Fang Wu; Eric A Cohen; Stacey Winham; Emily F Conant; Celine Vachon; Despina Kontos
Journal:  Med Image Anal       Date:  2021-07-02       Impact factor: 13.828

3.  Lifestyle, Behavioral, and Dietary Risk Factors in Relation to Mammographic Breast Density in Women at High Risk for Breast Cancer.

Authors:  Thomas P Ahern; Brian L Sprague; Nicholas H Farina; Erin Tsai; Melissa Cuke; Despina Kontos; Marie E Wood
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2021-02-22       Impact factor: 4.090

  3 in total

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