Evaluation of LIBRA software for fully automated mammographic density assessment in breast cancer risk prediction

Aimilia Gastounioti, Christine Damases Kasi, Christopher G. Scott, Kathleen R. Brandt, Matthew R. Jensen, Carrie B. Hruska, Fang F. Wu, Aaron D. Norman, Emily F. Conant, Stacey J. Winham, Karla Kerlikowske, Despina Kontos, Celine M. Vachon

Research output: Contribution to journalArticle

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 6 17 [standard deviation]) and 1225 matched control patients (median age, 61 years 6 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.

Original languageEnglish (US)
Pages (from-to)24-31
Number of pages8
JournalRadiology
Volume296
Issue number1
DOIs
StatePublished - Jul 2020

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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    Gastounioti, A., Kasi, C. D., Scott, C. G., Brandt, K. R., Jensen, M. R., Hruska, C. B., Wu, F. F., Norman, A. D., Conant, E. F., Winham, S. J., Kerlikowske, K., Kontos, D., & Vachon, C. M. (2020). Evaluation of LIBRA software for fully automated mammographic density assessment in breast cancer risk prediction. Radiology, 296(1), 24-31. https://doi.org/10.1148/radiol.2020192509