Preliminary evaluation of the publicly available Laboratory for Breast Radiodensity Assessment (LIBRA) software tool: comparison of fully automated area and volumetric density measures in a case-control study with digital mammography

Brad M Keller, Jinbo Chen, Dania Daye, Emily F Conant, Despina Kontos, Brad M Keller, Jinbo Chen, Dania Daye, Emily F Conant, Despina Kontos

Abstract

Introduction: Breast density, commonly quantified as the percentage of mammographically dense tissue area, is a strong breast cancer risk factor. We investigated associations between breast cancer and fully automated measures of breast density made by a new publicly available software tool, the Laboratory for Individualized Breast Radiodensity Assessment (LIBRA).

Methods: Digital mammograms from 106 invasive breast cancer cases and 318 age-matched controls were retrospectively analyzed. Density estimates acquired by LIBRA were compared with commercially available software and standard Breast Imaging-Reporting and Data System (BI-RADS) density estimates. Associations between the different density measures and breast cancer were evaluated by using logistic regression after adjustment for Gail risk factors and body mass index (BMI). Area under the curve (AUC) of the receiver operating characteristic (ROC) was used to assess discriminatory capacity, and odds ratios (ORs) for each density measure are provided.

Results: All automated density measures had a significant association with breast cancer (OR = 1.47-2.23, AUC = 0.59-0.71, P < 0.01) which was strengthened after adjustment for Gail risk factors and BMI (OR = 1.96-2.64, AUC = 0.82-0.85, P < 0.001). In multivariable analysis, absolute dense area (OR = 1.84, P < 0.001) and absolute dense volume (OR = 1.67, P = 0.003) were jointly associated with breast cancer (AUC = 0.77, P < 0.01), having a larger discriminatory capacity than models considering the Gail risk factors alone (AUC = 0.64, P < 0.001) or the Gail risk factors plus standard area percent density (AUC = 0.68, P = 0.01). After BMI was further adjusted for, absolute dense area retained significance (OR = 2.18, P < 0.001) and volume percent density approached significance (OR = 1.47, P = 0.06). This combined area-volume density model also had a significantly (P < 0.001) improved discriminatory capacity (AUC = 0.86) relative to a model considering the Gail risk factors plus BMI (AUC = 0.80).

Conclusions: Our study suggests that new automated density measures may ultimately augment the current standard breast cancer risk factors. In addition, the ability to fully automate density estimation with digital mammography, particularly through the use of publically available breast density estimation software, could accelerate the translation of density reporting in routine breast cancer screening and surveillance protocols and facilitate broader research into the use of breast density as a risk factor for breast cancer.

Figures

Fig. 1
Fig. 1
Example of density segmentation using the LIBRA software tool. a Left mediolateral oblique “For Processing” raw mammogram of a 57-year-old woman with a negative screening exam. b Breast image intensity histogram with fuzzy c-means clustering centroids (vertical lines). c Intensity-clustered breast image. d The final breast and dense tissue segmentation. LIBRA Laboratory for Individualized Breast Radiodensity Assessment
Fig. 2
Fig. 2
Relationship between different breast density measures. The association between the area-based and volumetric breast density is provided for both (a) absolute measures and (b) percent measures. The relationship between absolute and percent breast density measures are shown for (c) volumetric and (d) area density. Cancer cases are demarcated by ‘x’, controls by ‘o’. Regression lines, equations, and Spearman correlations are provided for reference
Fig. 3
Fig. 3
Relationship between BMI and breast density measures. The association between area-based and volumetric breast density versus BMI is provided for (a) area percent density (PD %), (b) volume percent density, (c) absolute dense area and (d) absolute dense volume. Cancer cases are demarcated by ‘x’; controls by ‘o’. Regression lines, equations, and Spearman correlations are also provided for reference. BMI body mass index
Fig. 4
Fig. 4
Mammogram of a breast consisting of a similar volumetric and area breast density. Example of (a) a mediolateral oblique view, “For Processing” (i.e., raw) mammogram and (b) the dense area tissue segmentation of a 56-year-old woman with a negative screening exam who has similar volumetric percent density (VD % = 21.4 %) and area breast percent density (PD % = 25.3 %) estimates
Fig. 5
Fig. 5
Mammogram of a breast consisting of a higher area breast density than volumetric density. Example of (a) a mediolateral oblique view, “For Processing” (i.e., raw) mammogram and (b) the dense area tissue segmentation of a 59-year-old woman with a negative screening exam who has different volumetric percent density (VD % = 14.6 %) and area breast percent density (PD % = 37.4 %) estimates

References

    1. Kerlikowske K. Evidence-based breast cancer prevention: the importance of individual risk. Ann Intern Med. 2009;151:750–2. doi: 10.7326/0003-4819-151-10-200911170-00012.
    1. Gail MH. Personalized estimates of breast cancer risk in clinical practice and public health. Stat Med. 2011;30:1090–104. doi: 10.1002/sim.4187.
    1. Kelsey JL, Gammon MD, John EM. Reproductive factors and breast cancer. Epidemiol Rev. 1993;15:36–47.
    1. Pharoah PD, Day NE, Duffy S, Easton DF, Ponder BA. Family history and the risk of breast cancer: a systematic review and meta-analysis. Int J Cancer. 1997;71:800–9. doi: 10.1002/(SICI)1097-0215(19970529)71:5<800::AID-IJC18>;2-B.
    1. Van den Brandt PA, Spiegelman D, Yaun S-S, Adami H-O, Beeson L, Folsom AR, et al. Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol. 2000;152:514–27. doi: 10.1093/aje/152.6.514.
    1. Easton DF, Ford D, Bishop DT. Breast and ovarian cancer incidence in BRCA1-mutation carriers. Breast Cancer Linkage Consortium. Am J Hum Genet. 1995;56:265–71. doi: 10.1002/ajmg.1320560305.
    1. Amir E, Freedman OC, Seruga B, Evans DG. Assessing women at high risk of breast cancer: a review of risk assessment models. J Natl Cancer Inst. 2010;102:680–91. doi: 10.1093/jnci/djq088.
    1. Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, et al. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst. 1989;81:1879–86. doi: 10.1093/jnci/81.24.1879.
    1. Gail MH, Costantino JP, Pee D, Bondy M, Newman L, Selvan M, et al. Projecting individualized absolute invasive breast cancer risk in African American women. J Natl Cancer Inst. 2007;99:1782–92. doi: 10.1093/jnci/djm223.
    1. Matsuno RK, Costantino JP, Ziegler RG, Anderson GL, Li H, Pee D, et al. Projecting individualized absolute invasive breast cancer risk in Asian and Pacific Islander American women. J Natl Cancer Inst. 2011;103:951–61. doi: 10.1093/jnci/djr154.
    1. Rockhill B, Spiegelman D, Byrne C, Hunter DJ, Colditz GA. Validation of the Gail et al. model of breast cancer risk prediction and implications for chemoprevention. J Natl Cancer Inst. 2001;93:358–66. doi: 10.1093/jnci/93.5.358.
    1. Boyd NF, Lockwood GA, Byng JW, Tritchler DL, Yaffe MJ. Mammographic densities and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 1998;7:1133–44.
    1. D’Orsi CJ, Bassett LW, Berg WA, Feig SA, Jackson VP, Kopans DB. Breast imaging reporting and data system: ACR BI-RADS, Mammography. 4. American College of Radiology: Reston, VA; 2003.
    1. Boyd NF, Guo H, Martin LJ, Sun L, Stone J, Fishell E, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 2007;356:227–36. doi: 10.1056/NEJMoa062790.
    1. Vacek PM, Geller BM. A prospective study of breast cancer risk using routine mammographic breast density measurements. Cancer Epidemiol Biomarkers Prev. 2004;13:715–22.
    1. Harvey JA, Bovbjerg VE. Quantitative assessment of mammographic breast density: relationship with breast cancer risk. Radiology. 2004;230:29–41. doi: 10.1148/radiol.2301020870.
    1. Vachon CM, Brandt KR, Ghosh K, Scott CG, Maloney SD, Carston MJ, et al. Mammographic breast density as a general marker of breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2007;16:43–9. doi: 10.1158/1055-9965.EPI-06-0738.
    1. McCormack VA, dos Santos SI. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev. 2006;15:1159–69. doi: 10.1158/1055-9965.EPI-06-0034.
    1. Keller B, Conant E, Oh H, Kontos D. Breast cancer risk prediction via area and volumetric estimates of breast density. In: Maidment ADA, Bakic PR, Gavenonis S, editors. 11th International Workshop on Breast Imaging (IWDM), Lecture Notes in Computer Science (LNCS) Berlin Heidelberg: Springer-Verlag; 2012. pp. 236–43.
    1. Ding J, Warren R, Warsi I, Day N, Thompson D, Brady M, et al. Evaluating the effectiveness of using standard mammogram form to predict breast cancer risk: case–control study. Cancer Epidemiol Biomarkers Prev. 2008;17:1074–81. doi: 10.1158/1055-9965.EPI-07-2634.
    1. Shepherd JA, Kerlikowske K, Ma L, Duewer F, Fan B, Wang J, et al. Volume of mammographic density and risk of breast cancer. Cancer Epidemiol Biomarkers Prev. 2011;20:1473–82. doi: 10.1158/1055-9965.EPI-10-1150.
    1. Nickson C, Arzhaeva Y, Aitken Z, Elgindy T, Buckley M, Li M, et al. AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes. Breast Cancer Res. 2013;15:R80. doi: 10.1186/bcr3474.
    1. Li J, Szekely L, Eriksson L, Heddson B, Sundbom A, Czene K, et al. High-throughput mammographic-density measurement: a tool for risk prediction of breast cancer. Breast Cancer Res. 2012;14:R114. doi: 10.1186/bcr3238.
    1. Heine JJ, Cao K, Rollison DE. Calibrated measures for breast density estimation. Acad Radiol. 2011;18:547–55. doi: 10.1016/j.acra.2010.12.007.
    1. Boyd N, Martin L, Gunasekara A, Melnichouk O, Maudsley G, Peressotti C, et al. Mammographic density and breast cancer risk: evaluation of a novel method of measuring breast tissue volumes. Cancer Epidemiol Biomarkers Prev. 2009;18:1754–62. doi: 10.1158/1055-9965.EPI-09-0107.
    1. Aitken Z, McCormack VA, Highnam RP, Martin L, Gunasekara A, Melnichouk O, et al. Screen-film mammographic density and breast cancer risk: a comparison of the volumetric standard mammogram form and the interactive threshold measurement methods. Cancer Epidemiol Biomarkers Prev. 2010;19:418–28. doi: 10.1158/1055-9965.EPI-09-1059.
    1. Kopans DB. Basic physics and doubts about relationship between mammographically determined tissue density and breast cancer risk. Radiology. 2008;246:348–53. doi: 10.1148/radiol.2461070309.
    1. Jeffreys M, Harvey J, Highnam R. Digital Mammography. Heidelberg, Germany: Springer; 2010. Comparing a new volumetric breast density method (VolparaTM) to cumulus; pp. 408–13.
    1. Ciatto S, Bernardi D, Calabrese M, Durando M, Gentilini MA, Mariscotti G, et al. A first evaluation of breast radiological density assessment by QUANTRA software as compared to visual classification. Breast. 2012;21:503–6. doi: 10.1016/j.breast.2012.01.005.
    1. Pepe MS, Kerr KF, Longton G, Wang Z. Testing for improvement in prediction model performance. Stat Med. 2013;32:1467–82. doi: 10.1002/sim.5727.
    1. Center for Biomedical Image Computing and Analytics. Laboratory for Individualized Breast Radiodensity Assessment (LIBRA) homepage. . Accessed date: August 19, 2015
    1. Keller BM, Nathan DL, Wang Y, Zheng Y, Gee JC, Conant EF, et al. 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. Med Phys. 2012;39:4903–17. doi: 10.1118/1.4736530.
    1. Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities. Phys Med Biol. 1994;39:1629–38. doi: 10.1088/0031-9155/39/10/008.
    1. Highnam R, Jeffreys M, McCormack V, Warren R, Davey Smith G, Brady M. Comparing measurements of breast density. Phys Med Biol. 2007;52:5881–95. doi: 10.1088/0031-9155/52/19/010.
    1. Hartman K, Highnam R, Warren R, Jackson V. Volumetric Assessment of Breast Tissue Composition from FFDM Images. In: Krupinski EA, editor. 9th International Workshop on Digital Mammography (IWDM), Lecture Notes in Computer Science (LNCS) Berlin Heidelberg: Springer-Verlag; 2008. pp. 33–9.
    1. Rubin DB. Inference and missing data. Biometrika. 1976;63:581–92. doi: 10.1093/biomet/63.3.581.
    1. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988;44:837–45. doi: 10.2307/2531595.
    1. Draper N, Smith H. Applied Regression Analysis (Wiley Series in Probability and Statistics) New York: Wiley-Interscience; 1998.
    1. Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics. Bioinformatics. 2007;23:2507–17. doi: 10.1093/bioinformatics/btm344.
    1. Chen J, Pee D, Ayyagari R, Graubard B, Schairer C, Byrne C, et al. Projecting absolute invasive breast cancer risk in white women with a model that includes mammographic density. J Natl Cancer Inst. 2006;98:1215–26. doi: 10.1093/jnci/djj332.
    1. Tice JA, Cummings SR, Ziv E, Kerlikowske K. Mammographic breast density and the Gail model for breast cancer risk prediction in a screening population. Breast Cancer Res Treat. 2005;94:115–22. doi: 10.1007/s10549-005-5152-4.
    1. Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: development and validation of a new predictive model. Ann Intern Med. 2008;148:337–47. doi: 10.7326/0003-4819-148-5-200803040-00004.
    1. Ooms EA, Zonderland HM, Eijkemans MJ, Kriege M, Mahdavian Delavary B, Burger CW, et al. Mammography: interobserver variability in breast density assessment. Breast. 2007;16:568–76. doi: 10.1016/j.breast.2007.04.007.
    1. Vachon CM, Scott CG, Fasching PA, Hall P, Tamimi RM, Li J, et al. Common breast cancer susceptibility variants in LSP1 and RAD51L1 are associated with mammographic density measures that predict breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2012;21:1156–66. doi: 10.1158/1055-9965.EPI-12-0066.
    1. Wolfe JN. Breast patterns as an index of risk for developing breast cancer. AJR Am J Roentgenol. 1976;126:1130–7. doi: 10.2214/ajr.126.6.1130.
    1. Keller BM, Nathan DL, Gavenonis SC, Chen J, Conant EF, Kontos D. Reader variability in breast density estimation from full-field digital mammograms: the effect of image postprocessing on relative and absolute measures. Acad Radiol. 2013;20:560–8. doi: 10.1016/j.acra.2013.01.003.
    1. Yaffe MJ, Boone JM, Packard N, Alonzo-Proulx O, Huang SY, Peressotti CL, et al. The myth of the 50–50 breast. Med Phys. 2009;36:5437–43. doi: 10.1118/1.3250863.
    1. Wang J, Azziz A, Fan B, Malkov S, Klifa C, Newitt D, et al. Agreement of mammographic measures of volumetric breast density to MRI. PLoS One. 2013;8:e81653. doi: 10.1371/journal.pone.0081653.
    1. Friedewald SM, Rafferty EA, Rose SL, Durand MA, Plecha DM, Greenberg JS, et al. Breast cancer screening using tomosynthesis in combination with digital mammography. JAMA. 2014;311:2499–507. doi: 10.1001/jama.2014.6095.
    1. Boyd NF, Martin LJ, Sun L, Guo H, Chiarelli A, Hislop G, et al. Body size, mammographic density, and breast cancer risk. Cancer Epidemiol Biomarkers Prev. 2006;15:2086–92. doi: 10.1158/1055-9965.EPI-06-0345.
    1. Munsell MF, Sprague BL, Berry DA, Chisholm G, Trentham-Dietz A. Body mass index and breast cancer risk according to postmenopausal estrogen-progestin use and hormone receptor status. Epidemiol Rev. 2014;36:114–36. doi: 10.1093/epirev/mxt010.
    1. Berstad P, Coates RJ, Bernstein L, Folger SG, Malone KE, Marchbanks PA, et al. A case–control study of body mass index and breast cancer risk in white and African-American women. Cancer Epidemiol Biomarkers Prev. 2010;19:1532–44. doi: 10.1158/1055-9965.EPI-10-0025.
    1. Pepe MS, Etzioni R, Feng Z, Potter JD, Thompson ML, Thornquist M, et al. Phases of biomarker development for early detection of cancer. J Natl Cancer Inst. 2001;93:1054–61. doi: 10.1093/jnci/93.14.1054.
    1. Pepe MS, Fan J, Seymour CW, Li C, Huang Y, Feng Z. Biases introduced by choosing controls to match risk factors of cases in biomarker research. Clin Chem. 2012;58:1242–51. doi: 10.1373/clinchem.2012.186007.

Source: PubMed

Patrocinadores y Colaboradores

Condiciones médicas

Intervenciones de drogas

3
Suscribir