Automated volumetric breast density measures: differential change between breasts in women with and without breast cancer

BACKGROUND

Given that breast cancer and normal dense fibroglandular tissue have similar radiographic attenuation, we examine whether automated volumetric density measures identify a differential change between breasts in women with cancer and compare to healthy controls.

METHODS

Eligible cases (n = 1160) had unilateral invasive breast cancer and bilateral full-field digital mammograms (FFDMs) at two time points: within 2 months and 1-5 years before diagnosis. Controls (n = 2360) were matched to cases on age and date of FFDMs. Dense volume (DV) and volumetric percent density (VPD) for each breast were assessed using Volpara™. Differences in DV and VPD between mammograms (median 3 years apart) were calculated per breast separately for cases and controls and their difference evaluated by using the Wilcoxon signed-rank test. To simulate clinical practice where cancer laterality is unknown, we examined whether the absolute difference between breasts can discriminate cases from controls using area under the ROC curve (AUC) analysis, adjusting for age, BMI, and time.

RESULTS

Among cases, the VPD and DV between mammograms of the cancerous breast decreased to a lesser degree (- 0.26% and - 2.10 cm³) than the normal breast (- 0.39% and - 2.74 cm³) for a difference of 0.13% (p value < 0.001) and 0.63 cm³ (p = 0.002), respectively. Among controls, the differences between breasts were nearly identical for VPD (- 0.02 [p = 0.92]) and DV (0.05 [p = 0.77]). The AUC for discriminating cases from controls using absolute difference between breasts was 0.54 (95% CI 0.52, 0.56) for VPD and 0.56 (95% CI, 0.54, 0.58) for DV.

CONCLUSION

There is a small relative increase in volumetric density measures over time in the breast with cancer which is not found in the normal breast. However, the magnitude of this difference is small, and this measure alone does not appear to be a good discriminator between women with and without breast cancer.

Abstract 588: Relationship of serum progesterone and progesterone metabolites with mammographic density

Background:

Mammographic breast density (MBD) is a strong breast cancer (BC) risk factor, but its biologic underpinnings are poorly understood. Use of estrogen plus progestin menopausal hormone therapy is linked to increased MBD and BC risk. Experimental data suggest that ratios of tumor promoting (5α-dihydroprogesterone [5αP]) and anti-tumorigenic (3α-dihydroprogesterone [3αP]) progesterone metabolite levels may be related to BC risk. However, relationships of endogenous progesterone and its metabolites with MBD and BC risk have not been established. Accordingly, we assessed levels of circulating progesterone and its metabolites with MBD.

Methods:

In this cross-sectional study, serum progesterone and its metabolites were quantified using a novel liquid chromatography-tandem mass spectrometry assay in 103 postmenopausal and 52 premenopausal (luteal menstrual cycle phase) women, ages 40-65, undergoing diagnostic image-guided ipsilateral breast biopsy. MBD was measured as percent fibroglandular volume (MBD-V) on pre-biopsy digital mammograms using single X-ray absorptiometry. Square-root transformed MBD-V was examined across tertile categories of progesterone/progesterone metabolites using age and body mass index (BMI)-adjusted linear regression models.

Results:

Concentrations of the hormones were as follows among postmenopausal women: progesterone [mean: 12.6 pmol/L (range: 5.2-45.8)], 3αP [5.6 pmol/L (1.4-18.8)], 5αP [100 pmol/L (16.7-388)], and 5αP/3αP ratio [26.1 (2.1-150)]; and among luteal phase premenopausal women: progesterone [2063 pmol/L (13.6-7098), 3αP [12.7 pmol/L (2.4-64.4)], 5αP [243 pmol/L (25.3-774)], 5αP/3αP ratio [25.9 (2.3-73.7)]. Among postmenopausal women, progesterone was positively associated with MBD-V (Tertile 3 vs. 1: β=0.68, p-trend=0.02). A similar borderline positive association was observed among premenopausal women (β=0.74, p-trend=0.10). Additional adjustment for circulating estradiol did not substantively alter observed associations. Levels of 3αP, 5αP and the 5αP/3αP ratio were not associated with MBD-V among pre- or postmenopausal women.

Conclusions:

Concentrations of progesterone and it metabolites show substantial inter-woman variation. We observed a positive association between endogenous progesterone and MBD-V among both postmenopausal and premenopausal luteal phase women. We did not observe an association with the ratio of 5αP to 3αP levels and MBD-V. These findings suggest the need for additional studies to understand the biological basis of the role of progesterone and its metabolites in MBD and BC risk.

Citation Format: Manila Hada, Hannah Oh, Sharon Fan, Roni T. Falk, Berta Geller, Pamela Vacek, Donald Weaver, John Shepherd, Jeff Wang, Bo Fan, Amir P. Mahmoudzadeh, Serghei Malkov, Sally Herschorn, Louise A. Brinton, Xia Xu, Mark E. Sherman, Britton Trabert, Gretchen L. Gierach. Relationship of serum progesterone and progesterone metabolites with mammographic density [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 588.

Abstract 3260: Utilizing digital pathology to understand breast epithelial characteristics of benign breast disease among women undergoing diagnostic image-guided breast biopsy

Introduction: Defining the histologic correlates of mammographic breast density (MD) may provide insights into why elevated MD is related to increased breast cancer risk. Studies suggest that reduced terminal ductal lobular (TDLU) involution is associated with elevated MD, and both are independent breast cancer risk factors among women who have undergone a biopsy for benign breast disease (BBD). Prior digital histologic analyses of normal breast tissues revealed epithelial nuclear density (END) and TDLU involution are correlated. Accordingly, we examined associations of END with TDLU involution and MD in clinical biopsies. Methods: We analyzed 262 image-guided breast biopsies diagnosed as BBD from 224 women. TDLU involution was visually assessed as TDLU count/mm2 and TDLU span (inversely related to level of involution) in background normal tissue, evaluated using digitized images. The Genie Classifier (Aperio) was applied to images to estimate nuclei count per unit epithelial area, (END). Single X-ray Absorptiometry of pre-biopsy craniocaudal digital mammograms was applied to measure global MD (percent fibroglandular volume (%FGV)). Analysis of covariance, adjusted for age and body mass index, examined mean END differences across tertiles of TDLU/MD measures. Analyses were conducted at the biopsy level using SAS PROC GENMOD to account for within-woman correlations. All tests were two-tailed. Results: Overall, 67% of BBD biopsies were proliferative. Higher END was observed among proliferative than non-proliferative BBD (median END: 10,187 vs. 9,953 respectively; p=0.04). Among all women, END significantly increased with increasing tertiles of TDLU measures (p-trends: TDLU count/100mm2=0.0001, TDLU span=0.046). Whereas TDLU metrics were positively associated with %FGV, no relationship was observed between END and %FGV. In analyses stratified by BBD severity, however, END and %FGV were positively associated among women with non-proliferative disease (p-trend=0.04), findings not observed with proliferative disease. Conclusions: Automated END and visually assessed TDLU involution metrics were positively associated with each other and with MD. However, associations were diluted for proliferative lesions, suggesting that applying automated digital pathology tools to unsegmented digital images of whole sections of BBD biopsies does not demonstrate the same associations with MD as visual assessment of TDLU involution.

Citation Format: Maeve Mullooly, Samantha Puvanesarajah, Shaoqi Fan, Ruth M. Pfeiffer, Linnea Olsson, Manila Hada, Erin L. Kirk, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Amir P. Mahmoudzadeh, Jeff Wang, Stephen M. Hewitt, Sally D. Herschorn, Mark E. Sherman, Melissa A. Troester, Gretchen L. Gierach. Utilizing digital pathology to understand breast epithelial characteristics of benign breast disease among women undergoing diagnostic image-guided breast biopsy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3260.

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

BACKGROUND

In 30 states, women who have had screening mammography are informed of their breast density on the basis of Breast Imaging Reporting and Data System (BI-RADS) density categories estimated subjectively by radiologists. Variation in these clinical categories across and within radiologists has led to discussion about whether automated BI-RADS density should be reported instead.

OBJECTIVE

To determine whether breast cancer risk and detection are similar for automated and clinical BI-RADS density measures.

DESIGN

Case-control.

SETTING

San Francisco Mammography Registry and Mayo Clinic.

PARTICIPANTS

1609 women with screen-detected cancer, 351 women with interval invasive cancer, and 4409 matched control participants.

MEASUREMENTS

Automated and clinical BI-RADS density assessed on digital mammography at 2 time points from September 2006 to October 2014, interval and screen-detected breast cancer risk, and mammography sensitivity.

RESULTS

Of women whose breast density was categorized by automated BI-RADS more than 6 months to 5 years before diagnosis, those with extremely dense breasts had a 5.65-fold higher interval cancer risk (95% CI, 3.33 to 9.60) and a 1.43-fold higher screen-detected risk (CI, 1.14 to 1.79) than those with scattered fibroglandular densities. Associations of interval and screen-detected cancer with clinical BI-RADS density were similar to those with automated BI-RADS density, regardless of whether density was measured more than 6 months to less than 2 years or 2 to 5 years before diagnosis. Automated and clinical BI-RADS density measures had similar discriminatory accuracy, which was higher for interval than screen-detected cancer (c-statistics: 0.70 vs. 0.62 [P < 0.001] and 0.72 vs. 0.62 [P < 0.001], respectively). Mammography sensitivity was similar for automated and clinical BI-RADS categories: fatty, 93% versus 92%; scattered fibroglandular densities, 90% versus 90%; heterogeneously dense, 82% versus 78%; and extremely dense, 63% versus 64%, respectively.

LIMITATION

Neither automated nor clinical BI-RADS density was assessed on tomosynthesis, an emerging breast screening method.

CONCLUSION

Automated and clinical BI-RADS density similarly predict interval and screen-detected cancer risk, suggesting that either measure may be used to inform women of their breast density.

PRIMARY FUNDING SOURCE

National Cancer Institute.

Combining quantitative and qualitative breast density measures to assess breast cancer risk

BACKGROUND

Accurately identifying women with dense breasts (Breast Imaging Reporting and Data System [BI-RADS] heterogeneously or extremely dense) who are at high breast cancer risk will facilitate discussions of supplemental imaging and primary prevention. We examined the independent contribution of dense breast volume and BI-RADS breast density to predict invasive breast cancer and whether dense breast volume combined with Breast Cancer Surveillance Consortium (BCSC) risk model factors (age, race/ethnicity, family history of breast cancer, history of breast biopsy, and BI-RADS breast density) improves identifying women with dense breasts at high breast cancer risk.

METHODS

We conducted a case-control study of 1720 women with invasive cancer and 3686 control subjects. We calculated ORs and 95% CIs for the effect of BI-RADS breast density and Volpara™ automated dense breast volume on invasive cancer risk, adjusting for other BCSC risk model factors plus body mass index (BMI), and we compared C-statistics between models. We calculated BCSC 5-year breast cancer risk, incorporating the adjusted ORs associated with dense breast volume.

RESULTS

Compared with women with BI-RADS scattered fibroglandular densities and second-quartile dense breast volume, women with BI-RADS extremely dense breasts and third- or fourth-quartile dense breast volume (75% of women with extremely dense breasts) had high breast cancer risk (OR 2.87, 95% CI 1.84-4.47, and OR 2.56, 95% CI 1.87-3.52, respectively), whereas women with extremely dense breasts and first- or second-quartile dense breast volume were not at significantly increased breast cancer risk (OR 1.53, 95% CI 0.75-3.09, and OR 1.50, 95% CI 0.82-2.73, respectively). Adding continuous dense breast volume to a model with BCSC risk model factors and BMI increased discriminatory accuracy compared with a model with only BCSC risk model factors (C-statistic 0.639, 95% CI 0.623-0.654, vs. C-statistic 0.614, 95% CI 0.598-0.630, respectively; P < 0.001). Women with dense breasts and fourth-quartile dense breast volume had a BCSC 5-year risk of 2.5%, whereas women with dense breasts and first-quartile dense breast volume had a 5-year risk ≤ 1.8%.

CONCLUSIONS

Risk models with automated dense breast volume combined with BI-RADS breast density may better identify women with dense breasts at high breast cancer risk than risk models with either measure alone.

Mammographic texture and risk of breast cancer by tumor type and estrogen receptor status

Background

Several studies have shown that mammographic texture features are associated with breast cancer risk independent of the contribution of breast density. Thus, texture features may provide novel information for risk stratification. We examined the association of a set of established texture features with breast cancer risk by tumor type and estrogen receptor (ER) status, accounting for breast density.

Methods

This study combines five case–control studies including 1171 breast cancer cases and 1659 controls matched for age, date of mammogram, and study. Mammographic breast density and 46 breast texture features, including first- and second-order features, Fourier transform, and fractal dimension analysis, were evaluated from digitized film-screen mammograms. Logistic regression models evaluated each normalized feature with breast cancer after adjustment for age, body mass index, first-degree family history, percent density, and study.

Results

Of the mammographic features analyzed, fractal dimension and second-order statistics features were significantly associated (p < 0.05) with breast cancer. Fractal dimensions for the thresholds equal to 10% and 15% (FD_TH10 and FD_TH15) were associated with an increased risk of breast cancer while thresholds from 60% to 85% (FD_TH60 to FD_TH85) were associated with a decreased risk. Increasing the FD_TH75 and Energy feature values were associated with a decreased risk of breast cancer while increasing Entropy was associated with a decreased risk of breast cancer. For example, 1 standard deviation increase of FD_TH75 was associated with a 13% reduced risk of breast cancer (odds ratio = 0.87, 95% confidence interval 0.79–0.95). Overall, the direction of associations between features and ductal carcinoma in situ (DCIS) and invasive cancer, and estrogen receptor positive and negative cancer were similar.

Conclusion

Mammographic features derived from film-screen mammograms are associated with breast cancer risk independent of percent mammographic density. Some texture features also demonstrated associations for specific tumor types. For future work, we plan to assess risk prediction combining mammographic density and features assessed on digital images.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0778-1) contains supplementary material, which is available to authorized users.

Abstract 3424: Longitudinal changes in volumetric breast density with adjuvant endocrine therapy among women with breast cancer

Breast density represents the stromal and epithelial tissue in the breast and is a strong risk factor for breast cancer. Reductions in breast density attributable to tamoxifen (TAM) and aromatase inhibitors (AI) may be associated with reduced risk of first primary and subsequent breast cancer. Studies assessing breast density changes have principally used two-dimensional measures. We assess changes in breast density following initiation of TAM and AI using two automated volumetric density measures that have shown strong associations to breast cancer risk.

Breast cancer cases with a full field digital mammogram (FFDM) prior to diagnosis (index mammogram) and after initiation of TAM (n = 379) or AI (n = 425) were identified from the Mayo Clinic Mammography Practice and the San Francisco Mammography Registry. Volumetric percent density (VPD) and dense volume (DV) were measured on 4-view FFDM using VolparaTM (Matakina Technology) and QuantraTM (Hologic) automated software. We used linear regression to assess the effect of treatment on mean annualized change in VPD and DV (change from index to last mammogram divided by time interval) for each software type, adjusting for age, body mass index (BMI) and density at index mammogram.

The median time between index mammogram and cancer diagnosis was 0.6 months (IQR: 0.2, 2.2) and median time between index and last mammogram was 3 years (IQR: 2.0, 3.9). Women on TAM were younger, had lower BMI and higher baseline VPD and DV relative to women on AI (Table). An annual decrease in VPD and DV was observed with both TAM and AI. Both Volpara and Quantra estimated a similar magnitude of change in VPD in women on TAM and AI, and a greater change in DV with TAM.

Our findings suggest that both Volpara and Quantra can assess volumetric changes in breast density among women on hormone therapy. If declines in volumetric density correlate with a reduction in breast cancer risk, these automated measures could be used in clinical practice to assess response to therapy. Annualized changes in volumetric breast density estimated by linear regression.Tamoxifen (n = 379)Aromatase Inhibitors (n = 425)Baseline Median (IQR)Annualized Change (95% CI)*Baseline Median (IQR)Annualized Change (95% CI)*Age at Diagnosis50.0 (45.0, 60.0)–63.0 (58.0, 71.0)–Body Mass Index (BMI)23.6 (21.5, 26.8)–25.7 (22.7, 29.9)–Time Interval¥3.0 (2.1, 3.9)–3.0 (2.1, 3.9)–VolparaPercent Density (VPD,%)11.6 (6.8, 18.8)-0.17 (-0.27, -0.10)7.2 (5.0, 11.0)-0.19 (-0.29, -0.12)Dense Volume (DV, cm3)64.7 (45.4, 90.9)-0.90 (-1.45, -0.48)51.9 (38.9, 69.9)-0.52 (-0.93, -0.23)QuantraPercent Density (VPD,%)14.5 (9.2, 20.2)-0.42 (-0.59, -0.28)9.9 (7.1, 14.5)-0.38 (-0.54, -0.25)Dense Volume (DV, cm3)94.0 (58.0, 144.0)-2.20 (-3.52, -1.19)80.0 (49.0, 128.0)-0.95 (-1.85, -0.35)IQR = Interquartile range ¥ Median number of years between index mammogram and last mammogram post-initiation of therapy. *Annualized change estimated as change from index to last mammogram divided by time interval and adjusted for study site, age at diagnosis, BMI and density at index mammogram.

Citation Format: Natalie J. Engmann, Celine M. Vachon, Christopher G. Scott, Matthew R. Jensen, Lin Ma, Kathleen R. Brandt, Amir P. Mahmoudzadeh, Serghei Malkov, Dana H. Whaley, Carrie B. Hruska, Fang F. Wu, Stacey J. Winham, Diana L. Miglioretti, Aaron D. Norman, John J. Heine, John Shepherd, V Shane Pankratz, Karla Kerlikowske. Longitudinal changes in volumetric breast density with adjuvant endocrine therapy among women with breast cancer. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr 3424.

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

Purpose To compare the classification of breast density with two automated methods, Volpara (version 1.5.0; Matakina Technology, Wellington, New Zealand) and Quantra (version 2.0; Hologic, Bedford, Mass), with clinical Breast Imaging Reporting and Data System (BI-RADS) density classifications and to examine associations of these measures with breast cancer risk. Materials and Methods In this study, 1911 patients with breast cancer and 4170 control subjects matched for age, race, examination date, and mammography machine were evaluated. Participants underwent mammography at Mayo Clinic or one of four sites within the San Francisco Mammography Registry between 2006 and 2012 and provided informed consent or a waiver for research, in compliance with HIPAA regulations and institutional review board approval. Digital mammograms were retrieved a mean of 2.1 years (range, 6 months to 6 years) before cancer diagnosis, with the corresponding clinical BI-RADS density classifications, and Volpara and Quantra density estimates were generated. Agreement was assessed with weighted κ statistics among control subjects. Breast cancer associations were evaluated with conditional logistic regression, adjusted for age and body mass index. Odds ratios, C statistics, and 95% confidence intervals (CIs) were estimated. Results Agreement between clinical BI-RADS density classifications and Volpara and Quantra BI-RADS estimates was moderate, with κ values of 0.57 (95% CI: 0.55, 0.59) and 0.46 (95% CI: 0.44, 0.47), respectively. Differences of up to 14% in dense tissue classification were found, with Volpara classifying 51% of women as having dense breasts, Quantra classifying 37%, and clinical BI-RADS assessment used to classify 43%. Clinical and automated measures showed similar breast cancer associations; odds ratios for extremely dense breasts versus scattered fibroglandular densities were 1.8 (95% CI: 1.5, 2.2), 1.9 (95% CI: 1.5, 2.5), and 2.3 (95% CI: 1.9, 2.8) for Volpara, Quantra, and BI-RADS classifications, respectively. Clinical BI-RADS assessment showed better discrimination of case status (C = 0.60; 95% CI: 0.58, 0.61) than did Volpara (C = 0.58; 95% CI: 0.56, 0.59) and Quantra (C = 0.56; 95% CI: 0.54, 0.58) BI-RADS classifications. Conclusion Automated and clinical assessments of breast density are similarly associated with breast cancer risk but differ up to 14% in the classification of women with dense breasts. This could have substantial effects on clinical practice patterns. (©) RSNA, 2015 Online supplemental material is available for this article.

Abstract 2768: Relationships between mammographic density, microvessel density, and breast biopsy diagnosis

Introduction: Mammographic density (MD) is a strong breast cancer risk factor; however, the majority of women with high MD have neither a prevalent tumor nor will they develop one in immediate follow-up. Magnetic resonance imaging (MRI) studies suggest that background parenchymal enhancement, an indicator of vascularity, is another strong breast cancer risk predictor. However, it is uncertain how correlated microvessel density (MVD), a histological marker of vascularity, is with MD and if it adds information for disease detection. We therefore investigated relationships between MVD, area and volume measures of MD, and biopsy diagnosis among 218 women referred for image-guided vacuum-assisted breast biopsies.

Methods: MVD was determined by counting CD31 (endothelial marker) positive vessels in whole sections of breast biopsies in three areas containing five 40X high power fields. Average MVD per area was calculated and then transformed based on a Box-Cox analysis to approximate a normal distribution. MD volume was quantified using single X-ray absorptiometry (SXA) in digital mammograms and MD area was quantified on the same image using thresholding methods. We used linear regression to evaluate associations between MVD (as the outcome) and MD measures (area and volume) adjusted for age and body mass index (BMI) in the overall population and stratified by biopsy diagnosis: cases (in situ or invasive carcinoma, n = 44) vs. non-cases (non-proliferative or proliferative benign breast disease, n = 174). Logistic regression was used to calculate odds ratios (ORs) and 95% confidence intervals (CIs) for the association between MVD and biopsy diagnosis (cases vs. non-cases) in models adjusted for age, BMI, and MD measures.

Results: MVD was inversely associated with absolute dense area and absolute dense volume in the overall sample (area p = 0.01, volume p = 0.11) and among non-cases (area p = 0.009, volume p = 0.007). In age-, BMI-, and dense area- or dense volume- adjusted logistic regression models, MVD was significantly associated with risk of in situ/invasive disease independent of absolute dense area (OR = 1.16, 95% CI = 1.04, 1.28) and independent of absolute dense volume (OR = 1.16, 95% CI = 1.05-1.29).

Conclusion: Our histopathologic analysis suggests that tissue vascularity, as reflected by MVD, may predict breast cancer risk independently of MD, thus providing theoretical support for the potential utility in breast cancer detection of imaging methods that reflect vascularity, such as contrast-enhanced MRI.

Citation Format: Ashley S. Felix, Petra Lenz, Ruth M. Pfeiffer, Stephen M. Hewitt, Jennifer Morris, Deesha Patel, Berta Geller, Pamela M. Vacek, Donald L. Weaver, Rachael E. Chicoine, John Shepherd, Amir P. Mahmoudzadeh, Jeff Wang, Bo Fan, Sally Herschorn, Jason Johnson, Louise A. Brinton, Mark E. Sherman, Gretchen L. Gierach. Relationships between mammographic density, microvessel density, and breast biopsy diagnosis. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 2768. doi:10.1158/1538-7445.AM2015-2768