Clinical Imaging 39 (2015) 582–586
Contents lists available at ScienceDirect
Clinical Imaging journal homepage: http://www.clinicalimaging.org
Reproducibility of automated volumetric breast density assessment in short-term digital mammography reimaging Eun Sook Ko a,⁎, Rock Bum Kim b, Boo-Kyung Han a a b
Department of Radiology, Samsung Medical Center Sungkyunkwan University School of Medicine, 50 Irwondong, Gangnam-gu, Seoul 135-710, Korea Department of Preventive Medicine, Dong-A University School of Medicine, Busan, Korea
a r t i c l e
i n f o
Article history: Received 27 November 2014 Received in revised form 21 January 2015 Accepted 16 February 2015 Keywords: Breast Mammography Mammographic density Reproducibility of results Technology assessment
a b s t r a c t Two automated volumetric breast density analyses of 44 patients who underwent image-guided needle localization in one breast were compared to calculate the agreement of assessment parameters in short-term digital mammography reimaging. The outputs of the automated volumetric breast density method included four parameters [fibroglandular tissue volume (Vfg), total breast volume (Vb), volumetric breast density (Vbd), and area breast density (Abd)]. The variability and agreement of each parameter were calculated in serial mammograms. There was no significant difference in mean Vfg, Vb, Vbd, or Abd between two mammograms (P = .249, .053, .727, and .603, respectively). © 2015 Elsevier Inc. All rights reserved.
1. Introduction Mammographic breast density is defined as the relative proportion of radiopaque areas (fibroglandular tissue) within the entire breast. Many studies reported that a higher mammographic density increases the risk of breast cancer in both Western and Asian women [1–5]. Therefore, mammographic density has been proposed as a variable for individual risk assessment [6]. Several studies have shown evidence that therapies influencing hormone levels such as tamoxifen can change both mammographic density and the risk of breast cancer [7–9]; furthermore, changes in breast density on mammography have been increasingly used to monitor the effects of treatment [7–10]. However, to promote the widespread use of mammographic density change as a surrogate biomarker of treatment effect, it is essential to establish the validity of the density assessment. Traditionally, the Breast Imaging Reporting and Data System (BI-RADS) density categories or visual and computer-aided estimations of percentage density are commonly used to assess mammographic density [11–14]. However, the assessment of breast density by humans is limited by low-to-intermediate reproducibility [15–17]. The reproducibility of breast density estimates could be improved by using automated or semiautomated techniques. Recently, several methods for fully automated volumetric estimations of breast density have been used [18–21]. These models showed variable results in predicting the risk of breast cancer compared to area-based measurements of breast density [19,20]. Although only limited studies have ⁎ Corresponding author. Tel.: +82 2 3410 0877; fax: +82 2 3410 0049. E-mail address: [email protected] (E.S. Ko). http://dx.doi.org/10.1016/j.clinimag.2015.02.011 0899-7071/© 2015 Elsevier Inc. All rights reserved.
focused on the reproducibility of this technique, those studies were performed with a relatively longer time interval or various combinations of machines which limited value of their results [22,23]. Therefore, the aim of this study was to compare the reproducibility of volumetric breast density assessment parameters in short-term reimaging performed on the same digital mammography equipment.
2. Materials and methods 2.1. Subjects and mammograms This retrospective study was approved by the Institutional Review Board of our institution, and the requirement for informed consent was waived. We searched our records from January 2014 to February 2014 for patients who underwent mammography- or sonographyguided needle localization in one breast. To be included in this study, patients had to have undergone image-guided needle localization after routine mammography that was performed on the same mammography unit no more than 2 months apart with availability of two sets of automated volumetric density analyses of the affected breast. Between the two mammography examinations, patients were not treated with any systemic therapy or breast surgery. We excluded patients who had a history of previous surgery on the eligible breast or the use of hormone replacement therapy. We also excluded patients who underwent image-guided needle localization of more than two sites to avoid the effects of hemorrhage on quantitative density measurements and effects related to technical deficits of the mammogram, such as inadequate positioning. Finally, a total of 44 breasts in 44 patients (mean age, 50.0
E.S. Ko et al. / Clinical Imaging 39 (2015) 582–586
years; range, 31–79 years) were identified. Only one breast per patient was chosen for analysis. All mammograms in this study were performed using the same fullfield digital mammography system (Selenia Dimensions, Hologic Inc., Bedford, MA, USA). All mammograms were acquired in standard craniocaudal (CC) and mediolateral oblique (MLO) projections using automatic optimization of acquisition parameters. Three (6.8%) of 44 image-guided needle localizations were performed under mammographic guidance, while the others were performed under sonographic guidance. All patients underwent image-guided needle localization for nonpalpable breast lesions, and 9 (20.5%) of 44 lesions proved to be benign. The mean interval between the first and second mammograms was 13.2 days (range, 3–56 days). Since positioning differences could be a major factor affecting discrepant results in density assessments on serial mammograms [24], three technicians performed each mammogram included in this study while referencing previous mammograms to avoid dissimilar positioning. 2.2. Automated volumetric breast density assessments For volumetric breast density analyses, raw image data were sent to a dedicated server running volumetric breast density analysis software (Quantra Version 2.0; Hologic Inc., Bedford, MA, USA). Briefly, this software provides fibroglandular tissue volume and total breast volume from a two-view mammogram and calculates volumetric breast density as the ratio of these parameters. The algorithm uses acquisition parameters such as tube voltage, tube current, compression thickness, and attenuation coefficients of different breast tissues to estimate the thickness of parenchymal and/or adipose tissue that the X-ray beam penetrated to deposit a given amount of energy on the detector. The Quantra software output includes fibroglandular tissue volume (Vfg), total breast volume (Vb), volumetric breast density (Vbd), and area breast density (Abd) (Fig. 1). Using volumetric measurements, Quantra also provides BI-RADS-like scores, referred to as quantized density (Qˍabd) (Fig. 1). A dedicated breast radiologist with 9 years of experience (K.E.S) reviewed and recorded those quantitative data displayed on picture archiving and communication system. 2.3. Statistical analysis We calculated the absolute and relative differences for each of four volumetric breast density parameters (Vfg, Vb, Vbd, and Abd) between two consecutive examinations. The absolute difference was the absolute value of the relative difference between serial mammograms. The relative difference was defined as the difference in percent density obtained by subtracting the percent density assessed at the second mammogram from the percent density at the first mammogram. For example, if the percent densities assessed at the first mammogram and second mammogram were 10% and 20%, the relative difference would be − 10%, and the absolute difference would be 10%. A Wilcoxon signed rank test was used to evaluate significant differences between the mean
Fig. 1. The output of automated volumetric breast density assessment program.
583
volumetric breast density parameters from the first and second examinations. Kappa statistics were used to test for agreement between the first and second Qˍabd. The intraclass correlation coefficient (ICC) was measured to test the agreement between the four volumetric breast density parameters on the two serial mammograms. Spearman’s correlation coefficients were determined for the consecutive measurements. Differences in the Spearman’s correlation coefficients of Vbd and Abd were tested for statistical significance using Fisher’s z transformation. SPSS version 19 (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses. P b .05 was considered statistically significant. 3. Results Volumetric breast density parameters calculated from the first and second mammograms are shown in Table 1. There was no significant difference in mean Vfg, Vb, Vbd, or Abd between the first and second mammograms (P = .249, .053, .727, and .603, respectively). The mean absolute differences of serial Abd values were higher than those of Vbd values (3.43 vs. 1.91). The median absolute differences of serial Abd values were also higher than those of Vbd values (3.00 vs. 1.00). The mean absolute differences of serial Vfg and Vb values were 14.91 and 47.00, respectively. The kappa value for the two BI-RADS-like scores (Qˍabd) was 0.744, showing substantial agreement (Table 2). The mean ICC value was 0.974 for Vfg [95% confidence interval (CI), 0.953–0.986; P b .001], 0.990 for Vb (95% CI, 0.981–0.994; P b .001), 0.982 for Vbd (95% CI, 0.967–0.990; P b .001), and 0.985 for Abd (95% CI, 0.972–0.992; P b .001). All parameters showed excellent agreement and were statistically significant (Fig. 2). Spearman’s correlations between the two examinations for each parameter are shown in Fig. 3. All parameters demonstrated high Spearman’s correlation coefficients greater than 0.9, which were statistically significant and showed strong correlations. However, the difference in Spearman’s correlation coefficients between Vbd and Abd was not statistically significant (P = .507). 4. Discussion Mammographic density is well known as a general marker of breast cancer risk [3,24]. Cuzick et al. showed in their nested case–control study that a change in mammographic density over 12–18 months is an excellent predictor of response to tamoxifen in a preventive setting [7]. Furthermore, a recent retrospective study using quantitative imaging analysis software to assess mammographic density showed that
Table 1 Variability of automated volumetric breast density assessment parameters from serial mammograms Mean Vfg (cm3) 1st examination 2nd examination Relative difference Absolute difference Vb (cm3) 1st examination 2nd examination Relative difference Absolute difference Vbd (%) 1st examination 2nd examination Relative difference Absolute difference Abd (%) 1st examination 2nd examination Relative difference Absolute difference
S.D.
Median
Min
Max
P value
102.25 99.89 2.36 14.91
69.90 73.40 22.66 17.08
79.00 76.50 2.50 8.00
11.00 18.00 −88.00 0.00
318.00 377.00 58.00 88.00
.249
576.02 561.55 13.73 47.00
314.50 299.97 61.18 40.93
558.50 518.50 12.50 39.50
106.00 138.00 −185.00 1.00
1837.00 1741.00 125.00 185.00
.053
18.84 18.89 −0.05 1.91
10.39 10.29 2.74 1.94
17.50 16.00 0.00 1.00
5.00 4.00 −6.00 0.00
42.00 41.00 8.00 8.00
.727
28.45 28.20 0.11 3.43
16.94 17.20 4.19 2.36
24.00 25.00 0.00 3.00
1.00 1.00 −9.00 0.00
59.00 62.00 9.00 9.00
.603
584
E.S. Ko et al. / Clinical Imaging 39 (2015) 582–586
Table 2 Agreement of the two BI-RADS-like scores (Qˍabd) between the two serial examinations Qˍabd (2nd)
Qˍabd (1st)
1 2 3 4
1
2
3
4
2 1 0 0
1 11 0 0
0 4 10 1
0 0 1 13
mammographic density changes during the short-term use of adjuvant endocrine therapy could be used as a significant predictor of long-term recurrence in women with estrogen-receptor-positive breast cancer. According to their results, the hazard ratios for recurrence in patients who experienced ˂5% absolute mammographic density reduction and those in patients with increased mammographic density after approximately 1 year of endocrine therapy were 1.92 and 2.26, respectively (P = .048 and .027, respectively), compared to patients with a mammographic density reduction of ≥ 10% [25]. Many other previous studies have demonstrated changes in mammographic density associated with hormonal therapy in a wide range of absolute differences from 4% to 13% [7–10]. However, to be used as a treatment monitoring modality, mammographic density should have good reproducibility. To investigate the reproducibility, Kim et al. [22] retrospectively evaluated the variability of current breast density assessment methods in short-term reimaging of the same breast using digital mammography. According to their study, for BI-RADS assessment, 29% were assessed differently by one category after short-term reimaging. The mean absolute differences in percentage density in short-term serial CC and MLO view mammograms were 7.4% and 6.4%, respectively, in computer-aided assessment using Cumulus software. Furthermore, 29% (CC view) and 22% (MLO view) of computer-aided assessments had discrepancies of greater than 10% density, which were often used as criteria for significant changes after hormonal therapy [7,25]. They concluded that considerable variability in breast density assessments occurred in short-term reimaging with digital mammography, particularly in women of younger age, with greater breast density, and when examined using different types of mammography equipment. Different types of mammography systems had more frequently discrepant assessments than equivalent mammography systems (30% vs. 7% in computer-aided assessments). We questioned the wide variability in their study caused by the inclusion of various types of mammography machines and not by the limitations of the quantitative density measurement software. Additionally, we were concerned about the reproducibility of the Cumulus software used in their study. Studies using Cumulus software, which has been the most widely used package for mammographic density measurements in previous studies, indicate good intraobserver reproducibility with correlation coefficients of 0.92–0.96 [25,26]. Despite the good reproducibility reported, because Cumulus software requires the placement of a dichotomous threshold between dense and nondense tissue, the measurements could still be subjective according to the operator [27]. We presumed that automated volumetric density measurements could solve these issues. Therefore, we designed this study to evaluate the reproducibility of serial imaging in a short-term interval using the same digital mammography equipment and automated volumetric density analyses. Compared to Kim’s results regarding digital radiography with direct conversion type (direct DR)/direct DR or direct radiography with indirect conversion type (indirect DR)/indirect DR, the absolute differences between the two serial examinations were much smaller in our study. However, the question remains whether dense breast volume or dense breast area is more appropriate as a treatment monitoring parameter in breast cancer. A study by Boyd et al. comparing the predictive value of dense volume and dense area on mammograms found that measurements of the volume of dense tissue did not improve breast
Fig. 2. A 46-year-old woman who underwent sonography-guided needle localization. (a) First mediolateral oblique mammogram shows small mass in her upper breast. (b) First output of automated volumetric breast density. (c) Second mediolateral oblique mammogram obtained 15 days after first mammogram shows a needle within previous mass. (d) Second output of automated volumetric breast density.
cancer risk prediction, contrary to expectations [28]. Engelken et al. [23] compared the reproducibility of two volumetric parameters, fibroglandular tissue volume and percent density, using the same automated volumetric mammographic density analysis software as in our study. According to their study, the volumetric measurement of fibroglandular tissue volume is a very highly reproducible parameter in serial mammograms and shows better reproducibility than percent
E.S. Ko et al. / Clinical Imaging 39 (2015) 582–586
585
Fig. 3. Spearman’s correlation coefficients of four parameters from serial mammograms obtained on the same mammography unit. (a) Vfg, (b) Vb, (c) Vbd, (d) Abd.
density in consecutive mammograms of normal women, suggesting that it may be a better parameter for evaluating breast composition in longitudinal studies. However, their study used a long duration between consecutive mammograms, with a median interval of 14.9 months. This limited the conclusions that can be drawn from their study. Our study showed that automated volumetric breast density measurements have very high reproducibility when the same mammography machines were used in a short interval. In our study, the reproducibility of Vb and Vfg expressed as cm 3 was also excellent. However, because quite bigger number changes than Vbd and Abd expressed as %, they would be hard to use Vb and Vfg for monitoring treatment efficacy or as a risk assessment parameter. Abd showed better reproducibility than Vbd (Spearman’s rho = 0.967 and 0.956, respectively) but was not statistically significant. In addition, the absolute difference of Vbd was smaller than that of Abd, which suggests that Vbd might have been more appropriate as a surrogate marker in interventional studies. Our study has several limitations. First, our study included only 44 patients. Because of the sample size, we could not perform a more comprehensive analysis. Second, because this was a retrospective study, we could not consider the menstrual cycles of the premenopausal women. Breast tissue is known to be less radiopaque in the follicular phase than in the luteal phase [29,30]. When considering this finding, studies examining breast density as a surrogate biomarker in premenopausal women should be optimized to minimize the influence of menstrual cycle by controlling for the phase of the menstrual cycle in which the
mammogram is obtained. Third, we did not analyze various technical factors such as compression force, mAs, kVp, or breast thickness, which could affect the mammographic density assessment. Fourth, we did not investigate other host factors that can affect breast density such as body mass index, age at menarche, and parity. Fifth, our technicians performed mammography using previous mammograms as reference images. This meant that our study could accurately evaluate reproducibility of volumetric density assessment. However, this study design might be different from actual practice. Sixth, all second examinations were obtained after image-guided needle localization. It is possible that hemorrhage could have affected mammographic density measurements by increasing density. However, this study design was inevitable when considering the need to obtain two sets of mammography examinations within a very short interval. In a similar study by Engelken et al. [23], the mean interval between the two examinations was 14.9 months, while ours was 13.2 days. This is a strength of our study. In conclusion, automated volumetric breast density assessment from digital mammography shows extremely high reproducibility in consecutive examinations. Although Abd showed greater agreement than Vbd, it failed to reach statistical significance. The mean absolute difference was smaller for Vbd than Abd. Therefore, in assessments of treatment efficacy or risk prediction, we recommend that the same mammography machine be used. Furthermore, Vbd could be more feasibly used as a monitoring parameter.
586
E.S. Ko et al. / Clinical Imaging 39 (2015) 582–586
References [1] Mousa NA, Crystal P, Wolfman WL, Bedaiwy MA, Casper RF. Aromatase inhibitors and mammographic breast density in postmenopausal women receiving hormone therapy. Menopause 2008;15:875–84. [2] Jeon JH, Kang JH, Kim Y, Lee HY, Choi KS, Jun JK, et al. Reproductive and hormonal factors associated with fatty or dense breast patterns among Korean Women. Cancer Res Treat 2011;43:42–8. [3] 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. [4] Ursin G, Ma H, Wu AH, Bernstein L, Salane M, Parisky YR, et al. Mammographic density and breast cancer in three ethnic groups. Cancer Epidemiol Biomarkers Prev 2003;12:332–8. [5] Nagata C, Matsubara T, Fujita H, Nagao Y, Shibuya C, Kashiki Y, et al. Mammographic density and the risk of breast cancer in Japanese women. Br J Cancer 2005;92:2102–6. [6] 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. [7] Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, et al. Tamoxifeninduced reduction in mammographic density and breast cancer risk reduction: a nested case–control study. J Natl Cancer Inst 2011;103:744–52. [8] Weitzel JN, Buys SS, Sherman WH, Daniels A, Ursin G, MacDonald DJ, et al. Reduced mammographic density with use of a gonadotropin-releasing hormone agonist-based chemoprevention regimen in BRCA1 carriers. Clin Cancer Res 2007;13(2 Pt 1):654–8. [9] Ursin G, Astrahan MA, Salane M, Parisky YR, Pearce JG, Daniels JR, et al. The detection of changes in mammographic densities. Cancer Epidemiol Biomarkers Prev 1998;7:43–7. [10] Smith J, Dilawari A, Ursin G, Andreopoulou E, Checka C, Axelrod D, et al. A pilot study of letrozole for one year in women at enhanced risk of developing breast cancer: effects on mammographic density. Anticancer Res 2012;32:1327–31. [11] Harvey JA, Bovbjerg VE, Smolkin ME, Williams MB, Petroni GR. Evaluating hormone therapy-associated increases in breast density comparison between reported and simultaneous assignment of BI-RADS categories, visual assessment, and quantitative analysis. Acad Radiol 2005;12:853–62. [12] Kerlikowske K, Ichikawa L, Miglioretti DL, Buist DS, Vacek PM, Smith-Bindman R, et al. Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk. J Natl Cancer Inst 2007;99:386–95. [13] Lee-Han H, Cooke G, Boyd NF. Quantitative evaluation of mammographic densities: a comparison of methods of assessment. Eur J Cancer Prev 1995;4:285–92. [14] Byng JW, Yaffe MJ, Jong RA, Shumak RS, Lockwood GA, Tritchler DL, et al. Analysis of mammographic density and breast cancer risk from digitized mammograms. Radiographics 1998;18:1587–98. [15] Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B, Carozzi F, et al. Categorizing breast mammographic density: intra- and interobserver reproducibility of BIRADS density categories. Breast 2005;14:269–75.
[16] 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. [17] Pérez-Gómez B, Ruiz F, Martínez I, Casals M, Miranda J, Sánchez-Contador C, et al. Women’s features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN). Breast Cancer Res Treat 2012;132:287–95. [18] Jeffreys M, Warren R, Highnam R, Smith GD. Initial experiences of using an automated volumetric measure of breast density: the standard mammogram form. Br J Radiol 2006;79:378–82. [19] 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. [20] Lokate M, Kallenberg MG, Karssemeijer N, Van den Bosch MA, Peeters PH, Van Gils CH. Volumetric breast density from full-field digital mammograms and its association with breast cancer risk factors: a comparison with a threshold method. Cancer Epidemiol Biomarkers Prev 2010;19:3096–105. [21] 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. [22] Kim WH, Moon WK, Kim SM, Yi A, Chang JM, Koo HR, et al. Variability of breast density assessment in short-term reimaging with digital mammography. Eur J Radiol 2013;82:1724–30. [23] Engelken F, Singh JM, Fallenberg EM, Bick U, Böttcher J, Renz DM. Volumetric breast composition analysis: reproducibility of breast percent density and fibroglandular tissue volume measurements in serial mammograms. Acta Radiol 2014;55:32–8. [24] 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. [25] Kim J, Han W, Moon HG, Ahn SK, Shin HC, You JM, et al. Breast density change as a predictive surrogate for response to adjuvant endocrine therapy in hormone receptor positive breast cancer. Breast Cancer Res 2012;14:R102. http://dx.doi.org/10.1186/bcr3221. [26] Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities. Phys Med Biol 1994;39:1629–38. [27] Boyd NF, Martin LJ, Bronskill M, Yaffe MJ, Duric N, Minkin S. Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst 2010;102:1224–37. [28] 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. [29] Morrow M, Chatterton RT, Rademaker AW, Hou N, Jordan VC, Hendrick RE, et al. A prospective study of variability in mammographic density during the menstrual cycle. Breast Cancer Res Treat 2010;121:565–74. [30] White E, Velentgas P, Mandelson MT, Lehman CD, Elmore JG, Porter P, et al. Variation in mammographic breast density by time in menstrual cycle among women aged 40–49 years. J Natl Cancer Inst 1998;90:906–10.
Contents lists available at ScienceDirect
Clinical Imaging journal homepage: http://www.clinicalimaging.org
Reproducibility of automated volumetric breast density assessment in short-term digital mammography reimaging Eun Sook Ko a,⁎, Rock Bum Kim b, Boo-Kyung Han a a b
Department of Radiology, Samsung Medical Center Sungkyunkwan University School of Medicine, 50 Irwondong, Gangnam-gu, Seoul 135-710, Korea Department of Preventive Medicine, Dong-A University School of Medicine, Busan, Korea
a r t i c l e
i n f o
Article history: Received 27 November 2014 Received in revised form 21 January 2015 Accepted 16 February 2015 Keywords: Breast Mammography Mammographic density Reproducibility of results Technology assessment
a b s t r a c t Two automated volumetric breast density analyses of 44 patients who underwent image-guided needle localization in one breast were compared to calculate the agreement of assessment parameters in short-term digital mammography reimaging. The outputs of the automated volumetric breast density method included four parameters [fibroglandular tissue volume (Vfg), total breast volume (Vb), volumetric breast density (Vbd), and area breast density (Abd)]. The variability and agreement of each parameter were calculated in serial mammograms. There was no significant difference in mean Vfg, Vb, Vbd, or Abd between two mammograms (P = .249, .053, .727, and .603, respectively). © 2015 Elsevier Inc. All rights reserved.
1. Introduction Mammographic breast density is defined as the relative proportion of radiopaque areas (fibroglandular tissue) within the entire breast. Many studies reported that a higher mammographic density increases the risk of breast cancer in both Western and Asian women [1–5]. Therefore, mammographic density has been proposed as a variable for individual risk assessment [6]. Several studies have shown evidence that therapies influencing hormone levels such as tamoxifen can change both mammographic density and the risk of breast cancer [7–9]; furthermore, changes in breast density on mammography have been increasingly used to monitor the effects of treatment [7–10]. However, to promote the widespread use of mammographic density change as a surrogate biomarker of treatment effect, it is essential to establish the validity of the density assessment. Traditionally, the Breast Imaging Reporting and Data System (BI-RADS) density categories or visual and computer-aided estimations of percentage density are commonly used to assess mammographic density [11–14]. However, the assessment of breast density by humans is limited by low-to-intermediate reproducibility [15–17]. The reproducibility of breast density estimates could be improved by using automated or semiautomated techniques. Recently, several methods for fully automated volumetric estimations of breast density have been used [18–21]. These models showed variable results in predicting the risk of breast cancer compared to area-based measurements of breast density [19,20]. Although only limited studies have ⁎ Corresponding author. Tel.: +82 2 3410 0877; fax: +82 2 3410 0049. E-mail address: [email protected] (E.S. Ko). http://dx.doi.org/10.1016/j.clinimag.2015.02.011 0899-7071/© 2015 Elsevier Inc. All rights reserved.
focused on the reproducibility of this technique, those studies were performed with a relatively longer time interval or various combinations of machines which limited value of their results [22,23]. Therefore, the aim of this study was to compare the reproducibility of volumetric breast density assessment parameters in short-term reimaging performed on the same digital mammography equipment.
2. Materials and methods 2.1. Subjects and mammograms This retrospective study was approved by the Institutional Review Board of our institution, and the requirement for informed consent was waived. We searched our records from January 2014 to February 2014 for patients who underwent mammography- or sonographyguided needle localization in one breast. To be included in this study, patients had to have undergone image-guided needle localization after routine mammography that was performed on the same mammography unit no more than 2 months apart with availability of two sets of automated volumetric density analyses of the affected breast. Between the two mammography examinations, patients were not treated with any systemic therapy or breast surgery. We excluded patients who had a history of previous surgery on the eligible breast or the use of hormone replacement therapy. We also excluded patients who underwent image-guided needle localization of more than two sites to avoid the effects of hemorrhage on quantitative density measurements and effects related to technical deficits of the mammogram, such as inadequate positioning. Finally, a total of 44 breasts in 44 patients (mean age, 50.0
E.S. Ko et al. / Clinical Imaging 39 (2015) 582–586
years; range, 31–79 years) were identified. Only one breast per patient was chosen for analysis. All mammograms in this study were performed using the same fullfield digital mammography system (Selenia Dimensions, Hologic Inc., Bedford, MA, USA). All mammograms were acquired in standard craniocaudal (CC) and mediolateral oblique (MLO) projections using automatic optimization of acquisition parameters. Three (6.8%) of 44 image-guided needle localizations were performed under mammographic guidance, while the others were performed under sonographic guidance. All patients underwent image-guided needle localization for nonpalpable breast lesions, and 9 (20.5%) of 44 lesions proved to be benign. The mean interval between the first and second mammograms was 13.2 days (range, 3–56 days). Since positioning differences could be a major factor affecting discrepant results in density assessments on serial mammograms [24], three technicians performed each mammogram included in this study while referencing previous mammograms to avoid dissimilar positioning. 2.2. Automated volumetric breast density assessments For volumetric breast density analyses, raw image data were sent to a dedicated server running volumetric breast density analysis software (Quantra Version 2.0; Hologic Inc., Bedford, MA, USA). Briefly, this software provides fibroglandular tissue volume and total breast volume from a two-view mammogram and calculates volumetric breast density as the ratio of these parameters. The algorithm uses acquisition parameters such as tube voltage, tube current, compression thickness, and attenuation coefficients of different breast tissues to estimate the thickness of parenchymal and/or adipose tissue that the X-ray beam penetrated to deposit a given amount of energy on the detector. The Quantra software output includes fibroglandular tissue volume (Vfg), total breast volume (Vb), volumetric breast density (Vbd), and area breast density (Abd) (Fig. 1). Using volumetric measurements, Quantra also provides BI-RADS-like scores, referred to as quantized density (Qˍabd) (Fig. 1). A dedicated breast radiologist with 9 years of experience (K.E.S) reviewed and recorded those quantitative data displayed on picture archiving and communication system. 2.3. Statistical analysis We calculated the absolute and relative differences for each of four volumetric breast density parameters (Vfg, Vb, Vbd, and Abd) between two consecutive examinations. The absolute difference was the absolute value of the relative difference between serial mammograms. The relative difference was defined as the difference in percent density obtained by subtracting the percent density assessed at the second mammogram from the percent density at the first mammogram. For example, if the percent densities assessed at the first mammogram and second mammogram were 10% and 20%, the relative difference would be − 10%, and the absolute difference would be 10%. A Wilcoxon signed rank test was used to evaluate significant differences between the mean
Fig. 1. The output of automated volumetric breast density assessment program.
583
volumetric breast density parameters from the first and second examinations. Kappa statistics were used to test for agreement between the first and second Qˍabd. The intraclass correlation coefficient (ICC) was measured to test the agreement between the four volumetric breast density parameters on the two serial mammograms. Spearman’s correlation coefficients were determined for the consecutive measurements. Differences in the Spearman’s correlation coefficients of Vbd and Abd were tested for statistical significance using Fisher’s z transformation. SPSS version 19 (SPSS Inc., Chicago, IL, USA) was used for all statistical analyses. P b .05 was considered statistically significant. 3. Results Volumetric breast density parameters calculated from the first and second mammograms are shown in Table 1. There was no significant difference in mean Vfg, Vb, Vbd, or Abd between the first and second mammograms (P = .249, .053, .727, and .603, respectively). The mean absolute differences of serial Abd values were higher than those of Vbd values (3.43 vs. 1.91). The median absolute differences of serial Abd values were also higher than those of Vbd values (3.00 vs. 1.00). The mean absolute differences of serial Vfg and Vb values were 14.91 and 47.00, respectively. The kappa value for the two BI-RADS-like scores (Qˍabd) was 0.744, showing substantial agreement (Table 2). The mean ICC value was 0.974 for Vfg [95% confidence interval (CI), 0.953–0.986; P b .001], 0.990 for Vb (95% CI, 0.981–0.994; P b .001), 0.982 for Vbd (95% CI, 0.967–0.990; P b .001), and 0.985 for Abd (95% CI, 0.972–0.992; P b .001). All parameters showed excellent agreement and were statistically significant (Fig. 2). Spearman’s correlations between the two examinations for each parameter are shown in Fig. 3. All parameters demonstrated high Spearman’s correlation coefficients greater than 0.9, which were statistically significant and showed strong correlations. However, the difference in Spearman’s correlation coefficients between Vbd and Abd was not statistically significant (P = .507). 4. Discussion Mammographic density is well known as a general marker of breast cancer risk [3,24]. Cuzick et al. showed in their nested case–control study that a change in mammographic density over 12–18 months is an excellent predictor of response to tamoxifen in a preventive setting [7]. Furthermore, a recent retrospective study using quantitative imaging analysis software to assess mammographic density showed that
Table 1 Variability of automated volumetric breast density assessment parameters from serial mammograms Mean Vfg (cm3) 1st examination 2nd examination Relative difference Absolute difference Vb (cm3) 1st examination 2nd examination Relative difference Absolute difference Vbd (%) 1st examination 2nd examination Relative difference Absolute difference Abd (%) 1st examination 2nd examination Relative difference Absolute difference
S.D.
Median
Min
Max
P value
102.25 99.89 2.36 14.91
69.90 73.40 22.66 17.08
79.00 76.50 2.50 8.00
11.00 18.00 −88.00 0.00
318.00 377.00 58.00 88.00
.249
576.02 561.55 13.73 47.00
314.50 299.97 61.18 40.93
558.50 518.50 12.50 39.50
106.00 138.00 −185.00 1.00
1837.00 1741.00 125.00 185.00
.053
18.84 18.89 −0.05 1.91
10.39 10.29 2.74 1.94
17.50 16.00 0.00 1.00
5.00 4.00 −6.00 0.00
42.00 41.00 8.00 8.00
.727
28.45 28.20 0.11 3.43
16.94 17.20 4.19 2.36
24.00 25.00 0.00 3.00
1.00 1.00 −9.00 0.00
59.00 62.00 9.00 9.00
.603
584
E.S. Ko et al. / Clinical Imaging 39 (2015) 582–586
Table 2 Agreement of the two BI-RADS-like scores (Qˍabd) between the two serial examinations Qˍabd (2nd)
Qˍabd (1st)
1 2 3 4
1
2
3
4
2 1 0 0
1 11 0 0
0 4 10 1
0 0 1 13
mammographic density changes during the short-term use of adjuvant endocrine therapy could be used as a significant predictor of long-term recurrence in women with estrogen-receptor-positive breast cancer. According to their results, the hazard ratios for recurrence in patients who experienced ˂5% absolute mammographic density reduction and those in patients with increased mammographic density after approximately 1 year of endocrine therapy were 1.92 and 2.26, respectively (P = .048 and .027, respectively), compared to patients with a mammographic density reduction of ≥ 10% [25]. Many other previous studies have demonstrated changes in mammographic density associated with hormonal therapy in a wide range of absolute differences from 4% to 13% [7–10]. However, to be used as a treatment monitoring modality, mammographic density should have good reproducibility. To investigate the reproducibility, Kim et al. [22] retrospectively evaluated the variability of current breast density assessment methods in short-term reimaging of the same breast using digital mammography. According to their study, for BI-RADS assessment, 29% were assessed differently by one category after short-term reimaging. The mean absolute differences in percentage density in short-term serial CC and MLO view mammograms were 7.4% and 6.4%, respectively, in computer-aided assessment using Cumulus software. Furthermore, 29% (CC view) and 22% (MLO view) of computer-aided assessments had discrepancies of greater than 10% density, which were often used as criteria for significant changes after hormonal therapy [7,25]. They concluded that considerable variability in breast density assessments occurred in short-term reimaging with digital mammography, particularly in women of younger age, with greater breast density, and when examined using different types of mammography equipment. Different types of mammography systems had more frequently discrepant assessments than equivalent mammography systems (30% vs. 7% in computer-aided assessments). We questioned the wide variability in their study caused by the inclusion of various types of mammography machines and not by the limitations of the quantitative density measurement software. Additionally, we were concerned about the reproducibility of the Cumulus software used in their study. Studies using Cumulus software, which has been the most widely used package for mammographic density measurements in previous studies, indicate good intraobserver reproducibility with correlation coefficients of 0.92–0.96 [25,26]. Despite the good reproducibility reported, because Cumulus software requires the placement of a dichotomous threshold between dense and nondense tissue, the measurements could still be subjective according to the operator [27]. We presumed that automated volumetric density measurements could solve these issues. Therefore, we designed this study to evaluate the reproducibility of serial imaging in a short-term interval using the same digital mammography equipment and automated volumetric density analyses. Compared to Kim’s results regarding digital radiography with direct conversion type (direct DR)/direct DR or direct radiography with indirect conversion type (indirect DR)/indirect DR, the absolute differences between the two serial examinations were much smaller in our study. However, the question remains whether dense breast volume or dense breast area is more appropriate as a treatment monitoring parameter in breast cancer. A study by Boyd et al. comparing the predictive value of dense volume and dense area on mammograms found that measurements of the volume of dense tissue did not improve breast
Fig. 2. A 46-year-old woman who underwent sonography-guided needle localization. (a) First mediolateral oblique mammogram shows small mass in her upper breast. (b) First output of automated volumetric breast density. (c) Second mediolateral oblique mammogram obtained 15 days after first mammogram shows a needle within previous mass. (d) Second output of automated volumetric breast density.
cancer risk prediction, contrary to expectations [28]. Engelken et al. [23] compared the reproducibility of two volumetric parameters, fibroglandular tissue volume and percent density, using the same automated volumetric mammographic density analysis software as in our study. According to their study, the volumetric measurement of fibroglandular tissue volume is a very highly reproducible parameter in serial mammograms and shows better reproducibility than percent
E.S. Ko et al. / Clinical Imaging 39 (2015) 582–586
585
Fig. 3. Spearman’s correlation coefficients of four parameters from serial mammograms obtained on the same mammography unit. (a) Vfg, (b) Vb, (c) Vbd, (d) Abd.
density in consecutive mammograms of normal women, suggesting that it may be a better parameter for evaluating breast composition in longitudinal studies. However, their study used a long duration between consecutive mammograms, with a median interval of 14.9 months. This limited the conclusions that can be drawn from their study. Our study showed that automated volumetric breast density measurements have very high reproducibility when the same mammography machines were used in a short interval. In our study, the reproducibility of Vb and Vfg expressed as cm 3 was also excellent. However, because quite bigger number changes than Vbd and Abd expressed as %, they would be hard to use Vb and Vfg for monitoring treatment efficacy or as a risk assessment parameter. Abd showed better reproducibility than Vbd (Spearman’s rho = 0.967 and 0.956, respectively) but was not statistically significant. In addition, the absolute difference of Vbd was smaller than that of Abd, which suggests that Vbd might have been more appropriate as a surrogate marker in interventional studies. Our study has several limitations. First, our study included only 44 patients. Because of the sample size, we could not perform a more comprehensive analysis. Second, because this was a retrospective study, we could not consider the menstrual cycles of the premenopausal women. Breast tissue is known to be less radiopaque in the follicular phase than in the luteal phase [29,30]. When considering this finding, studies examining breast density as a surrogate biomarker in premenopausal women should be optimized to minimize the influence of menstrual cycle by controlling for the phase of the menstrual cycle in which the
mammogram is obtained. Third, we did not analyze various technical factors such as compression force, mAs, kVp, or breast thickness, which could affect the mammographic density assessment. Fourth, we did not investigate other host factors that can affect breast density such as body mass index, age at menarche, and parity. Fifth, our technicians performed mammography using previous mammograms as reference images. This meant that our study could accurately evaluate reproducibility of volumetric density assessment. However, this study design might be different from actual practice. Sixth, all second examinations were obtained after image-guided needle localization. It is possible that hemorrhage could have affected mammographic density measurements by increasing density. However, this study design was inevitable when considering the need to obtain two sets of mammography examinations within a very short interval. In a similar study by Engelken et al. [23], the mean interval between the two examinations was 14.9 months, while ours was 13.2 days. This is a strength of our study. In conclusion, automated volumetric breast density assessment from digital mammography shows extremely high reproducibility in consecutive examinations. Although Abd showed greater agreement than Vbd, it failed to reach statistical significance. The mean absolute difference was smaller for Vbd than Abd. Therefore, in assessments of treatment efficacy or risk prediction, we recommend that the same mammography machine be used. Furthermore, Vbd could be more feasibly used as a monitoring parameter.
586
E.S. Ko et al. / Clinical Imaging 39 (2015) 582–586
References [1] Mousa NA, Crystal P, Wolfman WL, Bedaiwy MA, Casper RF. Aromatase inhibitors and mammographic breast density in postmenopausal women receiving hormone therapy. Menopause 2008;15:875–84. [2] Jeon JH, Kang JH, Kim Y, Lee HY, Choi KS, Jun JK, et al. Reproductive and hormonal factors associated with fatty or dense breast patterns among Korean Women. Cancer Res Treat 2011;43:42–8. [3] 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. [4] Ursin G, Ma H, Wu AH, Bernstein L, Salane M, Parisky YR, et al. Mammographic density and breast cancer in three ethnic groups. Cancer Epidemiol Biomarkers Prev 2003;12:332–8. [5] Nagata C, Matsubara T, Fujita H, Nagao Y, Shibuya C, Kashiki Y, et al. Mammographic density and the risk of breast cancer in Japanese women. Br J Cancer 2005;92:2102–6. [6] 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. [7] Cuzick J, Warwick J, Pinney E, Duffy SW, Cawthorn S, Howell A, et al. Tamoxifeninduced reduction in mammographic density and breast cancer risk reduction: a nested case–control study. J Natl Cancer Inst 2011;103:744–52. [8] Weitzel JN, Buys SS, Sherman WH, Daniels A, Ursin G, MacDonald DJ, et al. Reduced mammographic density with use of a gonadotropin-releasing hormone agonist-based chemoprevention regimen in BRCA1 carriers. Clin Cancer Res 2007;13(2 Pt 1):654–8. [9] Ursin G, Astrahan MA, Salane M, Parisky YR, Pearce JG, Daniels JR, et al. The detection of changes in mammographic densities. Cancer Epidemiol Biomarkers Prev 1998;7:43–7. [10] Smith J, Dilawari A, Ursin G, Andreopoulou E, Checka C, Axelrod D, et al. A pilot study of letrozole for one year in women at enhanced risk of developing breast cancer: effects on mammographic density. Anticancer Res 2012;32:1327–31. [11] Harvey JA, Bovbjerg VE, Smolkin ME, Williams MB, Petroni GR. Evaluating hormone therapy-associated increases in breast density comparison between reported and simultaneous assignment of BI-RADS categories, visual assessment, and quantitative analysis. Acad Radiol 2005;12:853–62. [12] Kerlikowske K, Ichikawa L, Miglioretti DL, Buist DS, Vacek PM, Smith-Bindman R, et al. Longitudinal measurement of clinical mammographic breast density to improve estimation of breast cancer risk. J Natl Cancer Inst 2007;99:386–95. [13] Lee-Han H, Cooke G, Boyd NF. Quantitative evaluation of mammographic densities: a comparison of methods of assessment. Eur J Cancer Prev 1995;4:285–92. [14] Byng JW, Yaffe MJ, Jong RA, Shumak RS, Lockwood GA, Tritchler DL, et al. Analysis of mammographic density and breast cancer risk from digitized mammograms. Radiographics 1998;18:1587–98. [15] Ciatto S, Houssami N, Apruzzese A, Bassetti E, Brancato B, Carozzi F, et al. Categorizing breast mammographic density: intra- and interobserver reproducibility of BIRADS density categories. Breast 2005;14:269–75.
[16] 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. [17] Pérez-Gómez B, Ruiz F, Martínez I, Casals M, Miranda J, Sánchez-Contador C, et al. Women’s features and inter-/intra-rater agreement on mammographic density assessment in full-field digital mammograms (DDM-SPAIN). Breast Cancer Res Treat 2012;132:287–95. [18] Jeffreys M, Warren R, Highnam R, Smith GD. Initial experiences of using an automated volumetric measure of breast density: the standard mammogram form. Br J Radiol 2006;79:378–82. [19] 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. [20] Lokate M, Kallenberg MG, Karssemeijer N, Van den Bosch MA, Peeters PH, Van Gils CH. Volumetric breast density from full-field digital mammograms and its association with breast cancer risk factors: a comparison with a threshold method. Cancer Epidemiol Biomarkers Prev 2010;19:3096–105. [21] 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. [22] Kim WH, Moon WK, Kim SM, Yi A, Chang JM, Koo HR, et al. Variability of breast density assessment in short-term reimaging with digital mammography. Eur J Radiol 2013;82:1724–30. [23] Engelken F, Singh JM, Fallenberg EM, Bick U, Böttcher J, Renz DM. Volumetric breast composition analysis: reproducibility of breast percent density and fibroglandular tissue volume measurements in serial mammograms. Acta Radiol 2014;55:32–8. [24] 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. [25] Kim J, Han W, Moon HG, Ahn SK, Shin HC, You JM, et al. Breast density change as a predictive surrogate for response to adjuvant endocrine therapy in hormone receptor positive breast cancer. Breast Cancer Res 2012;14:R102. http://dx.doi.org/10.1186/bcr3221. [26] Byng JW, Boyd NF, Fishell E, Jong RA, Yaffe MJ. The quantitative analysis of mammographic densities. Phys Med Biol 1994;39:1629–38. [27] Boyd NF, Martin LJ, Bronskill M, Yaffe MJ, Duric N, Minkin S. Breast tissue composition and susceptibility to breast cancer. J Natl Cancer Inst 2010;102:1224–37. [28] 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. [29] Morrow M, Chatterton RT, Rademaker AW, Hou N, Jordan VC, Hendrick RE, et al. A prospective study of variability in mammographic density during the menstrual cycle. Breast Cancer Res Treat 2010;121:565–74. [30] White E, Velentgas P, Mandelson MT, Lehman CD, Elmore JG, Porter P, et al. Variation in mammographic breast density by time in menstrual cycle among women aged 40–49 years. J Natl Cancer Inst 1998;90:906–10.
