- Email: [email protected]
The relationship of obesity, mammographic breast density, and magnetic resonance imaging in patients with breast cancer
Clinical Imaging 40 (2016) 1167–1172
Contents lists available at ScienceDirect
Clinical Imaging journal homepage: http://www.clinicalimaging.org
The relationship of obesity, mammographic breast density, and magnetic resonance imaging in patients with breast cancer Jennifer Gillman a, Jennifer Chun b, Shira Schwartz b, Freya Schnabel b, Linda Moy a,⁎ a b
Department of Radiology, New York University School of Medicine, Perlmutter Cancer Center, 160 East 34th Street, New York, NY, 10016, USA Department of Surgery, New York University School of Medicine, Perlmutter Cancer Center, 160 East 34th Street, New York, NY, 10016, USA
a r t i c l e
i n f o
Article history: Received 26 May 2016 Received in revised form 27 July 2016 Accepted 8 August 2016 Available online xxxx Keywords: Breast cancer Mammographic breast density Magnetic resonance imaging Fibroglandular tissue Background parenchymal enhancement
a b s t r a c t Purpose: The purpose was to evaluate the relationship between body mass index (BMI), mammographic breast density, magnetic resonance (MR) background parenchymal enhancement (BPE), and MR fibroglandular tissue (FGT) in women with breast cancer. Methods: Our institutional database was queried for patients with preoperative mammography and breast MR imaging. Results: There were 573 women eligible for analysis. Elevated BMI was associated with advanced stage of disease (P=.01), lower mammographic density (Pb.0001), lower FGT (Pb.0001), higher BPE (P=.005), and nonpalpable lesions (P=.04). Conclusions: Higher BMI was associated with decreased breast density and FGT. Higher BMI was also associated with advanced stage disease and nonpalpable tumors on clinical exam. © 2016 Elsevier Inc. All rights reserved.
1. Introduction According to the Centers for Disease Control and Prevention, more than one third of adult women are obese, and the rate of obesity significantly increases with age [1]. As of 2013, the American Medical Association officially recognized obesity as a disease, and it is a modifiable risk factor for type-2 diabetes mellitus and cardiovascular disease. In addition, postmenopausal obese women have a 31% increased risk of developing breast cancer [2]. This would suggest that obese women represent a population that would benefit from regular breast cancer screening. Screening mammography provides information regarding the presence or absence of suspicious findings and also provides the referring clinician with information regarding the density of the breast tissue. Mammographic breast density is described as the proportion of glandular tissue to fatty tissue. In clinical practice, it is assessed using a fourcategory score defined in the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) [3]. Dense breast tissue on mammography is an independent risk factor for breast cancer which also lowers the sensitivity of the exam [4–7]. According to Boyd et al., extremely dense breast tissue causes a masking effect which increases the odds of detecting breast cancers between screenings [odds ratio (OR)=17.8, comparing ≥75% to b10% dense breast tissue] [4]. Furthermore, the relative risk of breast cancer in women with ≥50% dense ⁎ Corresponding author. New York University School of Medicine, 160 East 34th Street, New York, NY, 10016. Tel.: +1 212 731 5333; fax: +1 212 731 6051. E-mail address: [email protected] (L. Moy). http://dx.doi.org/10.1016/j.clinimag.2016.08.009 0899-7071/© 2016 Elsevier Inc. All rights reserved.
breast parenchyma is 2.5–6-fold higher compared to women with predominantly fatty breasts [7]. At present, 21 states require women to be notified if they are found to have dense breast tissue [8] on routine screening mammography, presumably in order to allow for a discussion regarding the advisability of supplemental screening methods. Although elevated body mass index (BMI) is associated with lower breast density, obese women are at increased risk for postmenopausal breast cancer [9–12]. In addition to regular mammography, magnetic resonance imaging (MRI) is increasingly used as part of the recommended screening for women at higher risk for developing breast cancer. Breast tissue characteristics that are assessed on MRI include background parenchymal enhancement (BPE) and fibroglandular tissue (FGT). BPE represents the enhancement of normal breast tissue after administering intravenous contrast and reflects the vascularity of the FGT. FGT is a threedimensional (3D) representation of fibroglandular tissue on MRI. BPE and, to a lesser extent, FGT are hormonally regulated and decrease with menopause, use of antiestrogen therapies, and history of bilateral salpingo-oophorectomy [13–17]. Despite the association of obesity with an increased risk for breast cancer, higher BMI is associated with decreased breast density on mammography. At present, there is no information regarding the effect of BMI on FGT or BPE as evaluated on MRI. The main purpose of this study was to examine the association between BMI and mammographic breast density, BPE, and FGT in women with breast cancer. Additionally, we examined the association between BMI and clinical characteristics, such as clinical presentation and stage of disease.
1168
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
2. Materials and methods 2.1. Study participants The Breast Cancer Database at our medical center is a longitudinal registry that was established in January 2010. All patients undergoing definitive breast cancer surgery at our institution are eligible to enroll in the Breast Cancer Database. The variables collected include information on personal and family history, screening history, methods of diagnosis, stage at diagnosis, details of treatment, and outcomes. All clinical data were obtained from detailed questionnaires filled out at the time of diagnosis and review of the electronic medical records. This study was approved by the Institutional Review Board and was compliant with the standards of the Health Insurance Portability and Accountability Act. Patients included in this study were enrolled in the Breast Cancer Database between January 2010 and September 2014 and had both mammography and breast MRI prior to surgery. Each patient contributed a single examination to the study. Men were excluded from this study. Demographic information, indication for the examination, and information including mammographic density and BI-RADS assessment were obtained from the electronic medical record. Reader interpretations of BPE, FGT, and mammographic breast density (below) were sent to a data manager who consolidated this information in an Excel spreadsheet. 2.2. Diagnostic imaging 2.2.1. Mammography imaging technique All mammograms were performed with digital technique and were acquired using MAMMOMAT Novation DR software (version V8.3, Siemens Healthcare). Based on routine institutional practice, the images were further analyzed by iCAD computer-aided detection software (iCAD, version VA20E; iCAD, Inc., Nashua, NH, USA). 2.2.2. MRI technique Bilateral dynamic contrast-enhanced breast MRI examinations for premenopausal women are scheduled during the second week (days 8–14) of their menstrual cycle. All breast MRI examinations were performed on commercially available systems at 1.5 T (Avanto, Siemens Medical Solutions) or 3.0 T (TIM Trio, Siemens Medical Solutions) with the patient in prone position using a dedicated surface breast coil (7-channel Breast Biopsy Array, InVivo Research). The standard imaging protocol includes a localizing sequence followed by a sagittal T2weighted sequence (repetition time/echo time, 7220/84), a sagittal T1-weighted non-fat-suppressed 3D fast spoiled gradient-recalled echo sequence (4.01/1.52; flip angle, 12°; matrix, 384×384; field of view, 270 mm; section thickness, 1 mm), followed by the same sagittal T1-weighted fat-suppressed 3D fast spoiled gradient-recalled echo sequence performed before and four times after a rapid bolus injection of 0.1 mmol/L of gadopentetate dimeglumine (Magnevist, Bayer Healthcare Pharmaceuticals) per kilogram of body weight at an injection rate of 2.0 ml/s via an intravenous catheter. Image acquisition began immediately after administration of the contrast material and saline bolus. The first contrast-enhanced dynamic sequence was obtained at approximately 100 s, followed by four additional consecutive sequences (three sagittal followed by one delayed axial). The delayed axial images were obtained so that subtle asymmetric BPE could be appreciated. Postprocessing included subtraction images and maximum intensity projection images. Images were reviewed on high-resolution picture archiving and communication system monitors. 2.3. Image assessment Mammographic breast density was categorized according to the American College of Radiology as entirely fatty, scattered fibroglandular, heterogeneously dense, or extremely dense breasts (Table 1 and Fig. 1)
Table 1 BI-RADS classifications of mammographic breast density, FGT, and BPE BI-RADS
Mammographic breast density
FGT
BPE
a b c
Almost entirely fatty Scattered fibroglandular Heterogeneously dense Extremely dense
Almost entirely fatty Scattered fibroglandular tissue Heterogeneous fibroglandular tissue Extreme fibroglandular tissue
Minimal Mild Moderate
d
Marked
[3]. All mammograms were assessed for breast density by two fellowship-trained breast radiologists in consensus. All breast MRI examinations were assessed for BPE, in consensus, by two fellowship-trained breast radiologists who had up to 12 years of experience in reading breast MRI. Both readers were blinded to mammographic density, clinical data of the patients, and pathology results. The level of global BPE, rather than the highest BPE in a single quadrant, was assessed using a combination of pre- and the first postcontrast T1weighted fat-saturated and subtracted images and was recorded on a four-point scale (a: minimal, b: mild, c: moderate, d: marked) in accordance with latest BI-RADS categories (Table 1 and Fig. 1) [3]. The volume and intensity of enhancement were considered in the global assessment of BPE and categorized on the basis of MR BI-RADS criteria as minimal, mild, moderate, or marked. The volume of breast parenchymal enhancement was estimated qualitatively by reviewing the amount of enhancing FGT on multiple contiguous slices. Furthermore, the amount of FGT was evaluated using the following scale based on American College of Radiology BI-RADS criteria: entirely fatty, scattered fibroglandular, heterogeneously fibroglandular, and extreme fibroglandular tissue (Table 1 and Fig. 1) [3]. In cases of asymmetry of the breasts, the higher level of mammographic density, BPE, and FGT was recorded. 2.4. Statistical analyses Statistical analyses were performed using descriptive statistics, analysis of variance (ANOVA), Pearson's chi-square, and linear and logistic regression. The variables of interest included age, family history of breast cancer, atypical hyperplasia, lobular carcinoma in situ (LCIS), tumor characteristics, palpability, mammographic breast density, BPE, FGT, menopausal status, use of chemoprevention, and screening frequency as defined as the number of mammograms the patients had in the past 6 years. In our analyses, we looked at BMI as a categorical variable: underweight (≤18 kg/m 2), normal weight (18–24 kg/m 2), overweight (25–29 kg/m 2), and obese (≥30 kg/m 2), in accordance with the World Health Organization criteria [18]; as a dichotomous variable (b25 kg/m2 and ≥25 kg/m 2) in accordance with the increased risk of breast cancer demonstrated in overweight and obese postmenopausal women [2,19,20]; and as a continuous variable. All analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA). 3. Results Out of a total of 1991 women enrolled in the Breast Cancer Database at the time of the study, 573 (29%) patients had both a mammogram and a breast MRI prior to their breast cancer surgery. Of these women, the median age was 53 years (22–86 years). The majority of women were Caucasian (73%) and postmenopausal (54%) with a median BMI of 24.9 kg/m 2 (range: 16.8–46.3). The majority of women underwent annual screening mammography (52%) prior to their cancer diagnosis. The majority of breast cancers in this group were detected by mammography (53%). The majority of the patients had stage 0 or stage 1 breast cancer, with ductal carcinoma in situ representing 23% of the total. The median invasive tumor size was 1.4 cm (range 0.01–12.5 cm). The majority of patients were estrogen receptor (ER) positive (84%), progesterone receptor positive (71%), and HER2neu negative (86%) (Table 2). Forty-three3 (8%) of the patients had triple-negative breast cancer. Only
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
1169
Fig. 1. BI-RADS categories of mammographic breast density, FGT, and BPE. Row I: Digital mammography images show different breasts with (A) almost entirely fatty, (B) scattered fibroglandular, (C) heterogeneously dense, and (D) extremely dense breast tissue. Row II: T1-weighted non-fat-suppressed MR images show different breasts with (A) almost entirely fatty, (B) scattered, (C) heterogeneously dense, and (D) extremely dense amounts of FGT. Row III: T1-weighted fat-suppressed contrast-enhanced subtraction MR images show different breasts with (A) minimal, (B) mild, (C) moderate, and (D) marked BPE.
5% of patients were on hormonal therapy for chemoprevention, and 12% of patients had a history of hormone replacement therapy usage prior to being diagnosed with breast cancer. When BMI was treated as a dichotomous variable (BMI b25 kg/m 2 and BMI ≥25 kg/m 2), we found that women with a BMI ≥25 were more likely to be older (Pb.0001) and postmenopausal (Pb.0001) than women with a BMI b25. There was no association between BMI and family history of breast cancer (P=.87), history of atypical hyperplasia (P=.82) or LCIS (P=.76), and tumor histology (P=.88) (Table 2). We also found no association between BMI and invasive tumor size (P= .10). However, there was a statistically significant association between higher BMI and later stage of disease at diagnosis (P=.01) (Table 2). In regard to the relationship of imaging characteristics and BMI, we found that women who had a BMI ≥25 had a lower mammographic breast density (Pb.0001), lower FGT (Pb.0001), and higher BPE (P= .005) (Fig. 2). Patients with a BMI ≥25 were more likely to have predominantly fatty breasts and less likely to have extremely dense breasts [OR=21.3, 95% confidence interval (CI): 7.3–62.1]. A similar inverse relationship was seen between BMI and FGT on MRI (OR=26.8, 95% CI: 12.0–60.0). Women who had a BMI ≥25 were more likely to have moderate (OR=1.72, 95% CI: 1.02–2.90) or mild BPE (OR=2.08, 95% CI: 1.30–3.34) and less likely to have minimal BPE. When BMI was treated as a continuous variable, we found that the same significant associations existed between BMI and stage of breast cancer (P=.002), mammographic breast density (Pb.0001), BPE (P=.02), and FGT (Pb.0001). When BMI was treated as a categorical variable with four categories, there was a significant association between BMI and mammographic breast density (Pb.0001) and FGT (Pb.0001), but no association between BMI and BPE (P=.07). Since breast density, BPE, and FGT typically decrease with age, we performed a linear regression analysis with age as a confounding variable. Controlling for age, we found that lower breast density (Pb.0001), lower FGT (Pb.0001), and higher BPE (Pb.0001) all remained
significantly associated with elevated BMI. Similarly, since studies have shown that BPE and FGT are influenced by hormonal changes, after controlling for menopausal status, we found that the association between BMI and stage of breast cancer (P=.04), mammographic breast density (Pb.0001), FGT (Pb.0001), and BPE (Pb.0001) remained statistically significant. With regard to method of breast cancer presentation and BMI, women who had a higher BMI were more likely to present with nonpalpable cancers (P=.04). We also found no difference in screening behavior in women with higher BMI compared to women with lower BMI (P=.29). We then performed a logistic regression looking at BMI as a dichotomous outcome with the imaging characteristics using age as a confounding variable to confirm our above findings. After controlling for age, we found that lower breast density (Pb.0001), lower FGT (Pb.0001), and higher BPE (P=.002) were associated with having a BMI ≥25. When we controlled for menopausal status, we found that the association between higher BMI and higher stage of breast cancer (P=.01), lower breast density (Pb.0001), lower FGT (Pb.0001), and higher BPE (P= .007) remained significant.
4. Discussion In support of the literature, our study shows that BMI is inversely associated with breast density. Patients with a BMI ≥25 were more likely to have predominantly fatty breast tissue and less likely to have extremely dense breast tissue. Our study shows a similar inverse relationship between BMI and MR-FGT. This further contributes to the theory that mammographic density and FGT are similar in etiology, both reflecting the amount of fibroglandular breast tissue in proportion to the amount of fatty tissue. Prior studies demonstrate mixed results regarding the relationship between BMI and FGT or BPE [17,18]. Given that FGT provides a 3D analysis of fibroglandular tissue, FGT may be a
1170
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
Table 2 Patient characteristics and relationship with BMI* Variables
Total (N=573)
Median age, years
53 (22–86)
Menopausal No Yes Family history of breast cancer Negative Positive Atypical hyperplasia Negative Positive LCIS Negative Positive Histology Ductal carcinoma in situ Invasive ductal carcinoma Invasive lobular carcinoma Other invasive Median tumor size, cm Stage of breast cancer 0 I IIA, IIB IIIA, IIIB, IIIC No residual cancer Estrogen receptor Positive Negative Missing Progesterone receptor Positive Negative Missing Her2-Neu Positive Negative Equivocal NA/Unknown
%
BMI b25 (n=288)
%
50 (22–84)
BMI ≥25 (n=285)
%
57 (27–86)
P value Pb.0001
265 308
46 54
158 130
55 45
107 178
38 62
Pb.0001
426 147
74 26
215 73
75 25
211 74
74 26
P=.87
556 17
97 3
279 9
97 3
277 8
97 3
P=.82
560 13
98 2
282 6
98 2
278 7
98 2
P=.76
135 353 61 24 1.4 (0.01–12.5)
23 62 11 4
65 181 30 12 1.2 (0.04–12.5)
23 63 10 4
70 172 31 12 1.5 (0.01–11.0)
25 60 11 4
P=.88
131 273 128 37 4
23 48 22 6 1
66 152 59 10 1
23 53 21 3 0.4
65 121 69 27 3
23 42 24 10 1
P=.01
473 91 9
84 16 –
238 45 5
84 16 –
235 46 4
84 16 –
P=.88
400 162 11
71 29 –
204 77 7
73 27 –
196 85 4
70 30 –
P=.46
50 373 10 140
12 86 2 –
31 182 5 70
14 84 2 –
19 191 5 70
9 89 2 –
P=.21
P=.10
⁎ Pearson chi-squared analyses.
more objective and quantitative measurement of breast density and may be more useful for breast cancer risk assessment [19]. To our knowledge, this is the largest study to specifically examine the relationship between BMI, FGT, and BPE. We found higher BMI to be associated with elevated BPE, with overweight and obese women more likely to have moderate (type 3) or mild (type 2) BPE. King et al. have described an association between increased BPE and increased risk of developing breast cancer [22], and although this study is not designed to assess breast cancer risk, we found that women with a higher BMI had higher BPE. Elevated BPE can limit the interpretation of MRI examination resulting in greater false-positive rates [20,21]. In postmenopausal women, higher BMI is an important risk factor for developing breast cancer [23]. Interestingly, our study demonstrates an association between higher BMI and elevated BPE despite the finding that these patients were statistically more likely to be postmenopausal and older. BPE represents the vascularity of the FGT and is known to be hormonally sensitive, decreasing with menopause, antiestrogen therapies, and bilateral salpingo-oophorectomy [13–17]. The biological mechanism behind the increased risk in postmenopausal women with increased BMI is not fully understood. Obese and overweight postmenopausal women may maintain relatively higher levels of peripheral estrogen production, which is one theory as to why this patient population is at increased risk for breast cancer. This increased production of estrogen by the adipose tissue becomes the principal site of estrogen biosynthesis in postmenopausal women and may have multiple effects [24–27]. We hypothesize that the increased levels of estrogen may lead to higher BPE in our cohort. Additionally, estrogen enhances cell proliferation and may result in ER-positive tumors diagnosed at a later stage
[22]. Estrogen levels were not measured in this study, and our study found no association between ER-positive disease and BMI. Furthermore, an alternative mechanism describes the associations between obesity, proinflammatory cytokines/adipokines, and macrophage infiltration, promoting tumor development and angiogenesis [2,28,29]. Proinflammatory cytokines were not measured in this study, but monitoring such markers of inflammation should be considered in future research. Obesity in postmenopausal breast cancer has been associated with larger tumor size, advanced stage at diagnosis, and poorer prognosis [10,22,30,31]. In support of the literature, our patients who were overweight and obese were more likely to be diagnosed with stage III breast cancer, an association that continued to be significant after accounting for both age and menopausal status. We also found that breast cancers diagnosed with obese and overweight women were more likely to be nonpalpable with no significant difference in tumor size. It has been argued that obesity is associated with delayed breast cancer diagnosis due to barriers in accessing health care [32–34]. Wee et al. and Ferrante et al. show that obese women have significantly lower compliance with mammography screening, independent of income and other comorbidities [35,36]. Reasons for this potential difference in screening behavior include embarrassment about being weighed or undergoing physical exam and possible negative attitudes toward obese patients. In our study, we found no difference in mammographic screening frequency with BMI, but overall, only 52% of patients were getting annual mammograms. In response to the change in the US Preventive Services Task Force guidelines for breast cancer screening recommending biennial screening
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
1171
a
b
c
Fig. 2. Imaging characteristics and relationship with BMI. Graphs showing relationship between BMI and (a) mammographic breast density, (b) MR FGT, and (c) MR BPE. P values reflect the Pearson's chi-square analyses examining the association between BMI and imaging characteristics.
beginning at age 50 [37], obese and overweight women may benefit more from regular screening with mammography, given their increased risk and mortality of breast cancer as well as the improved sensitivity of mammography with decreasing breast density. Based on our results, annual screening with imaging may be particularly important because obese women were less likely to have cancerous lesions palpable on clinical breast exam (CBE). Obesity and breast size are thought to be associated with decreases in CBE sensitivity [35,36], although this has not been extensively studied and many reports evaluating CBE do not account for BMI or breast size [38–41]. Limitations of this study include its retrospective nature and limited generalizability. Our population represents a majority of Caucasian women (73%) presenting to private academic medical center. Interobserver variability of mammographic breast density was minimized by having both readers assess the study in consensus. We realize that the agreement may be particularly difficult to assess for the two middledensity categories and that automated systems have been developed recently that will provide the absolute volumetric breast density.
However, these systems use different methodologies to quantify breast density and have not been validated in large series of patients. Furthermore, only 29% of these patients met the criteria of having both a mammogram and a breast MRI. Although many women get a breast MRI for being at high risk of breast cancer, only 26% of these patients had a family history of breast cancer. Preoperative breast MRI exams are not routinely performed at our institution; however, recommending a breast MRI exam is dependent on the treating physician. In addition, all patients presenting for surgery of a known breast cancer were consecutively enrolled into the Breast Cancer Database, minimizing selection bias. Lastly, our population of interest consisted of women recently diagnosed with breast cancer. Since we did not compare women with and without breast cancer, we cannot generalize our results to all women. Further prospective studies are necessary to better understand the use of BMI, BPE, and FGT as predictors of disease. In conclusion, we have found that higher BMI is associated with later-stage breast cancers, as well as tumors that are less likely to be
1172
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
palpable on physical exam. Increased BMI is also associated with lower breast density, and therefore, overweight and obese patients would benefit from close monitoring with screening mammography. For obese and overweight women undergoing breast MRI, they are more likely to have lower FGT and elevated BPE. More research is needed to further elucidate the use of BPE and FGT as risk factors for the development of breast cancer. Acknowledgments We would like to acknowledge our institution's Clinical and Translational Science Institute for their mentorship, guidance, and support. References [1] Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity in the United States, 2009–2010. NCHS Data Brief 2012;82:1–7. [2] Carmichael AR. Obesity as a risk factor for development and poor prognosis of breast cancer. BJOG 2006;113(10):1160–6. [3] D'orsi C, Sickles E, Mendelson E, Morris E. ACR BI-RADS® atlas, breast imaging reporting and data system. 5th ed. American College of Radiology. American College of. Radiology: Reston, VA; 2013. [4] Boyd NF, Martin LJ, Yaffe MJ, Minkin S. Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res 2011;13(6):223. [5] 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(11):2086–92. [6] Kerlikowske K. The mammogram that cried Wolfe—NEJM. N Engl J Med 2007; 356(3):297–300. [7] McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2006;15(6):1159–69. [8] Two more states require breast density reporting—American College of Radiology [internet]. [cited 2015 Feb 3]. Available from: http://www.acr.org/Advocacy/ eNews/20150123-Issue/Two-More-States-Require-Breast-Density-Reporting. [9] Baglietto L, Krishnan K, Stone J, Apicella C, Southey MC, English DR, et al. Associations of mammographic dense and nondense areas and body mass index with risk of breast cancer. Am J Epidemiol 2014;179(4):475–83. [10] Gierach GL, Ichikawa L, Kerlikowske K, Brinton LA, Farhat GN, Vacek PM, et al. Relationship between mammographic density and breast cancer death in the breast cancer surveillance consortium. J Natl Cancer Inst 2012;104(16):1218–27. [11] Razzaghi H, Troester MA, Gierach GL, Olshan AF, Yankaskas BC, Millikan RC. Mammographic density and breast cancer risk in white and African American women. Breast Cancer Res Treat 2012;135(2):571–80. [12] Harris HR, Tamimi RM, Willett WC, Hankinson SE, Michels KB. Body size across the life course, mammographic density, and risk of breast cancer. Am J Epidemiol 2011; 174(8):909–18. [13] Amarosa AR, McKellop J, Klautau Leite AP, Moccaldi M, Clendenen TV, Babb JS, et al. Evaluation of the kinetic properties of background parenchymal enhancement throughout the phases of the menstrual cycle. Radiology 2013;268(2):356–65. [14] King V, Gu Y, Kaplan JB, Brooks JD, Pike MC, Morris EA. Impact of menopausal status on background parenchymal enhancement and fibroglandular tissue on breast MRI. Eur Radiol 2012;22(12):2641–7. [15] King V, Kaplan J, Pike MC, Liberman L, David Dershaw D, Lee CH, et al. Impact of tamoxifen on amount of fibroglandular tissue, background parenchymal enhancement, and cysts on breast magnetic resonance imaging. Breast J 2012;18(6):527–34. [16] King V, Goldfarb SB, Brooks JD, Sung JS, Nulsen BF, Jozefara JE, et al. Effect of aromatase inhibitors on background parenchymal enhancement and amount of fibroglandular tissue at breast MR imaging. Radiology 2012;264(3):670–8. [17] Price ER, Brooks JD, Watson EJ, Brennan SB, Comen EA, Morris EA. The impact of bilateral salpingo-oophorectomy on breast MRI background parenchymal enhancement and fibroglandular tissue. Eur Radiol 2014;24(1):162–8. [18] Physical status: the use and interpretation of anthropometry Report of a WHO expert committeeWorld Health Organ Tech Rep Ser 1995;854:1–452.
[19] Reeves GK, Pirie K, Beral V, Green J, Spencer E, Bull D. Cancer incidence and mortality in relation to body mass index in the million women study: cohort study. BMJ 2007; 335(7630):1134. [20] Van den Brandt PA, Spiegelman D, Yaun SS, Adami HO, Beeson L, Folsom AR, et al. Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol 2000;152(6):514–27. [21] Chun J, Refinetti AP, Klautau Leite A, Schnabel F, Hochman T, Moy L. The relationship of breast density, BMI, and menopausal status in mammography and MRI. J Clin Oncol 2012;30(Suppl. 27) [abstr 36]. [22] King V, Brooks JD, Bernstein JL, Reiner AS, Pike MC, Morris EA. Background parenchymal enhancement at breast MR imaging and breast cancer risk. Radiology 2011; 260(1):50–60. [23] Research WCRFaAIfC. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Washington DC: AICR; 2007. [24] Savolainen-Peltonen H, Vihma V, Leidenius M, Wang F, Turpeinen U, Hamalainen E, et al. Breast adipose tissue estrogen metabolism in postmenopausal women with or without breast cancer. J Clin Endocrinol Metab 2014;99(12):E2661–7. http://dx.doi. org/10.1210/jc.2014-2550. [25] Key TJ, Appleby PN, Reeves GK, Roddam A, Dorgan JF, Longcope C, et al. Endogenous hormones breast cancer collaborative G. Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women. J Natl Cancer Inst 2003;95(16): 1218–26. [26] Rinaldi S, Key TJ, Peeters PH, Lahmann PH, Lukanova A, Dossus L, et al. Anthropometric measures, endogenous sex steroids and breast cancer risk in postmenopausal women: a study within the EPIC cohort. Int J Cancer 2006;118(11):2832–9. http:// dx.doi.org/10.1002/ijc.21730. [27] Nieman KM, Romero IL, Van Houten B, Lengyel E. Adipose tissue and adipocytes support tumorigenesis and metastasis. Biochim Biophys Acta 2013;1831(10):1533–41. http://dx.doi.org/10.1016/j.bbalip.2013.02.010. [28] Giess CS, Yeh ED, Raza S, Birdwell RL. Background parenchymal enhancement at breast MR imaging: normal patterns, diagnostic challenges, and potential for false-positive and false-negative interpretation. RadioGraphics 2014;34(1): 234–47. [29] DeMartini WB, Liu F, Peacock S, Eby PR, Gutierrez RL, Lehman CD. Background parenchymal enhancement on breast MRI: impact on diagnostic performance. AJR Am J Roentgenol 2012;198(4):W373–80. [30] Eichholzer M, Huang DJ, Modlasiak A, Schmid SM, Schötzau A, Rohrmann S, et al. Impact of body mass index on prognostically relevant breast cancer tumor characteristics. Breast care (Basel), 8(3). Karger Publishers; 2013. p. 192–8. [31] Khan S, Shukla S, Sinha S, Meeran SM. Role of adipokines and cytokines in obesityassociated breast cancer: therapeutic targets. Cytokine Growth Factor Rev 2013; 24(6):503–13. [32] Sun X, Casbas-Hernandez P, Bigelow C, Makowski L, Joseph Jerry D, Smith Schneider S, et al. Normal breast tissue of obese women is enriched for macrophage markers and macrophage-associated gene expression. Breast Cancer Res Treat 2012; 131(3):1003–12. [33] Majed B, Moreau T, Senouci K, Salmon RJ, Fourquet A, Asselain B. Is obesity an independent prognosis factor in woman breast cancer? Breast Cancer Res Treat 2008; 111(2):329–42. [34] Niraula S, Ocana A, Ennis M, Goodwin PJ. Body size and breast cancer prognosis in relation to hormone receptor and menopausal status: a meta-analysis. Breast Cancer Res Treat 2012;134(2):769–81. [35] Wee CC, McCarthy EP, Davis RB, Phillips RS. Screening for cervical and breast cancer: is obesity an unrecognized barrier to preventive care? Ann Intern Med 2000; 132(9):697–704. [36] Ferrante JM, Chen P-H, Crabtree BF, Wartenberg D. Cancer screening in women: body mass index and adherence to physician recommendations. Am J Prev Med 2007;32(6):525–31. [37] Recommendation summary—US Preventive Services Task Force [internet]. [cited 2015 Feb 3]. Available from: http://www.uspreventiveservicestaskforce.org/uspstf/ uspsbrca.htm. [38] Barton MB, Harris R, Fletcher SW. Does this patient have breast cancer? JAMA 1999; 282(13):1270. [39] McDonald S, Saslow D, Alciati MH. Performance and reporting of clinical breast examination: a review of the literature. CA Cancer J Clin 2004;54(6):345–61. [40] Chagpar AB, McMasters KM, Saul J, Nurko J, Martin RCG, Scoggins CR, et al. Body mass index influences palpability but not stage of breast cancer at diagnosis. Am Surg 2007;73(6):555–60 [discussion 560]. [41] Bobo JK, Lawson HW, Lee NC. Risk factors for failure to detect a cancer during clinical breast examinations (United States). Cancer Causes Control 2003;14(5):461–8.
Contents lists available at ScienceDirect
Clinical Imaging journal homepage: http://www.clinicalimaging.org
The relationship of obesity, mammographic breast density, and magnetic resonance imaging in patients with breast cancer Jennifer Gillman a, Jennifer Chun b, Shira Schwartz b, Freya Schnabel b, Linda Moy a,⁎ a b
Department of Radiology, New York University School of Medicine, Perlmutter Cancer Center, 160 East 34th Street, New York, NY, 10016, USA Department of Surgery, New York University School of Medicine, Perlmutter Cancer Center, 160 East 34th Street, New York, NY, 10016, USA
a r t i c l e
i n f o
Article history: Received 26 May 2016 Received in revised form 27 July 2016 Accepted 8 August 2016 Available online xxxx Keywords: Breast cancer Mammographic breast density Magnetic resonance imaging Fibroglandular tissue Background parenchymal enhancement
a b s t r a c t Purpose: The purpose was to evaluate the relationship between body mass index (BMI), mammographic breast density, magnetic resonance (MR) background parenchymal enhancement (BPE), and MR fibroglandular tissue (FGT) in women with breast cancer. Methods: Our institutional database was queried for patients with preoperative mammography and breast MR imaging. Results: There were 573 women eligible for analysis. Elevated BMI was associated with advanced stage of disease (P=.01), lower mammographic density (Pb.0001), lower FGT (Pb.0001), higher BPE (P=.005), and nonpalpable lesions (P=.04). Conclusions: Higher BMI was associated with decreased breast density and FGT. Higher BMI was also associated with advanced stage disease and nonpalpable tumors on clinical exam. © 2016 Elsevier Inc. All rights reserved.
1. Introduction According to the Centers for Disease Control and Prevention, more than one third of adult women are obese, and the rate of obesity significantly increases with age [1]. As of 2013, the American Medical Association officially recognized obesity as a disease, and it is a modifiable risk factor for type-2 diabetes mellitus and cardiovascular disease. In addition, postmenopausal obese women have a 31% increased risk of developing breast cancer [2]. This would suggest that obese women represent a population that would benefit from regular breast cancer screening. Screening mammography provides information regarding the presence or absence of suspicious findings and also provides the referring clinician with information regarding the density of the breast tissue. Mammographic breast density is described as the proportion of glandular tissue to fatty tissue. In clinical practice, it is assessed using a fourcategory score defined in the American College of Radiology's Breast Imaging Reporting and Data System (BI-RADS) [3]. Dense breast tissue on mammography is an independent risk factor for breast cancer which also lowers the sensitivity of the exam [4–7]. According to Boyd et al., extremely dense breast tissue causes a masking effect which increases the odds of detecting breast cancers between screenings [odds ratio (OR)=17.8, comparing ≥75% to b10% dense breast tissue] [4]. Furthermore, the relative risk of breast cancer in women with ≥50% dense ⁎ Corresponding author. New York University School of Medicine, 160 East 34th Street, New York, NY, 10016. Tel.: +1 212 731 5333; fax: +1 212 731 6051. E-mail address: [email protected] (L. Moy). http://dx.doi.org/10.1016/j.clinimag.2016.08.009 0899-7071/© 2016 Elsevier Inc. All rights reserved.
breast parenchyma is 2.5–6-fold higher compared to women with predominantly fatty breasts [7]. At present, 21 states require women to be notified if they are found to have dense breast tissue [8] on routine screening mammography, presumably in order to allow for a discussion regarding the advisability of supplemental screening methods. Although elevated body mass index (BMI) is associated with lower breast density, obese women are at increased risk for postmenopausal breast cancer [9–12]. In addition to regular mammography, magnetic resonance imaging (MRI) is increasingly used as part of the recommended screening for women at higher risk for developing breast cancer. Breast tissue characteristics that are assessed on MRI include background parenchymal enhancement (BPE) and fibroglandular tissue (FGT). BPE represents the enhancement of normal breast tissue after administering intravenous contrast and reflects the vascularity of the FGT. FGT is a threedimensional (3D) representation of fibroglandular tissue on MRI. BPE and, to a lesser extent, FGT are hormonally regulated and decrease with menopause, use of antiestrogen therapies, and history of bilateral salpingo-oophorectomy [13–17]. Despite the association of obesity with an increased risk for breast cancer, higher BMI is associated with decreased breast density on mammography. At present, there is no information regarding the effect of BMI on FGT or BPE as evaluated on MRI. The main purpose of this study was to examine the association between BMI and mammographic breast density, BPE, and FGT in women with breast cancer. Additionally, we examined the association between BMI and clinical characteristics, such as clinical presentation and stage of disease.
1168
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
2. Materials and methods 2.1. Study participants The Breast Cancer Database at our medical center is a longitudinal registry that was established in January 2010. All patients undergoing definitive breast cancer surgery at our institution are eligible to enroll in the Breast Cancer Database. The variables collected include information on personal and family history, screening history, methods of diagnosis, stage at diagnosis, details of treatment, and outcomes. All clinical data were obtained from detailed questionnaires filled out at the time of diagnosis and review of the electronic medical records. This study was approved by the Institutional Review Board and was compliant with the standards of the Health Insurance Portability and Accountability Act. Patients included in this study were enrolled in the Breast Cancer Database between January 2010 and September 2014 and had both mammography and breast MRI prior to surgery. Each patient contributed a single examination to the study. Men were excluded from this study. Demographic information, indication for the examination, and information including mammographic density and BI-RADS assessment were obtained from the electronic medical record. Reader interpretations of BPE, FGT, and mammographic breast density (below) were sent to a data manager who consolidated this information in an Excel spreadsheet. 2.2. Diagnostic imaging 2.2.1. Mammography imaging technique All mammograms were performed with digital technique and were acquired using MAMMOMAT Novation DR software (version V8.3, Siemens Healthcare). Based on routine institutional practice, the images were further analyzed by iCAD computer-aided detection software (iCAD, version VA20E; iCAD, Inc., Nashua, NH, USA). 2.2.2. MRI technique Bilateral dynamic contrast-enhanced breast MRI examinations for premenopausal women are scheduled during the second week (days 8–14) of their menstrual cycle. All breast MRI examinations were performed on commercially available systems at 1.5 T (Avanto, Siemens Medical Solutions) or 3.0 T (TIM Trio, Siemens Medical Solutions) with the patient in prone position using a dedicated surface breast coil (7-channel Breast Biopsy Array, InVivo Research). The standard imaging protocol includes a localizing sequence followed by a sagittal T2weighted sequence (repetition time/echo time, 7220/84), a sagittal T1-weighted non-fat-suppressed 3D fast spoiled gradient-recalled echo sequence (4.01/1.52; flip angle, 12°; matrix, 384×384; field of view, 270 mm; section thickness, 1 mm), followed by the same sagittal T1-weighted fat-suppressed 3D fast spoiled gradient-recalled echo sequence performed before and four times after a rapid bolus injection of 0.1 mmol/L of gadopentetate dimeglumine (Magnevist, Bayer Healthcare Pharmaceuticals) per kilogram of body weight at an injection rate of 2.0 ml/s via an intravenous catheter. Image acquisition began immediately after administration of the contrast material and saline bolus. The first contrast-enhanced dynamic sequence was obtained at approximately 100 s, followed by four additional consecutive sequences (three sagittal followed by one delayed axial). The delayed axial images were obtained so that subtle asymmetric BPE could be appreciated. Postprocessing included subtraction images and maximum intensity projection images. Images were reviewed on high-resolution picture archiving and communication system monitors. 2.3. Image assessment Mammographic breast density was categorized according to the American College of Radiology as entirely fatty, scattered fibroglandular, heterogeneously dense, or extremely dense breasts (Table 1 and Fig. 1)
Table 1 BI-RADS classifications of mammographic breast density, FGT, and BPE BI-RADS
Mammographic breast density
FGT
BPE
a b c
Almost entirely fatty Scattered fibroglandular Heterogeneously dense Extremely dense
Almost entirely fatty Scattered fibroglandular tissue Heterogeneous fibroglandular tissue Extreme fibroglandular tissue
Minimal Mild Moderate
d
Marked
[3]. All mammograms were assessed for breast density by two fellowship-trained breast radiologists in consensus. All breast MRI examinations were assessed for BPE, in consensus, by two fellowship-trained breast radiologists who had up to 12 years of experience in reading breast MRI. Both readers were blinded to mammographic density, clinical data of the patients, and pathology results. The level of global BPE, rather than the highest BPE in a single quadrant, was assessed using a combination of pre- and the first postcontrast T1weighted fat-saturated and subtracted images and was recorded on a four-point scale (a: minimal, b: mild, c: moderate, d: marked) in accordance with latest BI-RADS categories (Table 1 and Fig. 1) [3]. The volume and intensity of enhancement were considered in the global assessment of BPE and categorized on the basis of MR BI-RADS criteria as minimal, mild, moderate, or marked. The volume of breast parenchymal enhancement was estimated qualitatively by reviewing the amount of enhancing FGT on multiple contiguous slices. Furthermore, the amount of FGT was evaluated using the following scale based on American College of Radiology BI-RADS criteria: entirely fatty, scattered fibroglandular, heterogeneously fibroglandular, and extreme fibroglandular tissue (Table 1 and Fig. 1) [3]. In cases of asymmetry of the breasts, the higher level of mammographic density, BPE, and FGT was recorded. 2.4. Statistical analyses Statistical analyses were performed using descriptive statistics, analysis of variance (ANOVA), Pearson's chi-square, and linear and logistic regression. The variables of interest included age, family history of breast cancer, atypical hyperplasia, lobular carcinoma in situ (LCIS), tumor characteristics, palpability, mammographic breast density, BPE, FGT, menopausal status, use of chemoprevention, and screening frequency as defined as the number of mammograms the patients had in the past 6 years. In our analyses, we looked at BMI as a categorical variable: underweight (≤18 kg/m 2), normal weight (18–24 kg/m 2), overweight (25–29 kg/m 2), and obese (≥30 kg/m 2), in accordance with the World Health Organization criteria [18]; as a dichotomous variable (b25 kg/m2 and ≥25 kg/m 2) in accordance with the increased risk of breast cancer demonstrated in overweight and obese postmenopausal women [2,19,20]; and as a continuous variable. All analyses were performed using SAS version 9.3 (SAS Institute Inc., Cary, NC, USA). 3. Results Out of a total of 1991 women enrolled in the Breast Cancer Database at the time of the study, 573 (29%) patients had both a mammogram and a breast MRI prior to their breast cancer surgery. Of these women, the median age was 53 years (22–86 years). The majority of women were Caucasian (73%) and postmenopausal (54%) with a median BMI of 24.9 kg/m 2 (range: 16.8–46.3). The majority of women underwent annual screening mammography (52%) prior to their cancer diagnosis. The majority of breast cancers in this group were detected by mammography (53%). The majority of the patients had stage 0 or stage 1 breast cancer, with ductal carcinoma in situ representing 23% of the total. The median invasive tumor size was 1.4 cm (range 0.01–12.5 cm). The majority of patients were estrogen receptor (ER) positive (84%), progesterone receptor positive (71%), and HER2neu negative (86%) (Table 2). Forty-three3 (8%) of the patients had triple-negative breast cancer. Only
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
1169
Fig. 1. BI-RADS categories of mammographic breast density, FGT, and BPE. Row I: Digital mammography images show different breasts with (A) almost entirely fatty, (B) scattered fibroglandular, (C) heterogeneously dense, and (D) extremely dense breast tissue. Row II: T1-weighted non-fat-suppressed MR images show different breasts with (A) almost entirely fatty, (B) scattered, (C) heterogeneously dense, and (D) extremely dense amounts of FGT. Row III: T1-weighted fat-suppressed contrast-enhanced subtraction MR images show different breasts with (A) minimal, (B) mild, (C) moderate, and (D) marked BPE.
5% of patients were on hormonal therapy for chemoprevention, and 12% of patients had a history of hormone replacement therapy usage prior to being diagnosed with breast cancer. When BMI was treated as a dichotomous variable (BMI b25 kg/m 2 and BMI ≥25 kg/m 2), we found that women with a BMI ≥25 were more likely to be older (Pb.0001) and postmenopausal (Pb.0001) than women with a BMI b25. There was no association between BMI and family history of breast cancer (P=.87), history of atypical hyperplasia (P=.82) or LCIS (P=.76), and tumor histology (P=.88) (Table 2). We also found no association between BMI and invasive tumor size (P= .10). However, there was a statistically significant association between higher BMI and later stage of disease at diagnosis (P=.01) (Table 2). In regard to the relationship of imaging characteristics and BMI, we found that women who had a BMI ≥25 had a lower mammographic breast density (Pb.0001), lower FGT (Pb.0001), and higher BPE (P= .005) (Fig. 2). Patients with a BMI ≥25 were more likely to have predominantly fatty breasts and less likely to have extremely dense breasts [OR=21.3, 95% confidence interval (CI): 7.3–62.1]. A similar inverse relationship was seen between BMI and FGT on MRI (OR=26.8, 95% CI: 12.0–60.0). Women who had a BMI ≥25 were more likely to have moderate (OR=1.72, 95% CI: 1.02–2.90) or mild BPE (OR=2.08, 95% CI: 1.30–3.34) and less likely to have minimal BPE. When BMI was treated as a continuous variable, we found that the same significant associations existed between BMI and stage of breast cancer (P=.002), mammographic breast density (Pb.0001), BPE (P=.02), and FGT (Pb.0001). When BMI was treated as a categorical variable with four categories, there was a significant association between BMI and mammographic breast density (Pb.0001) and FGT (Pb.0001), but no association between BMI and BPE (P=.07). Since breast density, BPE, and FGT typically decrease with age, we performed a linear regression analysis with age as a confounding variable. Controlling for age, we found that lower breast density (Pb.0001), lower FGT (Pb.0001), and higher BPE (Pb.0001) all remained
significantly associated with elevated BMI. Similarly, since studies have shown that BPE and FGT are influenced by hormonal changes, after controlling for menopausal status, we found that the association between BMI and stage of breast cancer (P=.04), mammographic breast density (Pb.0001), FGT (Pb.0001), and BPE (Pb.0001) remained statistically significant. With regard to method of breast cancer presentation and BMI, women who had a higher BMI were more likely to present with nonpalpable cancers (P=.04). We also found no difference in screening behavior in women with higher BMI compared to women with lower BMI (P=.29). We then performed a logistic regression looking at BMI as a dichotomous outcome with the imaging characteristics using age as a confounding variable to confirm our above findings. After controlling for age, we found that lower breast density (Pb.0001), lower FGT (Pb.0001), and higher BPE (P=.002) were associated with having a BMI ≥25. When we controlled for menopausal status, we found that the association between higher BMI and higher stage of breast cancer (P=.01), lower breast density (Pb.0001), lower FGT (Pb.0001), and higher BPE (P= .007) remained significant.
4. Discussion In support of the literature, our study shows that BMI is inversely associated with breast density. Patients with a BMI ≥25 were more likely to have predominantly fatty breast tissue and less likely to have extremely dense breast tissue. Our study shows a similar inverse relationship between BMI and MR-FGT. This further contributes to the theory that mammographic density and FGT are similar in etiology, both reflecting the amount of fibroglandular breast tissue in proportion to the amount of fatty tissue. Prior studies demonstrate mixed results regarding the relationship between BMI and FGT or BPE [17,18]. Given that FGT provides a 3D analysis of fibroglandular tissue, FGT may be a
1170
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
Table 2 Patient characteristics and relationship with BMI* Variables
Total (N=573)
Median age, years
53 (22–86)
Menopausal No Yes Family history of breast cancer Negative Positive Atypical hyperplasia Negative Positive LCIS Negative Positive Histology Ductal carcinoma in situ Invasive ductal carcinoma Invasive lobular carcinoma Other invasive Median tumor size, cm Stage of breast cancer 0 I IIA, IIB IIIA, IIIB, IIIC No residual cancer Estrogen receptor Positive Negative Missing Progesterone receptor Positive Negative Missing Her2-Neu Positive Negative Equivocal NA/Unknown
%
BMI b25 (n=288)
%
50 (22–84)
BMI ≥25 (n=285)
%
57 (27–86)
P value Pb.0001
265 308
46 54
158 130
55 45
107 178
38 62
Pb.0001
426 147
74 26
215 73
75 25
211 74
74 26
P=.87
556 17
97 3
279 9
97 3
277 8
97 3
P=.82
560 13
98 2
282 6
98 2
278 7
98 2
P=.76
135 353 61 24 1.4 (0.01–12.5)
23 62 11 4
65 181 30 12 1.2 (0.04–12.5)
23 63 10 4
70 172 31 12 1.5 (0.01–11.0)
25 60 11 4
P=.88
131 273 128 37 4
23 48 22 6 1
66 152 59 10 1
23 53 21 3 0.4
65 121 69 27 3
23 42 24 10 1
P=.01
473 91 9
84 16 –
238 45 5
84 16 –
235 46 4
84 16 –
P=.88
400 162 11
71 29 –
204 77 7
73 27 –
196 85 4
70 30 –
P=.46
50 373 10 140
12 86 2 –
31 182 5 70
14 84 2 –
19 191 5 70
9 89 2 –
P=.21
P=.10
⁎ Pearson chi-squared analyses.
more objective and quantitative measurement of breast density and may be more useful for breast cancer risk assessment [19]. To our knowledge, this is the largest study to specifically examine the relationship between BMI, FGT, and BPE. We found higher BMI to be associated with elevated BPE, with overweight and obese women more likely to have moderate (type 3) or mild (type 2) BPE. King et al. have described an association between increased BPE and increased risk of developing breast cancer [22], and although this study is not designed to assess breast cancer risk, we found that women with a higher BMI had higher BPE. Elevated BPE can limit the interpretation of MRI examination resulting in greater false-positive rates [20,21]. In postmenopausal women, higher BMI is an important risk factor for developing breast cancer [23]. Interestingly, our study demonstrates an association between higher BMI and elevated BPE despite the finding that these patients were statistically more likely to be postmenopausal and older. BPE represents the vascularity of the FGT and is known to be hormonally sensitive, decreasing with menopause, antiestrogen therapies, and bilateral salpingo-oophorectomy [13–17]. The biological mechanism behind the increased risk in postmenopausal women with increased BMI is not fully understood. Obese and overweight postmenopausal women may maintain relatively higher levels of peripheral estrogen production, which is one theory as to why this patient population is at increased risk for breast cancer. This increased production of estrogen by the adipose tissue becomes the principal site of estrogen biosynthesis in postmenopausal women and may have multiple effects [24–27]. We hypothesize that the increased levels of estrogen may lead to higher BPE in our cohort. Additionally, estrogen enhances cell proliferation and may result in ER-positive tumors diagnosed at a later stage
[22]. Estrogen levels were not measured in this study, and our study found no association between ER-positive disease and BMI. Furthermore, an alternative mechanism describes the associations between obesity, proinflammatory cytokines/adipokines, and macrophage infiltration, promoting tumor development and angiogenesis [2,28,29]. Proinflammatory cytokines were not measured in this study, but monitoring such markers of inflammation should be considered in future research. Obesity in postmenopausal breast cancer has been associated with larger tumor size, advanced stage at diagnosis, and poorer prognosis [10,22,30,31]. In support of the literature, our patients who were overweight and obese were more likely to be diagnosed with stage III breast cancer, an association that continued to be significant after accounting for both age and menopausal status. We also found that breast cancers diagnosed with obese and overweight women were more likely to be nonpalpable with no significant difference in tumor size. It has been argued that obesity is associated with delayed breast cancer diagnosis due to barriers in accessing health care [32–34]. Wee et al. and Ferrante et al. show that obese women have significantly lower compliance with mammography screening, independent of income and other comorbidities [35,36]. Reasons for this potential difference in screening behavior include embarrassment about being weighed or undergoing physical exam and possible negative attitudes toward obese patients. In our study, we found no difference in mammographic screening frequency with BMI, but overall, only 52% of patients were getting annual mammograms. In response to the change in the US Preventive Services Task Force guidelines for breast cancer screening recommending biennial screening
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
1171
a
b
c
Fig. 2. Imaging characteristics and relationship with BMI. Graphs showing relationship between BMI and (a) mammographic breast density, (b) MR FGT, and (c) MR BPE. P values reflect the Pearson's chi-square analyses examining the association between BMI and imaging characteristics.
beginning at age 50 [37], obese and overweight women may benefit more from regular screening with mammography, given their increased risk and mortality of breast cancer as well as the improved sensitivity of mammography with decreasing breast density. Based on our results, annual screening with imaging may be particularly important because obese women were less likely to have cancerous lesions palpable on clinical breast exam (CBE). Obesity and breast size are thought to be associated with decreases in CBE sensitivity [35,36], although this has not been extensively studied and many reports evaluating CBE do not account for BMI or breast size [38–41]. Limitations of this study include its retrospective nature and limited generalizability. Our population represents a majority of Caucasian women (73%) presenting to private academic medical center. Interobserver variability of mammographic breast density was minimized by having both readers assess the study in consensus. We realize that the agreement may be particularly difficult to assess for the two middledensity categories and that automated systems have been developed recently that will provide the absolute volumetric breast density.
However, these systems use different methodologies to quantify breast density and have not been validated in large series of patients. Furthermore, only 29% of these patients met the criteria of having both a mammogram and a breast MRI. Although many women get a breast MRI for being at high risk of breast cancer, only 26% of these patients had a family history of breast cancer. Preoperative breast MRI exams are not routinely performed at our institution; however, recommending a breast MRI exam is dependent on the treating physician. In addition, all patients presenting for surgery of a known breast cancer were consecutively enrolled into the Breast Cancer Database, minimizing selection bias. Lastly, our population of interest consisted of women recently diagnosed with breast cancer. Since we did not compare women with and without breast cancer, we cannot generalize our results to all women. Further prospective studies are necessary to better understand the use of BMI, BPE, and FGT as predictors of disease. In conclusion, we have found that higher BMI is associated with later-stage breast cancers, as well as tumors that are less likely to be
1172
J. Gillman et al. / Clinical Imaging 40 (2016) 1167–1172
palpable on physical exam. Increased BMI is also associated with lower breast density, and therefore, overweight and obese patients would benefit from close monitoring with screening mammography. For obese and overweight women undergoing breast MRI, they are more likely to have lower FGT and elevated BPE. More research is needed to further elucidate the use of BPE and FGT as risk factors for the development of breast cancer. Acknowledgments We would like to acknowledge our institution's Clinical and Translational Science Institute for their mentorship, guidance, and support. References [1] Ogden CL, Carroll MD, Kit BK, Flegal KM. Prevalence of obesity in the United States, 2009–2010. NCHS Data Brief 2012;82:1–7. [2] Carmichael AR. Obesity as a risk factor for development and poor prognosis of breast cancer. BJOG 2006;113(10):1160–6. [3] D'orsi C, Sickles E, Mendelson E, Morris E. ACR BI-RADS® atlas, breast imaging reporting and data system. 5th ed. American College of Radiology. American College of. Radiology: Reston, VA; 2013. [4] Boyd NF, Martin LJ, Yaffe MJ, Minkin S. Mammographic density and breast cancer risk: current understanding and future prospects. Breast Cancer Res 2011;13(6):223. [5] 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(11):2086–92. [6] Kerlikowske K. The mammogram that cried Wolfe—NEJM. N Engl J Med 2007; 356(3):297–300. [7] McCormack VA, dos Santos Silva I. Breast density and parenchymal patterns as markers of breast cancer risk: a meta-analysis. Cancer Epidemiol Biomarkers Prev 2006;15(6):1159–69. [8] Two more states require breast density reporting—American College of Radiology [internet]. [cited 2015 Feb 3]. Available from: http://www.acr.org/Advocacy/ eNews/20150123-Issue/Two-More-States-Require-Breast-Density-Reporting. [9] Baglietto L, Krishnan K, Stone J, Apicella C, Southey MC, English DR, et al. Associations of mammographic dense and nondense areas and body mass index with risk of breast cancer. Am J Epidemiol 2014;179(4):475–83. [10] Gierach GL, Ichikawa L, Kerlikowske K, Brinton LA, Farhat GN, Vacek PM, et al. Relationship between mammographic density and breast cancer death in the breast cancer surveillance consortium. J Natl Cancer Inst 2012;104(16):1218–27. [11] Razzaghi H, Troester MA, Gierach GL, Olshan AF, Yankaskas BC, Millikan RC. Mammographic density and breast cancer risk in white and African American women. Breast Cancer Res Treat 2012;135(2):571–80. [12] Harris HR, Tamimi RM, Willett WC, Hankinson SE, Michels KB. Body size across the life course, mammographic density, and risk of breast cancer. Am J Epidemiol 2011; 174(8):909–18. [13] Amarosa AR, McKellop J, Klautau Leite AP, Moccaldi M, Clendenen TV, Babb JS, et al. Evaluation of the kinetic properties of background parenchymal enhancement throughout the phases of the menstrual cycle. Radiology 2013;268(2):356–65. [14] King V, Gu Y, Kaplan JB, Brooks JD, Pike MC, Morris EA. Impact of menopausal status on background parenchymal enhancement and fibroglandular tissue on breast MRI. Eur Radiol 2012;22(12):2641–7. [15] King V, Kaplan J, Pike MC, Liberman L, David Dershaw D, Lee CH, et al. Impact of tamoxifen on amount of fibroglandular tissue, background parenchymal enhancement, and cysts on breast magnetic resonance imaging. Breast J 2012;18(6):527–34. [16] King V, Goldfarb SB, Brooks JD, Sung JS, Nulsen BF, Jozefara JE, et al. Effect of aromatase inhibitors on background parenchymal enhancement and amount of fibroglandular tissue at breast MR imaging. Radiology 2012;264(3):670–8. [17] Price ER, Brooks JD, Watson EJ, Brennan SB, Comen EA, Morris EA. The impact of bilateral salpingo-oophorectomy on breast MRI background parenchymal enhancement and fibroglandular tissue. Eur Radiol 2014;24(1):162–8. [18] Physical status: the use and interpretation of anthropometry Report of a WHO expert committeeWorld Health Organ Tech Rep Ser 1995;854:1–452.
[19] Reeves GK, Pirie K, Beral V, Green J, Spencer E, Bull D. Cancer incidence and mortality in relation to body mass index in the million women study: cohort study. BMJ 2007; 335(7630):1134. [20] Van den Brandt PA, Spiegelman D, Yaun SS, Adami HO, Beeson L, Folsom AR, et al. Pooled analysis of prospective cohort studies on height, weight, and breast cancer risk. Am J Epidemiol 2000;152(6):514–27. [21] Chun J, Refinetti AP, Klautau Leite A, Schnabel F, Hochman T, Moy L. The relationship of breast density, BMI, and menopausal status in mammography and MRI. J Clin Oncol 2012;30(Suppl. 27) [abstr 36]. [22] King V, Brooks JD, Bernstein JL, Reiner AS, Pike MC, Morris EA. Background parenchymal enhancement at breast MR imaging and breast cancer risk. Radiology 2011; 260(1):50–60. [23] Research WCRFaAIfC. Food, nutrition, physical activity, and the prevention of cancer: a global perspective. Washington DC: AICR; 2007. [24] Savolainen-Peltonen H, Vihma V, Leidenius M, Wang F, Turpeinen U, Hamalainen E, et al. Breast adipose tissue estrogen metabolism in postmenopausal women with or without breast cancer. J Clin Endocrinol Metab 2014;99(12):E2661–7. http://dx.doi. org/10.1210/jc.2014-2550. [25] Key TJ, Appleby PN, Reeves GK, Roddam A, Dorgan JF, Longcope C, et al. Endogenous hormones breast cancer collaborative G. Body mass index, serum sex hormones, and breast cancer risk in postmenopausal women. J Natl Cancer Inst 2003;95(16): 1218–26. [26] Rinaldi S, Key TJ, Peeters PH, Lahmann PH, Lukanova A, Dossus L, et al. Anthropometric measures, endogenous sex steroids and breast cancer risk in postmenopausal women: a study within the EPIC cohort. Int J Cancer 2006;118(11):2832–9. http:// dx.doi.org/10.1002/ijc.21730. [27] Nieman KM, Romero IL, Van Houten B, Lengyel E. Adipose tissue and adipocytes support tumorigenesis and metastasis. Biochim Biophys Acta 2013;1831(10):1533–41. http://dx.doi.org/10.1016/j.bbalip.2013.02.010. [28] Giess CS, Yeh ED, Raza S, Birdwell RL. Background parenchymal enhancement at breast MR imaging: normal patterns, diagnostic challenges, and potential for false-positive and false-negative interpretation. RadioGraphics 2014;34(1): 234–47. [29] DeMartini WB, Liu F, Peacock S, Eby PR, Gutierrez RL, Lehman CD. Background parenchymal enhancement on breast MRI: impact on diagnostic performance. AJR Am J Roentgenol 2012;198(4):W373–80. [30] Eichholzer M, Huang DJ, Modlasiak A, Schmid SM, Schötzau A, Rohrmann S, et al. Impact of body mass index on prognostically relevant breast cancer tumor characteristics. Breast care (Basel), 8(3). Karger Publishers; 2013. p. 192–8. [31] Khan S, Shukla S, Sinha S, Meeran SM. Role of adipokines and cytokines in obesityassociated breast cancer: therapeutic targets. Cytokine Growth Factor Rev 2013; 24(6):503–13. [32] Sun X, Casbas-Hernandez P, Bigelow C, Makowski L, Joseph Jerry D, Smith Schneider S, et al. Normal breast tissue of obese women is enriched for macrophage markers and macrophage-associated gene expression. Breast Cancer Res Treat 2012; 131(3):1003–12. [33] Majed B, Moreau T, Senouci K, Salmon RJ, Fourquet A, Asselain B. Is obesity an independent prognosis factor in woman breast cancer? Breast Cancer Res Treat 2008; 111(2):329–42. [34] Niraula S, Ocana A, Ennis M, Goodwin PJ. Body size and breast cancer prognosis in relation to hormone receptor and menopausal status: a meta-analysis. Breast Cancer Res Treat 2012;134(2):769–81. [35] Wee CC, McCarthy EP, Davis RB, Phillips RS. Screening for cervical and breast cancer: is obesity an unrecognized barrier to preventive care? Ann Intern Med 2000; 132(9):697–704. [36] Ferrante JM, Chen P-H, Crabtree BF, Wartenberg D. Cancer screening in women: body mass index and adherence to physician recommendations. Am J Prev Med 2007;32(6):525–31. [37] Recommendation summary—US Preventive Services Task Force [internet]. [cited 2015 Feb 3]. Available from: http://www.uspreventiveservicestaskforce.org/uspstf/ uspsbrca.htm. [38] Barton MB, Harris R, Fletcher SW. Does this patient have breast cancer? JAMA 1999; 282(13):1270. [39] McDonald S, Saslow D, Alciati MH. Performance and reporting of clinical breast examination: a review of the literature. CA Cancer J Clin 2004;54(6):345–61. [40] Chagpar AB, McMasters KM, Saul J, Nurko J, Martin RCG, Scoggins CR, et al. Body mass index influences palpability but not stage of breast cancer at diagnosis. Am Surg 2007;73(6):555–60 [discussion 560]. [41] Bobo JK, Lawson HW, Lee NC. Risk factors for failure to detect a cancer during clinical breast examinations (United States). Cancer Causes Control 2003;14(5):461–8.