Article

Physical activity, body mass index, and mammographic density in postmenopausal breast cancer survivors

Yale University, New Haven, Connecticut, United States
Journal of Clinical Oncology (Impact Factor: 18.43). 04/2007; 25(9):1061-6. DOI: 10.1200/JCO.2006.07.3965
Source: PubMed

ABSTRACT To investigate the association between physical activity, body mass index (BMI), and mammographic density in a racially/ethnically diverse population-based sample of 522 postmenopausal women diagnosed with stage 0-IIIA breast cancer and enrolled in the Health, Eating, Activity, and Lifestyle Study.
We collected information on BMI and physical activity during a clinic visit 2 to 3 years after diagnosis. Weight and height were measured in a standard manner. Using an interview-administered questionnaire, participants recalled the type, duration, and frequency of physical activities they had performed in the last year. We estimated dense area and percentage density as a continuous measure using a computer-assisted software program from mammograms imaged approximately 1 to 2 years after diagnosis. Analysis of covariance methods were used to obtain mean density across WHO BMI categories and physical activity tertiles adjusted for confounders.
We observed a statistically significant decline in percentage density (P for trend = .0001), and mammographic dense area (P for trend = .0052), with increasing level of BMI adjusted for potential covariates. We observed a statistically significant decline in mammographic dense area (P for trend = .036) with increasing level of sports/recreational physical activity in women with a BMI of at least 30 kg/m2. Conversely, in women with a BMI less than 25 kg/m2, we observed a non-statistically significant increase in mammographic dense area and percentage density with increasing level of sports/recreational physical activity.
Increasing physical activity among obese postmenopausal breast cancer survivors may be a reasonable intervention approach to reduce mammographic density.

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    • "The index mammogram was required to have FMMP codes used to define the density phenotype, be a screening (93% of the mammograms) or diagnostic (7% of the mammograms) study, and be a part of FMMP medical examinations. The woman was required to be 40–80 years old at the time of the index mammogram and to have a BMIo35 at the time of the mammogram, to exclude a few morbidly obese women, as BMIX35 has been consistently reported to be associated with lower density (Sala et al, 1999; Gapstur et al, 2003; Titus-Ernstoff et al, 2006; Irwin et al, 2007). As the result, we excluded 24 women with high density and 79 women with low density. "
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    ABSTRACT: We investigated associations of known breast cancer risk factors with breast density, a well-established and very strong predictor of breast cancer risk. This nested case-control study included breast cancer-free women, 265 with high and 860 with low breast density. Women were required to be 40-80 years old and should have a body mass index (BMI) <35 at the time of the index mammogram. Information on covariates was obtained from annual questionnaires. In the overall analysis, breast density was inversely associated with BMI at mammogram (P for trend<0.001), and parity (P for trend=0.02) and positively associated with alcohol consumption (ever vs never: odds ratio 2.0, 95% confidence interval 1.4-2.8). Alcohol consumption was positively associated with density, and the association was stronger in women with a family history of breast cancer (P<0.001) and in women with hormone replacement therapy (HRT) history (P<0.001). Parity was inversely associated with density in all subsets, except premenopausal women and women without a family history. The association of parity with density was stronger in women with HRT history (P<0.001). The associations of alcohol and parity with breast density appear to be in reverse direction, but stronger in women with a family history of breast cancer and women who ever used HRT.
    British Journal of Cancer 02/2012; 106(5):996-1003. DOI:10.1038/bjc.2012.1 · 4.82 Impact Factor
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    • "Most of the image processing techniques are implemented on the whole mammogram without taking into consideration that mammograms have different density patterns and that anatomical regions are used by radiologists in the interpretation [16]. The medical community has realized breast tissue density as an important risk indicator for the growth of breast cancer [17] [18] [19] [20] [21]. Wolfe has noticed that the risk for breast cancer growth is determined by mammography parenchymal patterns [22], and it has also been confirmed by other researchers, such as Boyd et al. [23], van Gils et al. [24] and Karssemeijer [25]. "
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    ABSTRACT: The focus of this paper is to review approaches for segmentation of breast regions in mammograms according to breast density. Studies based on density have been undertaken because of the relationship between breast cancer and density. Breast cancer usually occurs in the fibroglandular area of breast tissue, which appears bright on mammograms and is described as breast density. Most of the studies are focused on the classification methods for glandular tissue detection. Others highlighted on the segmentation methods for fibroglandular tissue, while few researchers performed segmentation of the breast anatomical regions based on density. There have also been works on the segmentation of other specific parts of breast regions such as either detection of nipple position, skin-air interface or pectoral muscles. The problems on the evaluation performance of the segmentation results in relation to ground truth are also discussed in this paper.
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    • "It can be used to identify women at increased risk who could benefit from more frequent screening or the use of alternative imaging and diagnostic methods (van Gils et al 1999). Also, MBD is one of the strongest risk factors that can be modified through adjustment of other factors, such as diet (Tseng et al 2007, Takata et al 2007) and physical activity (Irwin et al 2007). A causal link between MBD and breast cancer remains to be demonstrated. "
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    ABSTRACT: The purpose of this study was to evaluate the performance of an algorithm used to measure the volumetric breast density (VBD) from digital mammograms. The algorithm is based on the calibration of the detector signal versus the thickness and composition of breast-equivalent phantoms. The baseline error in the density from the algorithm was found to be 1.25 +/- 2.3% VBD units (PVBD) when tested against a set of calibration phantoms, of thicknesses 3-8 cm, with compositions equivalent to fibroglandular content (breast density) between 0% and 100% and under x-ray beams between 26 kVp and 32 kVp with a Rh/Rh anode/filter. The algorithm was also tested against images from a dedicated breast computed tomography (CT) scanner acquired on 26 volunteers. The CT images were segmented into regions representing adipose, fibroglandular and skin tissues, and then deformed using a finite-element algorithm to simulate the effects of compression in mammography. The mean volume, VBD and thickness of the compressed breast for these deformed images were respectively 558 cm(3), 23.6% and 62 mm. The displaced CT images were then used to generate simulated digital mammograms, considering the effects of the polychromatic x-ray spectrum, the primary and scattered energy transmitted through the breast, the anti-scatter grid and the detector efficiency. The simulated mammograms were analyzed with the VBD algorithm and compared with the deformed CT volumes. With the Rh/Rh anode filter, the root mean square difference between the VBD from CT and from the algorithm was 2.6 PVBD, and a linear regression between the two gave a slope of 0.992 with an intercept of -1.4 PVBD and a correlation with R(2) = 0.963. The results with the Mo/Mo and Mo/Rh anode/filter were similar.
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