Chikushima  Norimasa

Hasegawa  Nao

Homma  Noriyasu

Ichiji  Kei

Ohashi  Yuji

Osanai  Makoto

Tamura  Atsushi

Watanabe  Atsutoshi

Zhang Xiaoyong

English

紀要類 (bulletin)departmental bulletin pape

Zhang, Xiaoyong

Chikushima, Norimasa

Watanabe, Atsutoshi

Ohashi, Yuji

Hasegawa, Nao

Ichiji, Kei

Tamura, Atsushi

Osanai, Makoto

Homma, Noriyasu

Tohoku University Repository (TOUR) / 東北大学機関リポジトリ

Is Mammographic Breast Density a Risk Factorfor Breast Cancer in Japanese Women ?著者 Zhang  Xiaoyong, Chikushima  Norimasa,Watanabe  Atsutoshi, Ohashi  Yuji, Hasegawa Nao, Ichiji  Kei, Tamura  Atsushi, Osanai Makoto, Homma  Noriyasujournal orpublication title東北大学医学部保健学科紀要volume 24number 1page range 45-51year 2015-01-31URL http://hdl.handle.net/10097/5964045─　　─Is Mammographic Breast Density a Risk Factor for Breast Cancer in Japanese Women ?東北大医保健学科紀要　24（1） : 45～51，2015Original PaperIs Mammographic Breast Density a Risk Factor for Breast Cancer  in Japanese Women ?Xiaoyong Zhang1, Norimasa Chikushima1, Atsutoshi Watanabe1, Yuji Ohashi2,  Nao Hasegawa3, Kei Ichiji4, Atsushi Tamura1, Makoto Osanai1  and Noriyasu Homma11Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine2Department of Electrical Engineering, Graduate School of Engineering, Tohoku University3Department of Biomedical Engineering, Graduate School of Biomedical Engineering, Tohoku University4Research Institute of Electrical Communication, Tohoku University乳房密度は日本人女性でも乳がん罹患危険因子か張 　 暁 勇1，筑 島 徳 政1，渡 邉 篤 俊1，大 橋 悠 二2，長谷川奈保3， 市 地 　 慶4，田 村 篤 史1，小 山 内 実1，本 間 経 康11東北大学大学院医学系研究科　医用画像工学分野 2東北大学大学院工学研究科　電気エネルギーシステム専攻 3東北大学大学院医工学研究科　医工学専攻 4東北大学電気通信研究所Key words : Breast cancer, risk factor, mammogram, mammographic breast density  To analyze the association between mammographic breast density and breast cancer risk, a graphic user in-terface (GUI) software was developed in this study. Given a mammogram image, the GUI software is capable of segmenting fibroglandular tissues from the breast region and of calculating the mammographic breast density automatically. Using this software, a preliminary case-control study based on a Japanese women mammogra-phy database is conducted to analyze the breast cancer risk associated with the mammographic breast density.　The study demonstrated that the mammographic breast density is strongly associated with the breast cancer for Japanese women.1. Introduction The radiographic appearance of female breast var-ies among individuals because of differences in the relative amounts of fat, fibroglandular tissue, and the different X-ray attenuation characteristics1). In a mammogram image, the fat appears dark due to its radiolucent characteristic, and the fibroglandular tis-sue is radiodense and relatively bright. Pioneering studies by Wolfe2,3) have shown that the relative amount of fibroglandular tissues in the breast as as-sessed mammographically, also referred to as mam-mographic breast density, is an independent risk fac-tor for breast cancer, in fact the most significant after 46─　　─Xiaoyong Zhang・norimasa Chikushima・et alage1). To assess the mammographic breast density, the most commonly used method is to categorize the mammogram images into different categories based on radiologist visual judgment. As shown in Fig. 1, American College of Radiology (ACR) established a four-category mammographic breast density in the breast-imaging reporting and data system (BIRADS)4).　Over the past three decades, more than 40 stud-ies5-10) have been devoted to analyze the relationship between the mammographic breast density and breast cancer risk. These studies have demon-strated that the mammographic breast density is strongly associated with the breast cancer. Howev-er, they are almost based on the database obtained from American and European women. To our knowledge, no publication has been reported that the mammographic breast density in Japanese wom-en also strongly associated with the breast cancer.　In this study, we aim to conduct a preliminary inves-tigation on a mammogram database acquired from Japanese women to conform this hypothesis.2. Method and Materials2.1 Data Set The data sets observed in this study were ob-tained from a digital mammogram database provided by Tohoku University Hospital. Using the medical records of this database, we designed a case-control study to analyze the association between the mam-mographic breast density and risk of breast cancer.　We selected 316 medio-lateral oblique (MLO) mam-mogram images in one-year observation, in which breast cancer or its typical signs (microcalcification cluster, mass, and architecture distortion) are veri-fied by radiologists, as case subjects. As control subjects, 268 MLO mammograms, in which no breast cancer was developed in a similar period of observation, were selected in the study. The original mammographic images are acquired at a resolution 0.025 mm/pixel using 12-bit gray-level depth. For computational efficiency, the origi-nal images are converted into 8-bit gray-level with resolution 0.1 mm/pixel.2.2 Quantitative Breast Density Assessment For quantitative breast density assessment, a Graphic Use Interface (GUI) program using MAT-LAB was developed. The GUI program employs digital image processing techniques that allow radi-ologists to easily segment an observed mammogram image into breast region, pectoral muscle, and dense tissue, and to calculate the mammographic density percentage. The GUI program also allows radiolo-gists to adjust parameters manually for improving (a)　　　　　　　　(b)　　　　　　　　(c)　　　　　　　　(d)Fig. 1.　 Four categories of mammographic breast density in ACR-BIRADS. (a) Category I : the breasts are al-most entirely fatty, and fibro glandular tissue is less than 25%. (b) Category II : there are scattered fi-broglandular tissues, ranging from 25%-50% of the breast. (c) Category III : The breasts are heteroge-neously dense, and fibroglandular tissue ranges 51%-75% of the breast tissue. (d) The breasts are extremely dense and contains greater than 75% fibroglandular tissue.47─　　─Is Mammographic Breast Density a Risk Factor for Breast Cancer in Japanese Women ?the accuracy of the image segmentation. Fig. 2 shows a screenshot of the GUI for estimation of the mammographic density. Here, we briefly introduce the computing proce-dure of the mammographic density estimation sys-tem.  As shown in Fig. 3, the developed system consists of three major procedures. Given an ob-served mammogram image, the first procedure is to utilize an automatic algorithm to convert a gray-lev-el mammogram image into a binary image and to re-move the image labels form the binary image.　Fig. 4 (a) shows an observed mammogram images in which the image labels are located at the top-right of image. Fig. 4 (b) shows the processed image in which the breast region is segmented from the origi-nal image, and the image labels are removed from the original image. The second procedure is to segment the pectoral muscle and the dense tissues from the breast region.　In this procedure, a semi-automatic processing is used to delineate and remove the pectoral muscle manually from the breast region. Subsequently, the dense fibroglandular tissue can be separated from the remainder breast region by using a user-defined thresholding processing. Fig. 4 (c) shows a binary image in which the pectoral muscle is removed man-ually by using the GUI program. As shown in Fig. 4 (d), the dense fibroglandular tissue is seg-mented from the breast region and converted into binary image.Fig. 2.　 A screenshot of the GUI program for mammo-graphic density estimation. Fig. 3.　 A flowchart of GUI program for mammographic density estimation.(a)　　　　　　　　(b)　　　　　　　　(c)　　　　　　　　(d)Fig. 4.　 An example of image segmentation for mammographic breast density estimation. (a) An original mam-mogram image. (b) Image labels are removed from the original image. (c) The breast region in which the pectoral muscle is removed by a manual delineation. (d) The fibroglandular tissue segmented by a semi-automatic thresholding processing.48─　　─Xiaoyong Zhang・norimasa Chikushima・et al Finally, the mammographic breast density is cal-culated as a ratio of the dense tissues area to the breast area, given by　　　　　　　 d= X BXT#100%  (1)where XT is the area of dense tissues corresponding to Fig. 4 (d), and X B is the breast area obtained from image in Fig. 4 (c).3. Results Using the above described GUI program, quantita-tive assessments of mammographic breast density were conducted on the case-control subjects.　Fig. 5 shows four mammographic images in the observed data and the corresponding dense fibro glandular segmented by the GUI program. The mammo-graphic breast densities (from left to right) were eventually calculated as 17%, 48%, 66%, and 85%, respectively. According to the density category of BI-RADS, the observed mammogram images, in-cluding the case and control subjects, were then classified into four categories. Base on the four-categorized results, a relative incidence rate of breast cancer for each category was calculated by　　　　  (2)where i! 1, 2, 3, 4F I is the category index, Ncase and Ncontrol are the total numbers of case and control objects, respectively, and Ncasei  and Ncontroli  are the numbers of case and control objects in the i-th cate-gory. Table 1 summarizes the density categories in the observed subjects. From the results, we note that the relative incidence rate of category IV (high-est density) is more than twice of that of the catego-ry I (lowest density). In addition, according the quantitative density as-sessment, we classified the observed mammogram image into 10 density categories, and calculated the relative incidence rate of breast cancer for each cat-egory. Table 2 summarizes the 10-density catego-Ri=NcaseNcasei + NcontrolNcontroliNcaseNcasei#100%Fig. 5.　 Four mammographic images in the observed data and the corresponding dense fibroglandular segmented by the GUI program.49─　　─Is Mammographic Breast Density a Risk Factor for Breast Cancer in Japanese Women ?ries in the observed subjects. Since the maximum mammographic breast density in the observed imag-es was about 85%, the number of cases in the cate-gory X was zero. In this table, the relative inci-dence rates of category VIII and IX (highest density) was more than three times of that of the category I (lowest density). Figs. 6 (a) and 5 (b) plot the rela-tive incidence rates of breast cancer as the function of two different categories summarized in Tables 1 and 2. The results demonstrated that the mammo-graphic density can be a strong risk factor for breast cancer.4. Discussion In this study, we developed a GUI program that al-lows radiologists to measure the mammographic breast density quantitatively and retrospectively.　Compared with qualitative density categorization based on visual observation, the developed program can help radiologists to make a precision estimation Table 1.　 Four-category mammographic breast density classification and relative incidence rate of breast cancerCategory Control Objects Case ObjectsRi (%)No. Density RMLO LMLO RMLO LMLOI ( 0 ≤ d< 25%) 129 142 79 79  41.3II (25 ≤ d< 50%)  20  16 43 42  71.2III (50 ≤ d< 75%)   6   3 11 10  67.8IV (75 ≤ d<100%)   0   0  2  2 100Total 316 268Table 2.　 Ten-category mammographic breast density classification and relative incidence rate of breast cancerCategory Control Objects Case ObjectsRi (%)No. Density RMLO LMLO RMLO LMLOI ( 0 ≤ d< 10%) 59 58 19 23  29.7II (10 ≤ d< 20%) 68 49 48 37  46.1III (20 ≤ d< 30%) 23 32 24 34  55.4IV (30 ≤ d< 40%)  5  6 15 18  78.0V (40 ≤ d< 50%)  3  4 15 10  80.8VI (50 ≤ d< 60%)  3  2  6  7  73.9VII (60 ≤ d< 70%)  0  4  4  2  70.2VIII (70 ≤ d< 80%)  0  0  2  3 100IX (80 ≤ d< 90%)  0  0  1  1 100X (90 ≤ d<100%)  0  0  0  0 ─Total 316 26850─　　─Xiaoyong Zhang・norimasa Chikushima・et alof the mammographic breast density and to measure the cancer risk in terms of the mammographic breast density. Although a completely automatic density estimator is desirable, observer intervention is still indispensable since the appearance of mam-mogram image would be variable due to several fac-tors, such as irradiation condition, thickness of the breast, etc. Therefore, in our future work, we plan to improve the accuracy and precision of the estima-tion system by combining the digital image process-ing with pattern recognition technology. In this study, we also conducted a preliminary study to analyze the relationship between the mam-mographic breast density and breast cancer risk. A case-control study based on Japanese women dem-onstrated that the mammographic breast density is strongly associated with risk of breast cancer. This result is in agreement with previous studies that are all based on American or European women. How-ever, since the number of cohort used in this study is insufficient, the selections of case and control ob-jects probably introduce a selection bias. Due to this limitation, our study may not be sufficient to prove if the ethnic group is associated with the mammographic breast density, but it can suggest if the result agrees with those for American or Euro-pean women. In this study, we used a relative inci-dence rates for different mammographic breast den-sities to analyze the relationship between the mammographic breast density and breast cancer.　However, since the incidence rate is not based on a population study, the incidence rate obtained from the observation probably introduces estimation bias. A cohort collection is thus the most critical task to our further study.5. Conclusions An analysis of breast cancer risk factor based on quantitatively mammographic breast density estima-tion is presented in this paper. By utilizing digital image processing techniques, we developed a GUI software which is capable of calculating the mammo-graphic breast density. On the base of a cohort ob-tained from Japanese women, a preliminary case-contro l  s tudy  is  conducted  to  ana lyze  the relationship between mammographic breast density and breast cancer. The results suggested that mammographic breast density is strongly associated with breast cancer.Acknowledgments We would like to thank Prof. Tadashi Ishibashi, Dr. Yusuke Kawasumi, and Yumi Takane at Department of Clinical Imaging, Tohoku University Graduate School of Medicine for the providing the mammo-gram images and diagnosis results used in this study. A part of this study was supported by JST Reconstruction Promotion Program (Matching Pro-Fig. 6.　 Relative incidence of breast cancer as a function of breast density category. (a) Four density categories. (b) Ten density categories.51─　　─Is Mammographic Breast Density a Risk Factor for Breast Cancer in Japanese Women ?motion) H25 Sen I-401 and JSPS KAKENHI Grant Number 26540112.References 1) Boyd, N.F., et al. : Quantitative classification of mam-mographic densities and breast cancer risk : results from the Canadian National Breast Screening Study, J. Natl. Cancer Inst., 87, 670-675, 1995 2) Wolfe, N. : Breast patterns as an index of risk for de-veloping breast cancer, AJR, Am. J. Roentgenol., 126, 1130-1137, 1976 3) Wolfe, N. : Risk for breast cancer development deter-mined by mammographic parenchymal pattern, Cancer, 37, 2486-2492, 1976 4) D’Orsi, C.J., et al. : Breast imaging reporting and data system : ACR-BIRADS, in Mammography, 4th ed., American College of Radiology, Reston, 2003 5) Ursin, G., et al. : Mammographic density and breast cancer in three ethnic groups, Cancer Epidemiol. Bio-markers Prev., 12, 332-338, 2003 6) Maskarinec, G., et al. : A case-control study of mam-mographic densities in Hawaii, Breast Cancer Res. Treat., 63, 153-161, 2000 7) Van Gils, C.H., et al. : Mammographic breast density and risk of breast cancer : masking bias or causality ?, Eur. J. Epidemiol., 14, 315-320, 1998 8) Saftlas, A.F., et al. : Mammographic densities and risk of breast cancer, Cancer, 67, 2833-2838, 1991 9) Li, T., et al. : The association of measured breast tis-sue characteristics with mammographic density and other risk factors for breast cancer, Cancer Epidemiol. Biomarkers Prev., 14, 343-349, 200510) Saftlaset, A., et al. : Mammographic parenchymal pat-terns as indicators of breast cancer risk, Am. J. Epide-miol., 129, 518-526, 1989

Is Mammographic Breast Density a Risk Factor for Breast Cancer in Japanese Women ?

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乳房密度は日本人女性でも乳がん罹患危険因子か

乳房密度は日本人女性でも乳がん罹患危険因子か

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