Displaying publications 1 - 20 of 27 in total

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  1. Zulfiqar M, Rohazly I, Rahmah M
    Biomed Imaging Interv J, 2011 Apr;7(2):e14.
    PMID: 22291859 MyJurnal DOI: 10.2349/biij.7.2.e14
    PURPOSE: TO DETERMINE: (i) the mammographic parenchymal patterns in Malaysian women and whether the breasts are dense on mammogram; (ii) the effect of age on breast density; (iii) the effect of parity on breast density; (iv) the difference in breast parenchymal patterns among the major races of women in Malaysia.
    METHODS: This was a descriptive cross-sectional study of 1,784 patients (981 Malays, 571 Chinese, 214 Indians and 18 others) who had undergone mammography during the 1-year study period. Majority of women (41.7%) were aged between 51 and 60 years and majority (43%) had 3-4 children. The Tabar classification (Pattern I - V) was used to evaluate breast parenchymal patterns on mammogram. Tabar Pattern I was further divided into 3 sub-groups (Pattern IA, IB, and IC). The different patterns were then grouped into dense (IB, IC, IV, V) and not dense (IA, II, III) breasts. The SPSS package was used for statistical analysis.
    RESULTS: Majority (59%) of Malaysian women had dense breasts (Pattern IB 29%, IC 20%, IV 5%, and V 5%) and 41% did not have dense breasts (Pattern IA 28%, II 6%, and III 7%). Age and parity were inversely related to breast density (p < 0.0001). Chinese women (65.7%) had the highest percentage of dense breasts (p = 0.69, odds ratio = 1.22), followed by the Indians (57.2%) and the Malays (50.5%).
    CONCLUSION: Majority of women had dense breasts but Pattern IV, which has been associated with increased risk of breast cancer, was seen in only 5% of the women. The breast density reduced steadily with increasing age and parity. There was no statistically significant difference in breast density in the three main races.
    KEYWORDS: Mammography; breast density; breast parenchymal patterns
    Matched MeSH terms: Breast Density
  2. Ye Z, Nguyen TL, Dite GS, MacInnis RJ, Schmidt DF, Makalic E, et al.
    Breast Cancer Res, 2023 Oct 25;25(1):127.
    PMID: 37880807 DOI: 10.1186/s13058-023-01733-1
    BACKGROUND: Mammogram risk scores based on texture and density defined by different brightness thresholds are associated with breast cancer risk differently and could reveal distinct information about breast cancer risk. We aimed to investigate causal relationships between these intercorrelated mammogram risk scores to determine their relevance to breast cancer aetiology.

    METHODS: We used digitised mammograms for 371 monozygotic twin pairs, aged 40-70 years without a prior diagnosis of breast cancer at the time of mammography, from the Australian Mammographic Density Twins and Sisters Study. We generated normalised, age-adjusted, and standardised risk scores based on textures using the Cirrus algorithm and on three spatially independent dense areas defined by increasing brightness threshold: light areas, bright areas, and brightest areas. Causal inference was made using the Inference about Causation from Examination of FAmilial CONfounding (ICE FALCON) method.

    RESULTS: The mammogram risk scores were correlated within twin pairs and with each other (r = 0.22-0.81; all P breast cancer. For example our findings are consistent with the brightest areas being more aetiologically important than lighter areas for screen-detected breast cancer; conversely, light areas being more aetiologically important for interval breast cancer. Additionally, specific textural features capture aetiologically independent breast cancer risk information from dense areas. These findings highlight the utility of ICE FALCON and family data in decomposing the associations between intercorrelated disease biomarkers into distinct biological pathways.

    Matched MeSH terms: Breast Density
  3. Wahab AA, Salim MI, Ahamat MA, Manaf NA, Yunus J, Lai KW
    Med Biol Eng Comput, 2016 Sep;54(9):1363-73.
    PMID: 26463520 DOI: 10.1007/s11517-015-1403-7
    Breast cancer is the most common cancer among women globally, and the number of young women diagnosed with this disease is gradually increasing over the years. Mammography is the current gold-standard technique although it is known to be less sensitive in detecting tumors in woman with dense breast tissue. Detecting an early-stage tumor in young women is very crucial for better survival chance and treatment. The thermography technique has the capability to provide an additional functional information on physiological changes to mammography by describing thermal and vascular properties of the tissues. Studies on breast thermography have been carried out to improve the accuracy level of the thermography technique in various perspectives. However, the limitation of gathering women affected by cancer in different age groups had necessitated this comprehensive study which is aimed to investigate the effect of different density levels on the surface temperature distribution profile of the breast models. These models, namely extremely dense (ED), heterogeneously dense (HD), scattered fibroglandular (SF), and predominantly fatty (PF), with embedded tumors were developed using the finite element method. A conventional Pennes' bioheat model was used to perform the numerical simulation on different case studies, and the results obtained were then compared using a hypothesis statistical analysis method to the reference breast model developed previously. The results obtained show that ED, SF, and PF breast models had significant mean differences in surface temperature profile with a p value <0.025, while HD breast model data pair agreed with the null hypothesis formulated due to the comparable tissue composition percentage to the reference model. The findings suggested that various breast density levels should be considered as a contributing factor to the surface thermal distribution profile alteration in both breast cancer detection and analysis when using the thermography technique.
    Matched MeSH terms: Breast Density*
  4. Tan M, Mariapun S, Yip CH, Ng KH, Teo SH
    Phys Med Biol, 2019 01 31;64(3):035016.
    PMID: 30577031 DOI: 10.1088/1361-6560/aafabd
    Historically, breast cancer risk prediction models are based on mammographic density measures, which are dichotomous in nature and generally categorize each voxel or area of the breast parenchyma as 'dense' or 'not dense'. Using these conventional methods, the structural patterns or textural components of the breast tissue elements are not considered or ignored entirely. This study presents a novel method to predict breast cancer risk that combines new texture and mammographic density based image features. We performed a comprehensive study of the correlation of 944 new and conventional texture and mammographic density features with breast cancer risk on a cohort of Asian women. We studied 250 breast cancer cases and 250 controls matched at full-field digital mammography (FFDM) status for age, BMI and ethnicity. Stepwise regression analysis identified relevant features to be included in a linear discriminant analysis (LDA) classifier model, trained and tested using a leave-one-out based cross-validation method. The area under the receiver operating characteristic (AUC) and adjusted odds ratios (ORs) were used as the two performance assessment indices in our study. For the LDA trained classifier, the adjusted OR was 6.15 (95% confidence interval: 3.55-10.64) and for Volpara volumetric breast density, 1.10 (0.67-1.81). The AUC for the LDA trained classifier was 0.68 (0.64-0.73), compared to 0.52 (0.47-0.57) for Volpara volumetric breast density (p   breast cancer risk assessment based models. Parenchymal texture analysis has an important role for stratifying breast cancer risk in women, which can be implemented to routine breast cancer screening strategies.
    Matched MeSH terms: Breast Density
  5. Tan M, Aghaei F, Wang Y, Zheng B
    Phys Med Biol, 2017 01 21;62(2):358-376.
    PMID: 27997380 DOI: 10.1088/1361-6560/aa5081
    The purpose of this study is to evaluate a new method to improve performance of computer-aided detection (CAD) schemes of screening mammograms with two approaches. In the first approach, we developed a new case based CAD scheme using a set of optimally selected global mammographic density, texture, spiculation, and structural similarity features computed from all four full-field digital mammography images of the craniocaudal (CC) and mediolateral oblique (MLO) views by using a modified fast and accurate sequential floating forward selection feature selection algorithm. Selected features were then applied to a 'scoring fusion' artificial neural network classification scheme to produce a final case based risk score. In the second approach, we combined the case based risk score with the conventional lesion based scores of a conventional lesion based CAD scheme using a new adaptive cueing method that is integrated with the case based risk scores. We evaluated our methods using a ten-fold cross-validation scheme on 924 cases (476 cancer and 448 recalled or negative), whereby each case had all four images from the CC and MLO views. The area under the receiver operating characteristic curve was AUC  =  0.793  ±  0.015 and the odds ratio monotonically increased from 1 to 37.21 as CAD-generated case based detection scores increased. Using the new adaptive cueing method, the region based and case based sensitivities of the conventional CAD scheme at a false positive rate of 0.71 per image increased by 2.4% and 0.8%, respectively. The study demonstrated that supplementary information can be derived by computing global mammographic density image features to improve CAD-cueing performance on the suspicious mammographic lesions.
    Matched MeSH terms: Breast Density
  6. Tan KP, Mohamad Azlan Z, Rumaisa MP, Siti Aisyah Murni MR, Radhika S, Nurismah MI, et al.
    Med J Malaysia, 2014 Apr;69(2):79-85.
    PMID: 25241817 MyJurnal
    AIM: This study was performed to determine the accuracy of ultrasound (USG) as compared to mammography (MMG) in detecting breast cancer.

    METHODS: This was a review of patients who had breast imaging and biopsy during an 18-month period. Details of patients who underwent breast biopsy were obtained from the department biopsy record books and imaging request forms. Details of breast imaging findings and histology of lesions biopsied were obtained from the hospital Integrated Radiology Information System (IRIS). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of USG and MMG were calculated with histology as the gold standard.

    RESULTS: A total of 326 breast lesions were biopsied. Histology results revealed the presence of 74 breast cancers and 252 benign lesions. USG had a sensitivity of 82%, specificity of 84%, PPV = 60%, NPV = 94% and an accuracy of 84%. MMG had a sensitivity of 49%, specificity of 89%, PPV = 53%, NPV = 88% and an accuracy of 81%. A total of 161 lesions which were imaged with both modalities were analyzed to determine the significance in the differences in sensitivity and specificity between USG and MMG. Sensitivity of USG (75%) was significantly higher than sensitivity of MMG (44%) (X(2)1=6.905, p=0.014). Specificity of MMG (91%) was significantly higher than specificity of USG (79%) (X(2)1=27.114, p<0.001). Compared with MMG, the sensitivity of USG was 50% (95% CI 10%-90%) higher in women aged less than 50 years (X(2)1=0.000, p=1.000) and 27% (95% CI 19%-36%) higher in women aged 50 years and above (X(2)1=5.866, p=0.015). Compared with MMG, the sensitivity of USG was 40% (95% CI 10%-70%) higher in women with dense breasts (X(2)1=0.234, p=0.628) and 27% (95% CI 9%-46%) higher in women with non-dense breasts (X(2)1=4.585, p=0.032).

    CONCLUSION: Accuracy of USG was higher compared with MMG. USG was more sensitive than MMG regardless of age group. However, MMG was more specific in those aged 50 years and older. USG was more sensitive and MMG was more specific regardless of breast density. In this study, 20% of breast cancers detected were occult on MMG and seen only on USG.
    Matched MeSH terms: Breast Density
  7. Soh WH, Rajaram N, Mariapun S, Eriksson M, Fadzli F, Ho WK, et al.
    Cancer Causes Control, 2018 Sep;29(9):883-894.
    PMID: 30062608 DOI: 10.1007/s10552-018-1064-6
    BACKGROUND: Physical activity is a modifiable lifestyle factor associated with reduced breast cancer risk. Mammographic density is a strong, independent risk factor for breast cancer, and some breast cancer risk factors have been shown to modify mammographic density. However, the effect of physical activity on mammographic density, studied predominantly among Caucasians, has yielded conflicting results. In this study, we examined, in an Asian population, the association between physical activity and mammographic density.

    METHODS: We conducted a cross-sectional study of 2,377 Malaysian women aged 40-74 years. Physical activity information was obtained at screening mammogram and mammographic density was measured from mammograms by the area-based STRATUS method (n = 1,522) and the volumetric Volpara™ (n = 1,200) method. Linear regression analyses were performed to evaluate the association between physical activity and mammographic density, adjusting for potential confounders.

    RESULTS: We observed that recent physical activity was associated with area-based mammographic density measures among postmenopausal women, but not premenopausal women. In the fully adjusted model, postmenopausal women with the highest level of recent physical activity had 8.0 cm2 [95% confidence interval: 1.3, 14.3 cm2] lower non-dense area and 3.1% [0.1, 6.3%] higher area-based percent density, compared to women with the lowest level of recent physical activity. Physical activity was not associated to volumetric mammographic density.

    CONCLUSIONS: Our findings suggest that the beneficial effects of physical activity on breast cancer risk may not be measurable through mammographic density. Future research is needed to identify appropriate biomarkers to assess the effect of physical activity on breast cancer risk.

    Matched MeSH terms: Breast Density*
  8. Sindi R, Wong YH, Yeong CH, Sun Z
    Quant Imaging Med Surg, 2020 Jun;10(6):1237-1248.
    PMID: 32550133 DOI: 10.21037/qims-20-251
    Background: Despite increasing reports of 3D printing in medical applications, the use of 3D printing in breast imaging is limited, thus, personalized 3D-printed breast model could be a novel approach to overcome current limitations in utilizing breast magnetic resonance imaging (MRI) for quantitative assessment of breast density. The aim of this study is to develop a patient-specific 3D-printed breast phantom and to identify the most appropriate materials for simulating the MR imaging characteristics of fibroglandular and adipose tissues.

    Methods: A patient-specific 3D-printed breast model was generated using 3D-printing techniques for the construction of the hollow skin and fibroglandular region shells. Then, the T1 relaxation times of the five selected materials (agarose gel, silicone rubber with/without fish oil, silicone oil, and peanut oil) were measured on a 3T MRI system to determine the appropriate ones to represent the MR imaging characteristics of fibroglandular and adipose tissues. Results were then compared to the reference values of T1 relaxation times of the corresponding tissues: 1,324.42±167.63 and 449.27±26.09 ms, respectively. Finally, the materials that matched the T1 relaxation times of the respective tissues were used to fill the 3D-printed hollow breast shells.

    Results: The silicone and peanut oils were found to closely resemble the T1 relaxation times and imaging characteristics of these two tissues, which are 1,515.8±105.5 and 405.4±15.1 ms, respectively. The agarose gel with different concentrations, ranging from 0.5 to 2.5 wt%, was found to have the longest T1 relaxation times.

    Conclusions: A patient-specific 3D-printed breast phantom was successfully designed and constructed using silicone and peanut oils to simulate the MR imaging characteristics of fibroglandular and adipose tissues. The phantom can be used to investigate different MR breast imaging protocols for the quantitative assessment of breast density.

    Matched MeSH terms: Breast Density
  9. Schwartz TM, Hillis SL, Sridharan R, Lukyanchenko O, Geiser W, Whitman GJ, et al.
    J Med Imaging (Bellingham), 2020 Mar;7(2):022408.
    PMID: 32042859 DOI: 10.1117/1.JMI.7.2.022408
    Purpose: Computer-aided detection (CAD) alerts radiologists to findings potentially associated with breast cancer but is notorious for creating false-positive marks. Although a previous paper found that radiologists took more time to interpret mammograms with more CAD marks, our impression was that this was not true in actual interpretation. We hypothesized that radiologists would selectively disregard these marks when present in larger numbers. Approach: We performed a retrospective review of bilateral digital screening mammograms. We use a mixed linear regression model to assess the relationship between number of CAD marks and ln (interpretation time) after adjustment for covariates. Both readers and mammograms were treated as random sampling units. Results: Ten radiologists, with median experience after residency of 12.5 years (range 6 to 24) interpreted 1832 mammograms. After accounting for number of images, Breast Imaging Reporting and Data System category, and breast density, the number of CAD marks was positively associated with longer interpretation time, with each additional CAD mark proportionally increasing median interpretation time by 4.35% for a typical reader. Conclusions: We found no support for our hypothesis that radiologists will selectively disregard CAD marks when they are present in larger numbers.
    Matched MeSH terms: Breast Density
  10. Rajaram N, Mariapun S, Eriksson M, Tapia J, Kwan PY, Ho WK, et al.
    Breast Cancer Res Treat, 2017 01;161(2):353-362.
    PMID: 27864652 DOI: 10.1007/s10549-016-4054-y
    PURPOSE: Mammographic density is a measurable and modifiable biomarker that is strongly and independently associated with breast cancer risk. Paradoxically, although Asian women have lower risk of breast cancer, studies of minority Asian women in predominantly Caucasian populations have found that Asian women have higher percent density. In this cross-sectional study, we compared the distribution of mammographic density for a matched cohort of Asian women from Malaysia and Caucasian women from Sweden, and determined if variations in mammographic density could be attributed to population differences in breast cancer risk factors.

    METHODS: Volumetric mammographic density was compared for 1501 Malaysian and 4501 Swedish healthy women, matched on age and body mass index. We used multivariable log-linear regression to determine the risk factors associated with mammographic density and mediation analysis to identify factors that account for differences in mammographic density between the two cohorts.

    RESULTS: Compared to Caucasian women, percent density was 2.0% higher among Asian women (p breast cancer may be accounted for by height, weight, and parity. Given that pre-menopausal Asian and Caucasian women have similar population risk to breast cancer but different dense volume, development of more appropriate biomarkers of risk in pre-menopausal women is required.

    Matched MeSH terms: Breast Density*
  11. Rahmat K, Ab Mumin N, Ramli Hamid MT, Fadzli F, Ng WL, Muhammad Gowdh NF
    Medicine (Baltimore), 2020 Sep 25;99(39):e22405.
    PMID: 32991467 DOI: 10.1097/MD.0000000000022405
    This study aims to compare Quantra, as an automated volumetric breast density (Vbd) tool, with visual assessment according to ACR BI-RADS density categories and to determine its potential usage in clinical practice.Five hundred randomly selected screening and diagnostic mammograms were included in this retrospective study. Three radiologists independently assigned qualitative ACR BI-RADS density categories to the mammograms. Quantra automatically calculates the volumetric density data into the system. The readers were blinded to the Quantra and other readers assessment. Inter-reader agreement and agreement between Quantra and each reader were tested. Region under the curve (ROC) analysis was performed to obtain the cut-off value to separate dense from a non-dense breast. Results with P value breasts with a sensitivity of 86.2% and specificity of 83.1% (AUC 91.4%; confidence interval: 88.8, 94.1).Quantra showed moderate agreement with radiologists visual assessment. Hence, this study adds to the available evidence to support the potential use of Quantra as an adjunct tool for breast density assessment in routine clinical practice in the Asian population. We found 13.5% is the best cut-off value to stratify dense to non-dense breasts in our study population. Its application will provide an objective, consistent and reproducible results as well as aiding clinical decision-making on the need for supplementary breast ultrasound in our screening population.
    Matched MeSH terms: Breast Density*
  12. Nur Izzati Najwa Suliman, Rafidah Supar, Hairenanorashikin Sharip
    MyJurnal
    Application of compression during mammography is crucial to reduce breast thickness and reducing
    average glandular dose (AGD). With increasing participation in regular breast screening programmes, the
    total AGD received by patient remains a concern. Therefore, this paper aimed to evaluate the effect of
    compressed breast thickness (CBT) on the AGD during screening mammography using full field digital
    mammogram (FFDM). This study involved retrospective collection of mammographic data and reports
    from 148 women who came for screening mammography. Mammographic parameters which include
    CBT, AGD, compression force and breast density for both breast on craniocaudal (CC) view and
    mediolateral oblique (MLO) view were recorded and analysed. There was statistically significant
    variation in the mammographic parameters value between CC and MLO projections but no significant
    variation between right and left breasts. For CC projection, a weak positive correlation was identified
    between CBT and AGD (r=0.115, p=0.049) and between CBT and compression force (r=0.172, p=0.003).
    In addition, a weak positive correlation was also found between CBT and compression force (r=0.200,
    p=0.001) and between CBT and AGD (r=0.292, p
    Matched MeSH terms: Breast Density
  13. Norhayati Mohd Zain, Kanaga, Kumari Chelliah, Vengkatha, Priya Seriramulu, Shantini Arasaratnam, Poh, Bee Koon
    Jurnal Sains Kesihatan Malaysia, 2017;15(22):137-144.
    MyJurnal
    Daily food intake of women may affect their bone health by altering their bone mineral density (BMD) as the lack of certain
    nutrients may affect bone integrity whilst, BMD also can be a predictor of breast cancer. To date, many studies have been
    conducted to discuss on association of BMD and mammographic breast density (MBD) and how both are related to breast
    cancer risks but no consideration has been made on dietary intake. Therefore, this study was designed to determine
    the association of dietary intake with BMD and other breast cancer risk factors. A cross-sectional study on 76 pre- and
    postmenopausal women above 40 years underwent mammogram screening and Dual Energy X-ray Absorptiometry (DEXA)
    was conducted in Hospital Kuala Lumpur (HKL) for the duration of 1 year. Purposive sampling method was used to choose
    the respondents. Women who are diagnosed with breast cancer and underwent cancer treatment were excluded from this
    study. DEXA unit (Hologic Discovery W, Hologic, Inc) were used to measure BMD at the femoral neck and lumbar spine in
    grams per centimetre squared (g/cm2
    ) and they were classified into normal and abnormal group based on the T-scores.
    The subjects were asked about their daily dietary pattern for a duration of three days using Diet History Questionnaire
    (DHQ). The mean of selected characteristics were compared between groups. Additionally, binary logistic regression was
    used to determine the association between diet intake with BMD and other risk factors of breast cancer. The total number
    of pre- and postmenopausal women who consented to participate in this study are equal. The mean age was 47.1 years
    and 54.9 years for premenopausal and postmenopausal women respectively. The results indicate only menopausal age of
    the women was statistically significant (p < 0.05). A number of 17% premenopausal and 9% of postmenopausal women
    showed to have family history of breast cancer, however, it was not statistically significant (p = 0.12). There was no
    significant difference in daily energy intake of food in both groups (p = 0.22). None of the nutrients in daily food intake
    showed to be statistically significant. Menstrual status showed an association with BMD with p < 0.05 and the remaining
    risk factors did not show any association. Logistic regression revealed that only menstrual status had correlation with
    BMD in both groups. This study provided the dietary pattern and the effects on bone health. The association of other risk
    factors of breast cancer with BMD were also analysed and most of it showed a negative association.
    Matched MeSH terms: Breast Density
  14. Newman LA, Yip CH
    JAMA Surg, 2020 04 01;155(4):279-280.
    PMID: 32096827 DOI: 10.1001/jamasurg.2020.0280
    Matched MeSH terms: Breast Density
  15. McCormack VA, Burton A, dos-Santos-Silva I, Hipwell JH, Dickens C, Salem D, et al.
    Cancer Epidemiol, 2016 Feb;40:141-51.
    PMID: 26724463 DOI: 10.1016/j.canep.2015.11.015
    Mammographic density (MD) is a quantitative trait, measurable in all women, and is among the strongest markers of breast cancer risk. The population-based epidemiology of MD has revealed genetic, lifestyle and societal/environmental determinants, but studies have largely been conducted in women with similar westernized lifestyles living in countries with high breast cancer incidence rates. To benefit from the heterogeneity in risk factors and their combinations worldwide, we created an International Consortium on Mammographic Density (ICMD) to pool individual-level epidemiological and MD data from general population studies worldwide. ICMD aims to characterize determinants of MD more precisely, and to evaluate whether they are consistent across populations worldwide. We included 11755 women, from 27 studies in 22 countries, on whom individual-level risk factor data were pooled and original mammographic images were re-read for ICMD to obtain standardized comparable MD data. In the present article, we present (i) the rationale for this consortium; (ii) characteristics of the studies and women included; and (iii) study methodology to obtain comparable MD data from original re-read films. We also highlight the risk factor heterogeneity captured by such an effort and, thus, the unique insight the pooled study promises to offer through wider exposure ranges, different confounding structures and enhanced power for sub-group analyses.
    Matched MeSH terms: Breast Density
  16. Mariapun S, Ho WK, Kang PC, Li J, Lindström S, Yip CH, et al.
    Cancer Epidemiol Biomarkers Prev, 2016 Feb;25(2):327-33.
    PMID: 26677210 DOI: 10.1158/1055-9965.EPI-15-0746
    Mammographic density is an established risk factor for breast cancer and has a strong heritable component. Genome-wide association studies (GWAS) for mammographic density conducted in women of European descent have identified several genetic associations, but none of the studies have been tested in Asians. We sought to investigate whether these genetic loci, and loci associated with breast cancer risk and breast size, are associated with mammographic density in an Asian cohort.
    Matched MeSH terms: Breast Density
  17. Lo CH, Chai XY, Ting SSW, Ang SC, Chin X, Tan LT, et al.
    Cancer Med, 2020 05;9(9):3244-3251.
    PMID: 32130790 DOI: 10.1002/cam4.2821
    BACKGROUND: Breast cancer is the leading cause of death among women worldwide. Studies have identified breast density as a controversial risk factor of breast cancer. Moreover, studies found that breast density reduction through Tamoxifen could reduce risk of breast cancer significantly. To date, no study on the association between breast density and breast cancer has been carried out in Malaysia. If breast density is proven to be a risk factor of breast cancer, intervention could be carried out to reduce breast cancer risk through breast density reduction.

    PURPOSE: To determine if density of breast is an independent risk factor which will contribute to development of breast cancer.

    MATERIALS AND METHODS: A prospective cohort study is carried out in two hospitals targeting adult female patients who presented to the Breast Clinic with symptoms suspicious of breast cancer. Participants recruited were investigated for breast cancer based on their symptoms. Breast density assessed from mammogram was correlated with tissue biopsy results and final diagnosis of benign or malignant breast disease.

    RESULTS: Participants with dense breasts showed 29% increased risk of breast cancer when compared to those with almost entirely fatty breasts (odds ratio [OR] 1.29, 95% CI 0.38-4.44, P = .683). Among the postmenopausal women, those with dense breasts were 3.1 times more likely to develop breast cancer compared with those with fatty breasts (OR 3.125, 95% CI 0.72-13.64, P = .13). Moreover, the chance of developing breast cancer increases with age (OR 1.046, 95% CI 1.003-1.090, P breast decreases with increasing age (P breast density whether in the whole sample size, premenopausal, or postmenopausal group was consistently high.

    CONCLUSION: Although results were not statistically significant, important association between breast density and risk of breast cancer cannot be ruled out. The study is limited by a small sample size and subjective assessment of breast density. More studies are required to reconcile the differences between studies of contrasting evidence.

    Matched MeSH terms: Breast Density*
  18. Li J, Ugalde-Morales E, Wen WX, Decker B, Eriksson M, Torstensson A, et al.
    Cancer Res, 2018 11 01;78(21):6329-6338.
    PMID: 30385609 DOI: 10.1158/0008-5472.CAN-18-1018
    Genetic variants that increase breast cancer risk can be rare or common. This study tests whether the genetic risk stratification of breast cancer by rare and common variants in established loci can discriminate tumors with different biology, patient survival, and mode of detection. Multinomial logistic regression tested associations between genetic risk load [protein-truncating variant (PTV) carriership in 31 breast cancer predisposition genes-or polygenic risk score (PRS) using 162 single-nucleotide polymorphisms], tumor characteristics, and mode of detection (OR). Ten-year breast cancer-specific survival (HR) was estimated using Cox regression models. In this unselected cohort of 5,099 patients with breast cancer diagnosed in Sweden between 2001 and 2008, PTV carriers (n = 597) were younger and associated with more aggressive tumor phenotypes (ER-negative, large size, high grade, high proliferation, luminal B, and basal-like subtype) and worse outcome (HR, 1.65; 1.16-2.36) than noncarriers. After excluding 92 BRCA1/2 carriers, PTV carriership remained associated with high grade and worse survival (HR, 1.76; 1.21-2.56). In 5,007 BRCA1/2 noncarriers, higher PRS was associated with less aggressive tumor characteristics (ER-positive, PR-positive, small size, low grade, low proliferation, and luminal A subtype). Among patients with low mammographic density (<25%), non-BRCA1/2 PTV carriers were more often interval than screen-detected breast cancer (OR, 1.89; 1.12-3.21) than noncarriers. In contrast, higher PRS was associated with lower risk of interval compared with screen-detected cancer (OR, 0.77; 0.64-0.93) in women with low mammographic density. These findings suggest that rare and common breast cancer susceptibility loci are differentially associated with tumor characteristics, survival, and mode of detection.Significance: These findings offer the potential to improve screening practices for breast cancer by providing a deeper understanding of how risk variants affect disease progression and mode of detection. Cancer Res; 78(21); 6329-38. ©2018 AACR.
    Matched MeSH terms: Breast Density
  19. Lau S, Ng KH, Abdul Aziz YF
    Br J Radiol, 2016 Oct;89(1066):20160258.
    PMID: 27452264 DOI: 10.1259/bjr.20160258
    OBJECTIVE: To investigate the sensitivity and robustness of a volumetric breast density (VBD) measurement system to errors in the imaging physics parameters including compressed breast thickness (CBT), tube voltage (kVp), filter thickness, tube current-exposure time product (mAs), detector gain, detector offset and image noise.

    METHODS: 3317 raw digital mammograms were processed with Volpara(®) (Matakina Technology Ltd, Wellington, New Zealand) to obtain fibroglandular tissue volume (FGV), breast volume (BV) and VBD. Errors in parameters including CBT, kVp, filter thickness and mAs were simulated by varying them in the Digital Imaging and Communications in Medicine (DICOM) tags of the images up to ±10% of the original values. Errors in detector gain and offset were simulated by varying them in the Volpara configuration file up to ±10% from their default values. For image noise, Gaussian noise was generated and introduced into the original images.

    RESULTS: Errors in filter thickness, mAs, detector gain and offset had limited effects on FGV, BV and VBD. Significant effects in VBD were observed when CBT, kVp, detector offset and image noise were varied (p 

    Matched MeSH terms: Breast Density*
  20. Lau S, Abdul Aziz YF, Ng KH
    PLoS One, 2017;12(4):e0175781.
    PMID: 28419125 DOI: 10.1371/journal.pone.0175781
    OBJECTIVES: To investigate: (1) the variability of mammographic compression parameters amongst Asian women; and (2) the effects of reducing compression force on image quality and mean glandular dose (MGD) in Asian women based on phantom study.

    METHODS: We retrospectively collected 15818 raw digital mammograms from 3772 Asian women aged 35-80 years who underwent screening or diagnostic mammography between Jan 2012 and Dec 2014 at our center. The mammograms were processed using a volumetric breast density (VBD) measurement software (Volpara) to assess compression force, compression pressure, compressed breast thickness (CBT), breast volume, VBD and MGD against breast contact area. The effects of reducing compression force on image quality and MGD were also evaluated based on measurement obtained from 105 Asian women, as well as using the RMI156 Mammographic Accreditation Phantom and polymethyl methacrylate (PMMA) slabs.

    RESULTS: Compression force, compression pressure, CBT, breast volume, VBD and MGD correlated significantly with breast contact area (p<0.0001). Compression parameters including compression force, compression pressure, CBT and breast contact area were widely variable between [relative standard deviation (RSD)≥21.0%] and within (p<0.0001) Asian women. The median compression force should be about 8.1 daN compared to the current 12.0 daN. Decreasing compression force from 12.0 daN to 9.0 daN increased CBT by 3.3±1.4 mm, MGD by 6.2-11.0%, and caused no significant effects on image quality (p>0.05).

    CONCLUSIONS: Force-standardized protocol led to widely variable compression parameters in Asian women. Based on phantom study, it is feasible to reduce compression force up to 32.5% with minimal effects on image quality and MGD.

    Matched MeSH terms: Breast Density
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