scholarly journals Validation of a new fully automated software for 2D digital mammographic breast density evaluation in predicting breast cancer risk

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Paolo Giorgi Rossi ◽  
Olivera Djuric ◽  
Valerie Hélin ◽  
Susan Astley ◽  
Paola Mantellini ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Breast Imaging ◽  
Interval Cancer ◽  
Visual Assessment ◽  
Automated System ◽  
Masking Effect ◽  
Mammographic Breast Density ◽  
Point Estimate ◽  
Automated Method

AbstractWe compared accuracy for breast cancer (BC) risk stratification of a new fully automated system (DenSeeMammo—DSM) for breast density (BD) assessment to a non-inferiority threshold based on radiologists’ visual assessment. Pooled analysis was performed on 14,267 2D mammograms collected from women aged 48–55 years who underwent BC screening within three studies: RETomo, Florence study and PROCAS. BD was expressed through clinical Breast Imaging Reporting and Data System (BI-RADS) density classification. Women in BI-RADS D category had a 2.6 (95% CI 1.5–4.4) and a 3.6 (95% CI 1.4–9.3) times higher risk of incident and interval cancer, respectively, than women in the two lowest BD categories. The ability of DSM to predict risk of incident cancer was non-inferior to radiologists’ visual assessment as both point estimate and lower bound of 95% CI (AUC 0.589; 95% CI 0.580–0.597) were above the predefined visual assessment threshold (AUC 0.571). AUC for interval (AUC 0.631; 95% CI 0.623–0.639) cancers was even higher. BD assessed with new fully automated method is positively associated with BC risk and is not inferior to radiologists’ visual assessment. It is an even stronger marker of interval cancer, confirming an appreciable masking effect of BD that reduces mammography sensitivity.

Mammographic Breast Density: Effect on Imaging and Breast Cancer Risk

2010 ◽  
Vol 8 (10) ◽  
pp. 1157-1165 ◽  
Author(s):  
Renee W. Pinsky ◽  
Mark A. Helvie
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Menopausal Status ◽  
Masking Effect ◽  
Mammographic Breast Density ◽  
Ultrasound Screening ◽  
High Risk Women ◽  
Dense Breasts ◽  
Reduction Strategies ◽  
Risk Reduction Strategies

Mammographic breast density has been studied for more than 30 years. Greater breast density not only is related to decreased sensitivity of mammograms because of a masking effect but also is a major independent risk factor for breast cancer. This article defines breast density and reviews literature on quantification of mammographic density that is key to future clinical and research protocols. Important influences on breast density are addressed, including age, menopausal status, exogenous hormones, and genetics of density. Young women with dense breasts benefit from digital mammographic technique. The potential use of supplemental MRI and ultrasound screening techniques in high-risk women and women with dense breasts is explored, as are potential risk reduction strategies.

scholarly journals AutoDensity: an automated method to measure mammographic breast density that predicts breast cancer risk and screening outcomes

2013 ◽  
Vol 15 (5) ◽  
Author(s):  
Carolyn Nickson ◽  
Yulia Arzhaeva ◽  
Zoe Aitken ◽  
Tarek Elgindy ◽  
Mitchell Buckley ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Cancer Risk ◽  
Cancer Risk ◽  
Breast Density ◽  
Mammographic Breast Density ◽  
Automated Method

scholarly journals Mammographic Breast Density Profile of Jordanian Women With Normal and Breast Cancer Findings

2020 ◽  
Vol 14 ◽  
pp. 117822342092138
Author(s):  
Dana S Al-Mousa ◽  
Maram Alakhras ◽  
Kelly M Spuur ◽  
Haytham Alewaidat ◽  
Mohammad Rawashdeh ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Breast Density ◽  
Breast Imaging ◽  
Normal Group ◽  
University Hospital ◽  
Mammographic Breast Density ◽  
Screening Guidelines ◽  
Cancer Group ◽  
Bonferroni Adjustment ◽  
Breast Cancer Group

Purpose: To document the mammographic breast density (MBD) distribution of Jordanian women and the relationship with MBD with age. Correlation between breast cancer diagnosis and density was also explored. Methods: A retrospective review of 660 screening mammograms from King Abdullah University Hospital was conducted. Mammograms were classified into 2 groups: normal (return to routine screening) and breast cancer and rated using the American College of Radiology (ACR) Breast Imaging-Reporting and Data System (BI-RADS) 5th edition for MBD. The association between MBD and age was assessed by descriptive analyses and Kruskal-Wallis test. To compare between normal and breast cancer groups, chi-square post hoc tests with Bonferroni adjustment was used. Results: Groups consisted of 73.9% (n = 488) normal group and 26.1% (n = 172) breast cancer group. A significant inverse relationship was demonstrated between age and MBD among the normal ( r = −.319, P < .01) and breast cancer group ( r = −.569, P < .01). In total, 69% (n = 336) of women in the normal group and 71% (n = 122) in the breast cancer group and 79.1% (n = 159) of the normal group and 100% (n = 48) of the breast cancer group aged 40 to 49 years reported high MBD (ACR BI-RADS c or d). Conclusions: Most of women in both the normal and breast cancer groups evidenced increased MBD. Increased MBD was inversely proportional to age. As MBD has a known link to increased breast cancer risk and the decreased sensitivity of mammography and it is vital that future screening guidelines for Jordanian women consider the unique breast density distribution of this population.

scholarly journals Mammographic Breast Density Assessed with Fully Automated Method and its Risk for Breast Cancer

2019 ◽  
Vol 9 ◽  
pp. 43 ◽  
Author(s):  
Pendem Saikiran ◽  
Ruqiya Ramzan ◽  
Nandish S. ◽  
Phani Deepika Kamineni ◽  
Priyanka ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Risk Factors ◽  
Cancer Risk ◽  
Breast Density ◽  
Roc Curve ◽  
Clinical Risk Factors ◽  
Mammographic Breast Density ◽  
Risk Models ◽  
Clinical Risk ◽  
Automated Method

Objectives: We evaluated the association between breast cancer and breast density (BD) measured using fully automated software. We also evaluated the performance of cancer risk models such as only clinical risk factors, density related measures, and both clinical risk factors and density-related measures for determining cancer risk. Materials and Methods: This is a retrospective case–control study. The data were collected from August 2015 to December 2018. Two hundred fifty women with breast cancer and 400 control subjects were included in this study. We evaluated the BD qualitatively using breast imaging-reporting and data system density and quantitatively using 3D slicer. We also collected clinical factors such as age, familial history of breast cancer, menopausal status, number of births, body mass index, and hormonal replacement therapy use. We calculated the odds ratio (OR) for BD to determine the risk of breast cancer. We performed receiver operating characteristic (ROC) curve to assess the performance of cancer risk models. Results: The OR for the percentage BD for second, third, and fourth quartiles was 1.632 (95% confidence intervals [CI]: 1.102–2.416), 2.756 (95% CI: 1.704–4.458), and 3.163 (95% CI: 1.356–5.61). The area under ROC curve for clinical risk factors only, mammographic density measures, combined mammographic, and clinical risk factors was 0.578 (95% CI: 0.45, 0.64), 0.684 (95% CI: 0.58, 0.75), and 0.724 (95% CI: 0.64, 0.80), respectively. Conclusion: Mammographic BD was found to be positively associated with breast cancer. The density related measures combined clinical risk factors, and density model had good discriminatory power in identifying the cancer risk.

Mammographic breast density and survival in women with invasive breast cancer

2021 ◽  
Author(s):  
Margherita Pizzato ◽  
Greta Carioli ◽  
Stefano Rosso ◽  
Roberto Zanetti ◽  
Carlo La Vecchia
Keyword(s):  
Breast Cancer ◽  
Overall Survival ◽  
Breast Density ◽  
Breast Imaging ◽  
Proportional Hazards Model ◽  
Cox Proportional Hazards ◽  
Cox Proportional Hazards Model ◽  
Mammographic Breast Density ◽  
Rank Test ◽  
Prognostic Impact

Abstract Purpose: Mammographic breast density (BD) is strongly associated to breast cancer (BC) risk; however, its association with survival is unclear.Methods: Using data from the Piedmont Cancer Registry (Registro Tumori Piemonte), we identified 693 women diagnosed with primary invasive BC between 2009-2014. We applied the Kaplan-Meier method to estimate overall survival in strata of BD and the log-rank test to assess survival differences. We evaluated the hazard ratios (HRs) of death using Cox proportional hazards model and HRs of BC-related and other causes of death using the cause-specific hazards regression model. Models included terms for BD (assessed according to the Breast Imaging Reporting and Data System [BI-RADS] density classification) and were adjusted for selected patient and tumour characteristics.Results: There were102 deaths, of which 49 were from BC. After 5 years, the overall survival was 70% in women with BI-RADS 1, 85% in those with BI-RADS 2, about 95% in those with BI-RADS 3-4 (p <0.01). As compared to women with low BD (BI-RADS 1), the adjusted HRs of death was 0.71 (95% confidence interval (CI) 0.44–1.14) for BI-RADS 2 and 0.38 (95% CI 0.18–0.80) for BI-RADS 3-4 (p for trend = 0.010). As compared to BI-RADS 1, the adjusted HRs of BC-related death decreased with increasing BI-RADS BD from 0.90 (95% CI 0.43–1.87) for BI-RADS 2 to 0.32 (95% CI 0.12–0.91) for BI-RADS 3-4 (p for trend = 0.047). Conclusion: In women with BC, low BD has a negative prognostic impact.

Association between mammographic breast density and achievement of a pathologic complete remission (pCR) after neoadjuvant chemotherapy (NACT) for early breast cancer (EBC).

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e12611-e12611
Author(s):  
Cornelia Kolberg-Liedtke ◽  
Mohamed Shaheen ◽  
Oliver Hoffmann ◽  
Ann-Kathrin Bittner ◽  
Sarah Wetzig ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neoadjuvant Chemotherapy ◽  
Complete Remission ◽  
Early Breast Cancer ◽  
Breast Density ◽  
Breast Imaging ◽  
Tumor Biology ◽  
Small Sample ◽  
Mammographic Breast Density ◽  
Pathologic Complete Remission

e12611 Background: Neoadjuvant chemotherapy (NACT) is indicated in early breast cancer (EBC) with an unfavorable tumor biology. Achievement of pathologic complete remission (pCR) after NACT is indicating an improved prognosis. An association between pCR and mammographic breast density as defined by BIRADS (Breast Imaging Reporting and Data System) could be demonstrated. However, the definition of mammographic breast density by the American College of Radiology (ACR) is widely used worldwide and data regarding an association of breast density by this definition and pCR after NACT are missing. Methods: We conducted a retrospective analysis among patients who had received neoadjuvant chemotherapy (NACT) for EBC and had available data regarding mammographic breast density as defined by ACR before therapy, pCR, age, estrogen and progesterone receptor (ER, PR) status, HER2neu status and grading were included. An association between mammographic breast density (ACR) and pCR was analyzed. Results: 185 patients were included in this analysis, 35.7% of whom achieved a pCR. Mammographic breast density was ACR 1 in 15.1%, ACR 2 in 41.6%, ACR3 in 38.4% and ACR 4 in 4.9% of the patients. A negative correlation (Spearman-Rho) between mammographic breast density and pCR (correlation coefficient (CC) -0.240) was highly statistically significant (p = 0.001). The association of decreasing pCR rates with increasing mammographic breast density (pCR rates by ACR 1: 53.6%, ACR 2: 41.6, ACR 3: 25.4% and 11.1 %) was statistically significant (Chi-Square, p = 0.013). These results were independent of age, ER status, PR status, HER2neu status and grading. Conclusions: In our analysis higher mammographic breast density as defined by ACR was significantly correlated with a lower chance of achieving a pCR after NACT. Although this result has to be interpreted with caution due to the small sample size and the retrospective character of our investigation, it is completely in line with other investigations using other definitions of mammographic breast density. The pathophysiological cause of this association should be further elucidated to reveal potential mechanisms of treatment resistance.

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