Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer

Duffy, Stephen W.; Morrish, Oliver; Allgood, P; Black, Richard; Gillan, Maureen Gc; Willsher, Paula; Cooke, Julie; Duncan, Karen A.; Michell, Michael; Dobson, H.; Maroni, Roberta; Lim, Yit Yoong; Purushothaman, Hema; Suaris, Tamara; Astley, Susan M.; Young, Kenneth C.; Tucker, Lorraine; Gilbert, Fiona J.

doi:10.1016/j.ejca.2017.10.022

Cited by 73 publications

(51 citation statements)

References 42 publications

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“…20,21 Young women with dense mammary tissue were threefold likely to develop breast cancer than their counterparts. Similar results have been found by Duffy et al 22 The estrogens levels in women with dense breast may not be involved in the occurrence of breast cancer. 23 Carcinogenesis would certainly be related to higher cell volume than in women with less dense breasts.…”

Section: Discussionsupporting

confidence: 90%

Factors associated with breast cancer occurrence before the age of 40 in Yaoundé

Essiben

Meka

Ayissi

et al. 2020

Int J Reprod Contracept Obstet Gynecol

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Background: In Cameroon, one third of breast cancers concern women less than 40 of age and young age appeared to be an independent predictor of adverse outcome. This study aimed to determine factors associated with the development of breast cancer before the age of 40 in Yaoundé.Methods: A case-control study was conducted for 7 months, from November 1st, 2016 to May 31st, 2017 at the General Hospital and the gyneco-obstetric and Paediatric Hospital of Yaoundé. Study compared the socio-demographic, clinical and lifestyle data of 50 women less than 40 of age with breast cancer (cases) with those of 200 women below 40 of age without breast cancer (controls). Descriptive statistics, bivariate and multivariate logistic regressions were performed to assess the socio-demographic, clinical and lifestyle data. Data were computed using the SPSS version 23.0 software. The significance threshold value was set at 0.05.Results: After multivariate analysis, the factors independently associated with the occurrence of breast cancer before the age of 40 were: age ≥ 30 (aOR = 2.05); family history of breast cancer (aOR = 8.65); parity ≥ 1 (aOR = 2.46); breastfeeding (aOR = 3.39); waist circumference ≥ 88 centimeters (cm) (aOR = 4.38); breast density ≥ 90 (aOR = 2.89); physical activity ≤ 3 hours/week (aOR = 4.78). Conclusions: Breast cancer in women under 40 is associated with family history, reproductive life and women's lifestyles. Breastfeeding more than 12 months, having a balanced diet, supplementary screening tests in women with dense breasts and practicing physical activity; would be effective ways to prevent it in young women.

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Section: Discussionsupporting

confidence: 90%

Factors associated with breast cancer occurrence before the age of 40 in Yaoundé

Essiben

Meka

Ayissi

et al. 2020

Int J Reprod Contracept Obstet Gynecol

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“…A deep learning method proposed by Lehman et al 45 for assessing BI-RADS density in a clinical setting, showed good agreement between the model's predictions and radiologists' assessments. Duffy et al 46 investigated the association of different density measures with breast cancer risk using digital breast tomosynthesis and compared automatic and visual measures. All measures showed a positive correlation with cancer risk, but the strongest effect was shown by an absolute density measure.…”

Section: Discussionmentioning

confidence: 99%

Prediction of reader estimates of mammographic density using convolutional neural networks

et al. 2019

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Mammographic density is an important risk factor for breast cancer. In recent research, percentage density assessed visually using visual analogue scales (VAS) showed stronger risk prediction than existing automated density measures, suggesting readers may recognize relevant image features not yet captured by hand-crafted algorithms. With deep learning, it may be possible to encapsulate this knowledge in an automatic method. We have built convolutional neural networks (CNN) to predict density VAS scores from full-field digital mammograms. The CNNs are trained using whole-image mammograms, each labeled with the average VAS score of two independent readers. Each CNN learns a mapping between mammographic appearance and VAS score so that at test time, they can predict VAS score for an unseen image. Networks were trained using 67,520 mammographic images from 16,968 women and for model selection we used a dataset of 73,128 images. Two case-control sets of contralateral mammograms of screen detected cancers and prior images of women with cancers detected subsequently, matched to controls on age, menopausal status, parity, HRT and BMI, were used for evaluating performance on breast cancer prediction. In the case-control sets, odd ratios of cancer in the highest versus lowest quintile of percentage density were 2.49 (95% CI: 1.59 to 3.96) for screendetected cancers and 4.16 (2.53 to 6.82) for priors, with matched concordance indices of 0.587 (0.542 to 0.627) and 0.616 (0.578 to 0.655), respectively. There was no significant difference between reader VAS and predicted VAS for the prior test set (likelihood ratio chi square, p ¼ 0.134). Our fully automated method shows promising results for cancer risk prediction and is comparable with human performance.

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“…These limitations can affect all automated breast density methods. However, Volpara seems to provide a better correlation with visual assessment of breast density as noted by Duffy et al 57 who analyzed both Volpara and Quantra software, which helps to justify our choice of comparison in this research. Vinnicombe also prefers Volpara to Cumulus.…”

Section: Discussionmentioning

confidence: 72%

Quantitative assessment of breast density using transmission ultrasound tomography

et al. 2019

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Purpose Breast density is important in the evaluation of breast cancer risk. At present, breast density is evaluated using two‐dimensional projections from mammography with or without tomosynthesis using either (a) subjective assessment or (b) a computer‐aided approach. The purpose of this work is twofold: (a) to establish an algorithm for quantitative assessment of breast density using quantitative three‐dimensional transmission ultrasound imaging; and (b) to determine how these quantitative assessments compare with both subjective and objective mammographic assessments of breast density. Methods We described and verified a threshold‐based segmentation algorithm to give a quantitative breast density ( QBD ) on ultrasound tomography images of phantoms of known geometric forms. We also used the algorithm and transmission ultrasound tomography to quantitatively determine breast density by separating fibroglandular tissue from fat and skin, based on imaged, quantitative tissue characteristics, and compared the quantitative tomography segmentation results with subjective and objective mammographic assessments. Results Quantitative breast density ( QBD ) measured in phantoms demonstrates high quantitative accuracy with respect to geometric volumes with average difference of less than 0.1% of the total phantom volumes. There is a strong correlation between QBD and both subjective mammographic assessments of Breast Imaging ‐ Reporting and Data System ( BI ‐ RADS ) breast composition categories and Volpara density scores — the Spearman correlation coefficients for the two comparisons were calculated to be 0.90 (95% CI : 0.71–0.96) and 0.96 (95% CI : 0.92–0.98), respectively. Conclusions The calculation of breast density using ultrasound tomography and the described segmentation algorithm is quantitatively accurate in phantoms and highly correlated with both subjective and Food and Drug Administration ( FDA )‐cleared objective assessments of breast density.

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Mammographic density and breast cancer risk in breast screening assessment cases and women with a family history of breast cancer

Cited by 73 publications

References 42 publications

Factors associated with breast cancer occurrence before the age of 40 in Yaoundé

Factors associated with breast cancer occurrence before the age of 40 in Yaoundé

Prediction of reader estimates of mammographic density using convolutional neural networks

Quantitative assessment of breast density using transmission ultrasound tomography

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