Breast density is an important factor in assessing individual breast cancer risk. We aim to identify women at increased risk of developing breast cancer before they enter routine screening, using mammography in combination with known risk factors. This will enable targeting of preventive therapies and personalised screening. To reduce radiation risk, this paper examines whether density measurements in one breast or mammographic view could be used to accurately reflect individual risk. We analysed breast cancer risk using breast density in a 1:3 case-control dataset of mammograms from the Predicting Risk of Cancer at Screening Study (PROCAS). Breast density was measured using pVAS, an AI-based approach. Cancer risk in low and high breast density groups was compared using conditional logistic regression. High breast density was independently associated with increased breast cancer risk. Women in the highest breast density quintile averaged across all views had an Odds Ratio (OR) of 4.16 (95% CI 2.90-5.97) compared to those in the lowest. A similar OR was found in both the left 3.77 (95% CI 2.68-5.31) and right 4.52 (95% CI 3.12-6.55) breasts individually. ORs were also significant for each individual view: right mediolateral oblique (MLO) 4.19 (2.92–6.00), right craniocaudal (CC) 4.40 (3.09–6.27), left MLO 3.27 (2.34–4.56) and left CC 3.65 (2.60–5.11). The ability to predict breast cancer risk due to increased breast density was achieved using one breast and even one mammographic view. This provides the possibility of a pre-screening risk assessment using fewer images and therefore less radiation.
The prevention and early detection of breast cancer hinges on precise prediction of individual breast cancer risk. Whilst well-established clinical risk factors can be used to stratify the population into risk groups, the addition of genetic information and breast density has been shown to improve prediction. Deep learning based approach have been shown to automatically extract complex information from images. However, this is a challenging area of research, partly due to the lack of data within the field, therefore there is scope for novel approaches. Our method uses Multiple Instance Learning in tandem with attention in order to make accurate, short-term risk predictions from full-sized mammograms taken prior to the detection of cancer. This approach ensures small features like calcifications are not lost in a downsizing process and the whole mammogram is analysed effectively. An attention pooling mechanism is designed to highlight patches of increased importance and improve performance. We also use transfer learning in order to utilise a rich source of screen-detected cancers and evaluate whether a model trained to detect cancers in mammograms allows us also to predict risk in priors. Our model achieves an AUC of 0.620 (0.585,0.657) in cancer-free screening mammograms of women who went on to a screen-detected or interval cancer between 5 and 55 months later, including for common breast cancer risk factors. Additionally, our model is able to discriminate interval cancers at an AUC of 0.638 (0.572, 0.703) and highlights the potential for such a model to be used alongside national screening programmes.
Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. Whilst well-established clinical risk factors can be used to stratify the population into risk groups, the addition of genetic information and breast density has been shown to improve prediction. Machine learning enabled automatic risk prediction provides key advantages over existing methods such as the ability to extract more complex information from mammograms. However, this is a challenging area of research, partly due to the lack of data within the field, therefore there is scope for novel approaches. Our method uses Multiple Instance Learning in tandem with attention in order to make accurate, short-term risk predictions from full-sized mammograms taken prior to the detection of cancer. This approach ensures small features like calcifications are not lost in a downsizing process and the whole mammogram is analysed effectively. An attention pooling mechanism is designed to highlight patches of increased importance and improve performance. Additionally, this increases the interpretability of our model as important patches can be shown in a saliency map. We also use transfer learning in order to utilise a rich source of screen-detected cancers and evaluate whether a model trained to detect cancers in mammograms allows us also to predict risk in priors. Our model achieves an AUC of 0.635 (0.600,0.669) in cancer-free screening mammograms of women who went on to a screen-detected or interval cancer between 5 and 55 months and an AUC of 0.804 (0.777,0.830) in screen-detected cancers.
Estimation of breast density for cancer risk prediction is generally achieved by analysis of full-field digital mammograms. Conventional digital mammography should be avoided if possible in young women because of concerns about potential cancer induction, particularly in those with dense breasts who receive higher doses. This precludes repeated examinations over a short timescale to assess density change. We assess whether density can be accurately estimated in low dose mammograms with one-tenth of the standard dose, with the aim of providing a safe and effective method for use in younger women which is suitable for serial density measurement. We present analysis of data from an on-going clinical trial in which both standard and low dose mammograms are acquired under the same compression. We used both an existing convolutional neural network model designed to estimate breast density and a new model developed using a transfer learning approach. We then applied three methods to estimate density on the low dose mammograms: training on a different mammogram dataset; using simulated low dose data; and training directly on low dose mammograms using cross-validation. Pearson correlation coefficients between measurements on full dose and low dose mammograms ranged from 0.92 to 0.98 with the root mean squared error ranging between 3.37 and 7.27. Our results indicate that accurate density measurements can be made using low dose mammograms.
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