AI-based BRAIx risk score for the intermediate-term prediction of breast cancer: a population cohort study.

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Abstract

Artificial intelligence (AI)-based algorithms are being implemented in breast screening to detect breast cancers on mammographic images. We aimed to apply an epidemiological approach to demonstrate how a cancer detection algorithm can be leveraged as an intermediate-term predictor of breast cancer (current and 4-year risk) to deliver greater risk-based personalisation in screening mammography. In this population cohort study, we used detection scores from an AI cancer detection algorithm (BRAIx AI Reader), which was calibrated using a training dataset of 397 648 women aged 40 years to 97 years from women who screened at BreastScreen Victoria, Australia between Jan 1, 2016, and Dec 31, 2017, to create a woman-specific mammography-based score for breast cancer risk, the BRAIx risk score. Subsequently, the BRAIx risk score was evaluated on an independent test dataset of women from BreastScreen Victoria, Australia, comprising a random population cohort of 96 348 women who screened from Jan 1, 2016, to Dec 31, 2017, aged 40 years to 74 years, and an independent, external dataset from woman screened at Karolinska University Hospital, Stockholm, Sweden. We applied logistic regression, using the BRAIx risk score to estimate risks of invasive breast cancers on the test dataset: (1) detected at cohort entry (n=525); and (2) for women given an all clear, diagnosed during the next 4 years either at future screens (n=790) or during intervals between screens (n=308). We also trained full multivariate risk models (logistic regression and elastic net) using the training dataset and evaluated their predictive performance on the test and external validation data, with assessment of familial aspects of the BRAIx risk score achieved with inference about causation from examining changes in regression coefficients in an innovative statistical analysis framework. In both Australian and Swedish test datasets, the BRAIx risk score predicted cancer detection at cohort entry and future cancer risk (all p<0·0001). The BRAIx risk score was the strongest tested explanatory factor for cancer detection at cohort entry (odds ratio 13·80 [95% CI 9·54-20·80] in Australian data; 8·89 [3·19-37·49] in Swedish data) and for intermediate-term cancer risk (2·29 [2·13-2.47] in Australian data; 2·15 [1·85-2·50] in Swedish data). We found that adding a thresholded binary version of the BRAIx risk score significantly improved model fit (p<2·2 × 10^-16, Australian and Swedish data) and women with BRAIx risk scores of more than 2 were significantly at many-fold increased risk of intermediate-term cancer than women below that threshold (12·34 [7·33-20·91], Australia; 44·7 [11·9-184·9], Sweden; p<0·0001). For the top 2% of women given an all clear with the highest BRAIx risk score, the probability of a cancer diagnosis within 4 years was 9·7%. The BRAIx risk score explained 23% of why family history predicts 4-year risk (p<0·0001). After fitting the BRAIx risk score in a multivariate model, mammographic density was no longer significantly associated with breast cancer risk in the Australian test data (p>0·05) and became associated with lower risk for intermediate-term cancer in the external Swedish test dataset (0·83 [0·73-0·95]). The BRAIx risk score is a strong intermediate-term predictor of breast cancer (current to 4-year risk). Calibrating the score on a training dataset produces population-specific probabilities for calculating individual-specific risk scores for screening clients based on their mammogram images. These risk scores enable future development of personalised screening pathways to transform population breast cancer screening and save lives. Identification of women given an all clear but at very high risk, similar to those carrying BRCA1 and BRCA2 mutations, could reveal insights into both familial and non-familial causes of breast cancer. Australian Government Medical Research Future Fund, the Ramaciotti Foundation, the National Breast Cancer Foundation, Cancer Australia, and the National Health and Medical Research Council.

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