News
AI model could predict breast cancer risk without racial bias, study finds
The deep learning model has been shown to outperform traditional risk models in predicting a woman’s risk of developing breast cancer

A new AI model developed using mammogram image biomarkers could accurately predict both invasive and non-invasive forms of breast cancer without racial bias, new research has shown.
The findings, presented at the annual meeting of the Radiological Society of North America (RSNA), have suggested the model showed no bias across multiple races.
Traditional breast cancer risk assessment models use information obtained from patient questionnaires, such as medical and reproductive history, to calculate a patient’s future risk of developing breast cancer.
Data shows old models are more likely to demonstrate poor performance across different patient races, most likely due to the data used to develop the model.
“In the domain of precision medicine, risk-based screening has been elusive because we have not been able to accurately evaluate a woman’s risk of developing breast cancer,” said study lead author Leslie R. Lamb, a breast radiologist at Massachusetts General Hospital (MGH) in Boston.
“Even the best existing traditional risk models do not perform well on the individual level.
“Traditional models likely have racial biases due to the populations on which they were developed. Several of the commonly used models were developed on predominantly European Caucasian populations.”
According to the American Cancer Society, Black women demonstrate the lowest five-year relative survival rate for breast cancer among all racial and ethnic groups. This translates to a six per cent to eight per cent disparity in five-year survival rates between Black and white women across all breast cancer types.
The new deep learning AI risk assessment model developed using mammographic images has been shown to outperform traditional risk assessment models in future breast cancer development while also mitigating the racial biases seen in traditional models.
In the first study of its kind, Dr Lamb and colleagues assessed the performance of the image-based deep learning risk assessment model in predicting both future invasive breast cancer and non-invasive breast cancer across multiple races.
The study included 129,340 routine screening mammograms performed in 71,479 women between 2009 to 2018 with five-year follow-up data. Patient demographics were obtained from electronic medical records and instances of cancer were identified from the regional tumour registry.
The racial makeup of the study group included white, Black, Asian, self-reported and other races. The mean age of the women was 59 years old.
The deep learning model was shown to outperform traditional risk models in predicting a woman’s risk of developing early-stage, non-invasive breast cancer, also known as ductal carcinoma in situ (DCIS), as well as invasive breast cancer, known as invasive carcinoma.
“The model is able to translate the full diversity of subtle imaging biomarkers in the mammogram, beyond what the naked eye can see, that can predict a woman’s future risk of both DCIS and invasive breast cancer,” explained Lamb.
“The deep learning image-only risk model can provide increased access to more accurate, equitable and less costly risk assessment.”
Constance D. Lehman, senior author and breast radiologist at MGH, said: “This is a particularly exciting domain for AI, as it demonstrates the opportunity to apply ‘AI for good’—to reduce well-known racial disparities in risk assessment.
“We are now poised to translate these findings into improved clinical care for our patients.”
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