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.”
Cancer
Ovarian cancer cases rising among younger adults, study finds

Ovarian cancer cases are rising among younger adults in England, with bowel cancer showing a similar pattern, a new study suggests.
Researchers said excess weight is a key contributor, but is unlikely on its own to explain the pattern.
The authors wrote: “These patterns suggest that while similar risk factors across ages are likely, some cancers may have age-specific exposures, susceptibilities, or differences in screening and detection practices.”
They added: “Although overweight and obesity are linked to 10 of the 11 cancers evaluated and account for a substantial proportion of cancer cases, both BMI-attributable and BMI-non-attributable incidence rates have increased, though the latter more slowly, suggesting other contributors.”
The study analysed cancer incidence, meaning new diagnoses, in England between 2001 and 2019 across more than 20 cancer types, comparing adults aged 20 to 49 with those aged 50 and over.
Among younger women, cases of 16 out of 22 cancers increased significantly over the period, while among younger men, 11 out of 21 cancers increased significantly.
In particular, there was a significant rise in 11 cancers with known behavioural risk factors among adults under 50. These were thyroid, multiple myeloma, liver, kidney, gallbladder, bowel, pancreatic, endometrial, mouth, breast and ovarian cancers.
Rates of all 11 also rose significantly among adults aged 50 and over, with the notable exceptions of bowel and ovarian cancer.
Five cancers, endometrial, kidney, pancreatic, multiple myeloma and thyroid cancer, increased significantly faster in younger than in older women, while multiple myeloma increased faster in younger than in older men.
The researchers looked at established risk factors including smoking, alcohol intake, diet, physical inactivity and body mass index, a measure used to assess whether someone is underweight, a healthy weight, overweight or obese.
With the exception of mouth cancer, all 11 cancers were associated with obesity. Six, liver, bowel, mouth, pancreatic, kidney and ovarian, were also linked to smoking.
Four, liver, bowel, mouth and breast, were associated with alcohol intake. Three, bowel, breast and endometrial, were linked to physical inactivity, and one, bowel, was associated with dietary factors.
But apart from excess weight, trends in those risk factors over the past one to two decades were stable or improving among younger adults.
That suggests other factors may also play a part, including reproductive history, early-life or prenatal exposures, and changes in diagnosis and detection.
The study noted that red meat consumption fell among younger adults, while fibre intake remained stable or slightly improved in both sexes between 2009 and 2019, although more than 90 per cent of younger adults were still not eating enough fibre in 2018.
Established behavioural risk factors accounted for a substantial share of cancer cases.
Excess weight was the risk factor associated with most cancers in 2019, ranging from 5 per cent for ovarian cancer to 37 per cent for endometrial cancer.
The researchers said the findings were based on observational data, meaning the study could identify patterns but could not prove cause and effect.
They also noted there were no consistent long-term national data for several risk factors, that the analysis was limited to England rather than the UK, and that cancer remains far more common overall in older adults despite the rise in cases among younger people.
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