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AI model predicts breast cancer risk without racial bias

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A deep learning AI model that was developed using only mammogram image biomarkers accurately predicted both ductal carcinoma in situ (DCIS) and invasive carcinoma, according to a new study.

The model also 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 an individual’s future risk of developing breast cancer.

Study lead author Leslie R. Lamb, M.D., M.Sc., a breast radiologist at Massachusetts General Hospital (MGH) in Boston, said:

“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.

“Even the best existing traditional risk models do not perform well on the individual level.”

Traditional risk models have also demonstrated poor performance across different patient races, most likely due to the data used to develop the models.

According to the American Cancer Society, Black women demonstrate the lowest 5-year relative survival rate for breast cancer among all racial and ethnic groups in the US.

This translates to a persistent 6 per cent to 8 per cent disparity in five-year survival rates between Black and white women across all breast cancer types.

To accurately determine breast cancer risk, foster early detection and improve patient survival rates, it is important that risk models are created that are applicable across different populations.

In the first study of its kind, Dr Lamb and colleagues sought to assess the performance of an image-based deep learning risk assessment model in predicting both future invasive breast cancer and DCIS across multiple races.

The performance of the model was assessed by comparing areas under the receiver operating characteristic curve (AUC) with the DeLong test.

The AUC score measures the predictive rate of the model on a scale of from 0 to 1.

Multiple previous studies have estimated traditional risk model performance measured by AUC in the range of 0.59-0.62 for white women, with much lower performance in women of other races.

The multisite study included a total of 129,340 routine bilateral 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 (106,839 exams), Black (6,154 exams), Asian (6,435 exams), self-reported other races (6,257 exams) and unknown (3,655 exams).

The women had a mean age of 59 years old.

The deep learning model consistently outperformed traditional risk models in predicting the risk of developing DCIS, which is early-stage breast cancer, and invasive breast cancer, which is cancer that has potential to spread.

Dr Lamb said: “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.

“The deep learning image-only risk model can provide increased access to more accurate, equitable and less costly risk assessment.”

The predictive rate of both DCIS and invasive cancer was 0.71 across all races.

The AUC in predicting DCIS was 0.77 in non-white patients and 0.71 in white patients.

The AUC in predicting invasive cancer was 0.72 in non-white patients and 0.71 in white patients.

Senior author Constance D. Lehman, M.D., Ph.D., a 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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