Personalized Breast Screening a Step Closer to Reality

A new model for predicting breast cancer risk that is based on standard clinical measures could be used to develop personalized breast screening strategies that are more effective than the current “one size fits all” approach, say researchers.

“Several breast cancer risk prediction models have been created, but we believe this is one of the first models designed to guide breast screening strategies over a person’s lifetime using real data from a screening program,” said study author Javier Louro, PhD, Hospital del Mar Medical Research Institute, Barcelona, Spain.

“Our model might be considered a key for designing personalized screening aimed at reducing the harms and increasing the benefits of mammographic screening,” he said in a statement.

Someone with a low risk “might be offered screening with standard mammography every 3 or 4 years instead of 2 years,” Louro explained.

“Someone with medium risk might be offered screening with advanced 3D mammography every 3 years, while those at a high risk might be offered a new screening test with mammography or MRI every year.”

However, he cautioned that “all of these strategies are still theoretical and should be studied with regard to their effectiveness.”

Louro was talking about the new model at the 13th European Breast Cancer Conference (EBCC 13) on November 16.

Details of the New Prediction Model

To develop the new model, Louro and colleagues conducted a retrospective study of 57,411 women who underwent mammography in four counties in Norway between 2007 and 2019 as part of the BreastScreen Norway program, and followed them up to 2022.

The team gathered data on age, breast density, family history of breast cancer, body mass index, age at menarche, alcohol habit, exercise, pregnancy, hormone replacement therapy, and benign breast disease, and compared women with and those without a breast cancer diagnosis.

All of these 10 variables used were found to significantly explain part of the variability in the breast cancer risk.

Overall, the 4-year breast cancer risk predicted by the resulting model varied across the participants, from 0.22% to 7.43%, at a median of 1.10%.

Bootstrap resampling analysis revealed that the model overestimated the risk for breast cancer, at an expected-to-observed ratio of 1.10.

The largest effect on risk was from breast density on mammography. Women with dense breasts were at much higher risk: the adjusted hazard ratio was 1.71 for women with Volpara Density Grade 4 vs Grade 2 and was 1.37 when compared with Grade 3.

Exercise had a large impact on breast cancer risk, the researchers found. Women who exercised for 4 or more hours per week had an adjusted hazard ratio of 0.65 for breast cancer risk compared with women who never exercised. Although this effect of exercise reducing the risk for breast cancer is now widely known, it is not usually included in models that predict breast cancer risk, the team pointed out.

The team concluded that their prediction model could be used to personalize breast screening for women according to their risk assessment, although they acknowledge that more work is needed. This work is based on one screening program in one country, and similar studies in different settings are needed.

Reacting to the findings, Laura Biganzoli, MD, co-chair of the European Breast Cancer Conference and director of the Breast Centre at Santo Stefano Hospital, Prato, Italy, commented, “We know that breast screening programs are beneficial, but we also know that some people will experience potential harms caused by false-positives or overdiagnosis.”

“This research shows how we might be able to identify people with a high risk of breast cancer, but equally how we could identify those with a low risk. So it’s an important step toward personalized screening,” Biganzoli said.

This study was supported by a grant from Instituto de Salud Carlos III FEDER (grant PI/00047). No relevant financial relationships declared.

13th European Breast Cancer Conference (EBCC 13). Abstract 22. Presented November 16, 2022.

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