London: A team of British researchers has developed a new model that reliably predicts a woman's likelihood of developing and then dying of breast cancer within a decade.
The study, published in The Lancet Digital Health, analysed anonymised data from 11.6 million women aged 20-90 from 2000 to 2020. All of these women had no prior history of breast cancer, or the precancerous condition called 'ductal carcinoma in situ', or DCIS.
Identifying women at the highest risk of deadly cancers could improve screening. These women could be invited to start screening earlier, be invited for more frequent screenings, or be screened with different types of imaging. Such a personalised approach could further lower breast cancer deaths while avoiding unnecessary screening for lower-risk women. Women at higher risk for developing a deadly cancer could also be considered for treatments that try to prevent breast cancer from developing, said researchers at the University of Oxford.
"This is an important new study which potentially offers a new approach to screening. Risk-based strategies could offer a better balance of benefits and harms in breast cancer screening, enabling more personalised information for women to help improve decision-making. Risk-based approaches can also help make more efficient use of health service resources by targeting interventions to those most likely to benefit," said Julia Hippisley-Cox, Professor of Epidemiology at the varsity.
The researchers tested four different modelling techniques to predict breast cancer mortality risk. Two were more traditional statistical-based models and two used machine learning, a form of artificial intelligence. All models included the same types of data, like a woman’s age, weight, history of smoking, family history of breast cancer, and use of hormone therapy (HRT). The models were evaluated for their ability to predict risk accurately overall and across a diverse range of groups of women, such as from different ethnic backgrounds and age groups.
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A technique called 'internal-external cross-validation' was used. This involves splitting the dataset into structurally different parts, in this case, by region and time period, to understand how well the model might transport into different settings. The results showed that one statistical model, developed using 'competing risks regression' performed the best overall. It most accurately predicted which women will develop and die from breast cancer within 10 years.
The machine learning models were less accurate, especially for different ethnic groups of women. "If further studies confirm the accuracy of this new model, it could be used to identify women at high risk of deadly breast cancers who may benefit from improved screening and preventative treatment," the team said. (IANS)
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