[Worldkings] Top 200 breakthrough research works in the world (P. 44) University of Oxford develops a new model capable of predicting 10-year breast cancer risk (UK)


(Worldkings.org) Researchers at the University of Oxford have developed a new model that predicts a woman's likelihood of developing and dying of breast cancer within ten years.

A team of researchers at the University of Oxford, led by the Nuffield Department of Primary Care Health Sciences, have 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 today 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.

Breast cancer screening is vital but has challenges. While it reduces breast cancer deaths, it sometimes detects tumours that are not harmful (‘overdiagnosis’), which leads to unnecessary treatments. This not only harms some women, but also causes unnecessary costs to the NHS. For every 10,000 UK women aged 50 years invited to breast screening for the next 20 years, 43 breast cancer deaths are prevented by screening, but 129 women will be ‘overdiagnosed’.

The new model developed by the team works to predict a woman’s 10-year combined risk of developing and then dying from breast cancer. 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.

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. 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 predicts 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.

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 cancers developing.


According to ox.ac.uk

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