A recent study published in Nature Public Health Emergency Collection by French researchers Mahasti Saghatchian et. al. has revealed that an individual's 5-year risk of developing breast cancer can be predicted using MammoRisk.This software tool should aid in early diagnosis and proper treatment of breast cancer, the study mentions.
Most European countries have reported that in the past 20 years breast cancer-related death numbers have dwindled. In a report jointly drafted by Cancer Research UK and the Department of Health (England) October 2012, the credits for the same are given to Mammographic screening.

In an average-risk woman, the chances of developing breast cancer depends on various risk factors like:
mammographic breast density
family history of breast or ovarian cancer
history of benign breast disease
endocrine exposures
lifestyle patterns
A risk assessment clinic was initiated by the researchers for all women who volunteered to undergo an individual breast cancer risk assessment.
They underwent a complete breast cancer assessment including a questionnaire, mammogram which evaluated breast density, collection of saliva samples, radiologist consultation, and a breast cancer specialist. MammoRisk is a machine learning-based tool that provides an individual 5-year estimated risk of developing breast cancer based on the patient's breast density and clinical data, irrespective of the presence of Polygenic risk scores. The saliva samples were used to extract DNA to genotype 76 single-nucleotide polymorphisms. Possible risk, monitoring, screening, and prevention guidelines were communicated to them on an individual basis.
196 out of the 290 women who underwent this assessment were eligible to use MammoRisk for risk assessment. To the same when polygenic risk scores were added, 40% of patients changed to a different risk group and 28% changed from medium to high risk.
The researchers provided conclusive evidence stating that individual risk assessment stands feasible in the general population for breast cancer.
Read the full study here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8739506/