The Science behind our Technology
Identifying individuals at high breast cancer risk is critical for guiding medical decisions in terms of risk assessment, prediction, management and prevention, including screening, genetic counseling and testing, and preventative medical procedures.
In 2020, breast cancer was the most commonly diagnosed cancer type in the world, with more than 2.26 million new cases of breast cancer worldwide. Breast cancer remains the most commonly diagnosed cancer type among women in 157 countries out of 185 in 2022, according to the World Health Organization (WHO), and the most common cause of cancer death in women. It is the fourth most common cause of cancer death overall.
Over the past three decades, cancer incidence has doubled, ranking France ninth worldwide. This increase is particularly marked for breast cancer, for which France has overtaken countries such as Belgium, the Netherlands and Luxembourg, which were previously in the lead.
According to the International Agency for Research on Cancer (CIRC), France became the country with the highest incidence rate of breast cancer worldwide. Forecasts for 2050 indicate a worsening situation, with an estimated 595,000 cases of cancer and 261,000 deaths. Specifically for breast cancer, a 37% rise in deaths is anticipated, from 14,700 to 20,100.
The following table shows total global breast cancer incidence and rates in 2022 for women. France had the highest overall rate of breast cancer in 2022.
ASR = age-standardised rates
The following table shows total global breast cancer mortality in 2022 for women.
A lot of breast cancer risk prediction models have been developed over the past few decades. Many breast cancer risk models have undergone validation including discrimination and calibration in study populations other than those used in initial development, or have been further assessed in comparative studies. Breast cancer-related predictors including hormonal factors, environmental factors, family histories, genetic factors and radiographic factors have been based on in these risk models. Acknowledging the importance of early breast cancer detection and risk categorization, several models, including Gail, BCSC, Rosner–Colditz, and Tyrer–Cuzick, have been developed to predict breast cancer risk.
The Gail model, one of the most famous models, has been widely used and validated worldwide since it was developed in 1989.
The BOADICEA model is an algorithm developed to estimate a woman’s individual risk of breast cancer based on several factors. These include variants in breast cancer susceptibility genes, PRS scores, family history, mammographic density, sex and age, demographic factors, tumor pathology and questionnaire-based factors. This model helps to stratify women at low and high risk of developing breast cancer and holds potential for assisting in early disease management and care.
Breast cancer screenings and early detection are vital for reducing the mortality rate.
Mammography is currently the primary mode of screening for average-risk women, yet it can miss up to 20% of cases. Ultrasound is typically included as a diagnostic follow-up measure, only after a clinical examination and mammography. Ultrasound is also increasingly used as a supplemental screening tool, particularly in women with dense breasts, where mammographic sensitivity is limited. In order for ultrasound screening to be widely implemented, both the sensitivity and specificity of sonography must be improved. Recent studies have estimated the sensitivity of US to be 75%, 81%, and 83%, depending on patient population. Currently, the specificity of ultrasound in breast cancer diagnosis is also low, and between 70% and 85% of breast biopsies prove to be benign. These unnecessary biopsies result in both emotional and physical trauma to the patient as well as in socioeconomic costs on a wider scale.
