Title: An Artificial Intelligence System for Real-time Risk Assessment at Mammography Screening
Funder: Cancer Research UK

Abstract: Background Risk-adapted screening for breast cancer, in which both the frequency with which women are screened and the modality are chosen based on risk of breast cancer, is likely to be crucial for screening to remain a sustainable strategy for breast cancer control. Aims Our project will determine whether an artifical intelligence (AI) system to aid risk-stratified screening may do better than current methods to assess (1) risk of cancer at screening (detection), (2) risk of masking due to breast density (false negative screening result), and (3) long-term risk of breast cancer following a negative screen (risk assessment). We will use data already been collected by other researchers and build software tools to develop and test AI tools. The algorithms will be externally validated in studies that were originally designed to assess breast cancer risk assessment methods. The complete package will be made publicly available. Methods (1) Aggregate and curate a large database of screening mammograms from public sources and collaborating partners in order to develop (train) the tools; including more than 19,000 women with breast cancer, and more than 500,000 women without breast cancer from the UK, US and Russia. (2) Develop an AI (computer vision) algorithm for real-time detection of breast cancer in mammograms, based on state-of-the-art algorithms, including convolutional neural nets. (3) Develop an AI algorithm for residual lifetime risk of breast cancer in women without breast cancer at their screening visit, based on comparing the difference between the contralateral breast in cases with a randomly selected breast from controls. (4) Develop an AI algorithm to identify which mammograms are at a higher risk of failure of the screening test, by comparing characteristic of interval and screen-detected breast cancers. (5) Software development, including seamless integration with the PACS. (6) Externally validate the complete package in studies from the UK, Sweden and US, in which other risk factors are available and the ability of the algorithm to add to current methods may be determined; and to compare the algorithms with those from collaborators and others. How the results of the research will be used Results will be made available through a tested system for real-time risk assessment of all three risk domains at screening, which can be easily deployed in the clinic without requiring specialised IT infrastructure. It is intended that our findings will enable the design of new risk-adapted screening programs.

Lay abstract: Background Risk-adapted screening for breast cancer, in which both the frequency with which women are screened and the modality are chosen based on risk of breast cancer, is likely to be crucial for screening to remain a sustainable strategy for breast cancer control. Aims Our project will determine whether an artifical intelligence (AI) system to aid risk-stratified screening may do better than current methods to assess (1) risk of cancer at screening (detection), (2) risk of masking due to breast density (false negative screening result), and (3) long-term risk of breast cancer following a negative screen (risk assessment). We will use data already been collected by other researchers and build software tools to develop and test AI tools. The algorithms will be externally validated in studies that were originally designed to assess breast cancer risk assessment methods. The complete package will be made publicly available. Methods (1) Aggregate and curate a large database of screening mammograms from public sources and collaborating partners in order to develop (train) the tools; including more than 19,000 women with breast cancer, and more than 500,000 women without breast cancer from the UK, US and Russia. (2) Develop an AI (computer vision) algorithm for real-time detection of breast cancer in mammograms, based on state-of-the-art algorithms, including convolutional neural nets. (3) Develop an AI algorithm for residual lifetime risk of breast cancer in women without breast cancer at their screening visit, based on comparing the difference between the contralateral breast in cases with a randomly selected breast from controls. (4) Develop an AI algorithm to identify which mammograms are at a higher risk of failure of the screening test, by comparing characteristic of interval and screen-detected breast cancers. (5) Software development, including seamless integration with the PACS. (6) Externally validate the complete package in studies from the UK, Sweden and US, in which other risk factors are available and the ability of the algorithm to add to current methods may be determined; and to compare the algorithms with those from collaborators and others. How the results of the research will be used Results will be made available through a tested system for real-time risk assessment of all three risk domains at screening, which can be easily deployed in the clinic without requiring specialised IT infrastructure. It is intended that our findings will enable the design of new risk-adapted screening programs.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: CANCER AND NEOPLASMS