Artificial Intelligence-Driven Mammography-Based Future Breast Cancer Risk Prediction: A Systematic Review

Authors:
raw_author_name: Ojas A. Ramwala
raw_affiliation_string: Department of Biomedical Informatics and Medical Education, University of Washington School of Medicine, Seattle, Washington
raw_author_name: Kathryn P. Lowry
raw_affiliation_string: Department of Radiology, University of Washington School of Medicine, Seattle, Washington
raw_author_name: Solveig Hofvind
raw_affiliation_string: Section Head of Breast Cancer Screening, Cancer Registry of Norway, Oslo, Norway
raw_author_name: M. Luke Marinovich
raw_affiliation_string: The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
raw_author_name: Nehmat Houssami
raw_affiliation_string: National Breast Cancer Foundation Chair in Breast Cancer Prevention at the University of Sydney and Coeditor of The Breast; The Daffodil Centre, the University of Sydney, a joint venture with Cancer Council NSW, Sydney, New South Wales, Australia
raw_author_name: Joann G. Elmore
raw_affiliation_string: David Geffen School of Medicine at University of California at Los Angeles, Los Angeles, California; Director of UCLA’s National Clinician Scholars Program and Editor-in-Chief of Adult Primary Care at Up-To-Date
raw_author_name: Janie M. Lee
raw_affiliation_string: Director of Breast Imaging at Fred Hutchinson Cancer Center; Section Chief of Breast Imaging, Department of Radiology, University of Washington School of Medicine, Seattle, Washington
raw_author_name: Christoph I. Lee
raw_affiliation_string: Department of Radiology, University of Washington School of Medicine, Seattle, Washington, and Department of Health Systems & Population Health, University of Washington School of Public Health, Seattle, WA; Director of the Northwest Screening and Cancer Outcomes Research Enterprise at the University of Washington and Deputy Editor of Journal of the American College of Radiology

Abstract: To summarize the literature regarding the performance of mammography-image based artificial intelligence (AI) algorithms, with and without additional clinical data, for future breast cancer risk prediction.A systematic literature review was performed using six databases (medRixiv, bioRxiv, Embase, Engineer Village, IEEE Xplore, and PubMed) from 2012 through September 30, 2022. Studies were included if they used real-world screening mammography examinations to validate AI algorithms for future risk prediction based on images alone or in combination with clinical risk factors. The quality of studies was assessed, and predictive accuracy was recorded as the area under the receiver operating characteristic curve (AUC).Sixteen studies met inclusion and exclusion criteria, of which 14 studies provided AUC values. The median AUC performance of AI image-only models was 0.72 (range 0.62-0.90) compared with 0.61 for breast density or clinical risk factor-based tools (range 0.54-0.69). Of the seven studies that compared AI image-only performance directly to combined image + clinical risk factor performance, six demonstrated no significant improvement, and one study demonstrated increased improvement.Early efforts for predicting future breast cancer risk based on mammography images alone demonstrate comparable or better accuracy to traditional risk tools with little or no improvement when adding clinical risk factor data. Transitioning from clinical risk factor-based to AI image-based risk models may lead to more accurate, personalized risk-based screening approaches.