3 QUESTIONS ON . . . Using Artificial Intelligence in Breast Cancer Risk Prediction

Celine Vachon, PhD: Celine Vachon, PhDA rtificial intelligence (AI) technology is making headlines in many areas of research and sectors of business. Now, epidemiologists and others who study cancer screening methodology say they've found new evidence it could improve breast cancer detection efforts, too. In a study published in the Journal of Clinical Oncology (JCO), researchers found that using an AI algorithm designed to improve breast cancer detection on mammograms could improve breast cancer risk prediction, including interval and advanced cancers—particularly when combined with consideration of breast density (2023; doi: 10.1200/JCO.22.01153). “This adds further evidence to support computer-aided imaging assessments and will help motivate prospective studies to disentangle improved earlier diagnosis from an improved risk model for subsequent cancer,” Paul H. Frankel, PhD, JCO Associate Editor, wrote in a relevance statement published with the article. “Such prospective studies will also provide estimates that can more easily guide clinical practice.” In an interview with Oncology Times, the study's lead author, Celine Vachon, PhD, Professor and Consultant in the Division of Epidemiology within the Department of Quantitative Health Sciences at Mayo Clinic, shared more about the work. 1 What were the key findings from this research? “We evaluated whether the AI measure assessed on mammograms taken years prior to the cancer (2-5.5 years prior to the cancer diagnosis) contributed additional information to breast density for risk of breast cancer—in particular interval and advanced cancers. The AI algorithm was developed to be used on all women, not just those with dense breasts. It was run on the mammograms from women in our clinical practices who did and did not develop breast cancer. “AI score assessed on mammograms 2-5.5 years before cancer diagnosis was associated with invasive breast cancer, and similarly predictive of interval and advanced cancers in dense and non-dense breasts. These findings imply that the AI score is robust for long-term risk prediction by severity of cancer and independent of breast density for predicting cancer. “Furthermore, the AI score improved prediction of all cancer types in models with breast density measures. AI imaging algorithms coupled with breast density independently contribute to long-term risk prediction of invasive breast cancers, including advanced cancers.” 2 What are the implications of these findings and how might they improve real-world screening efforts? “Data from our study and others suggests that AI algorithms will help improve both breast cancer detection and breast cancer risk prediction, including interval cancers and advanced cancers. These will likely help better identify women for supplemental screening and, potentially, for identification of women who would benefit from early interventions. “The AI algorithm we used is commercially available. And there are several similar AI algorithms from multiple vendors available that likely would provide similar findings. However, it will be important to understand how these algorithms work among women from various races, BMI (those with obesity, for example), and how they contribute to factors already known for risk prediction (such as genetic factors). Also, these algorithms need to be able to identify prognostically poor cancers, not just DCIS or early cancers that may not progress. This research is underway by many groups to inform how best to use these algorithms in the clinical setting.” 3 What is the next step of this work? “We are pursuing additional research to understand the imaging factors that determine the high-risk score, including calcifications. We are also examining other AI models, in particular those designed for longer-term risk, and how the combination of these algorithms with genetic factors and breast density can better inform future risk. The bottom line that practicing oncologists and cancer care providers need to know now is that this research confirms the importance of both breast density and AI models to predict invasive breast cancer, including both interval and advanced cancers. Our data suggest that this AI algorithm is not only relevant at the time of cancer [for detection], but also 2-5.5 years prior to the breast cancer.”