Using artificial intelligence to support efficient same-day diagnostic imaging in breast cancer screening

Breast cancer is the most common solid cancer and the second leading cause of cancer death among U.S. women. Multiple studies have shown that screening mammography decreases breast cancer-related mortality, but the implementation of screening has inefficiencies and limitations that contribute to potential harms. False-

positive results and wait times for further evaluation are well-documented mammographic harms. Approximately 10% of all screening exams are recalled for diagnostic workup, of which 95% are found to be false positives, potentially resulting in benign biopsies and overdiagnosis. The percentage of women recalled for further

diagnostic workup varies between 8-14%, depending on the radiologist. Moreover, the anxiety experienced by a woman with a recent abnormal mammogram is significant. Many women sleep poorly and struggle to focus as they await a more definitive diagnostic workup. Given these limitations of screening, our overarching goal is to

minimize practice variabilities associated with recalls, reduce patient anxiety, and increase patient satisfaction. We propose to assess the feasibility and effect of introducing artificial intelligence (AI) solution at the point of care to (1) reduce the overall callback rate, (2) increase patient satisfaction by providing immediate screening

results, and for women who require further diagnostic workup, (3) eliminate the delay between screening and diagnostic workup. The AI solution enables immediate “online” interpretation of screening exams in a high- volume breast screening program. For women with abnormal mammograms, real-time interpretation of the

screening exam permits women to be scheduled for a diagnostic exam on the same day. This goal is accomplished in three aims. Aim 1 will validate and integrate an AI algorithm to triage screening mammograms within our institution’s breast screening population. We will ensure that the algorithm performs at an expected

level (i.e., non-inferior to existing radiologist performance) and integrate and refine the algorithm to communicate results clearly and efficiently to target users. Aim 2 will design and assess an AI-enabled workflow for same-day diagnostic exams. We will analyze the current state of care, identify impediments to implementing this program,

and develop changes to the care pathway to allow an AI intervention. In Aim 3, we will implement and evaluate the impacts of an AI-enabled same-day diagnostic imaging paradigm in three stages: (1) a pilot stage, involving a subset of women undergoing screening using 2D screening mammography at a single site; (2) an

implementation stage, involving a larger group of women undergoing 2D and 3D screening mammography at a single imaging center; and (3) an expansion stage, involving women being screened at a second imaging center. UCLA Health is a unique environment to evaluate this paradigm given the large number of screening exams

performed annually (>40,000 exams) and the distributed nature of its breast screening program across twelve geographically separated imaging centers. The expected outcome of this project is a generalizable approach for evaluating and integrating AI algorithms to effect improvements in care delivery.