Hidden clues in the mammogram: How AI can improve early breast cancer detection
Authors: C. Lehman, A. Yala, L. Lamb, R. Barzilay
Journal: Cancer Research (2021)

Innovative methods of risk assessment that leverage the strength of Artificial Intelligence (AI) are essential to propelthe goals of precision prevention forward  Since the creation of the Gail model in 1989, risk models have supportedrisk-adjusted screening and prevention, and their continued evolution has been a central pillar of breast cancerresearch  Prior research has explored multiple risk factors related to hormonal and genetic information  One factorthat has received substantial attention is mammographic breast density  Incorporating mammographic breastdensity into clinically used models such as the Gail and Tyrer-Cuzick risk models significantly improves prediction and discrimination  However, current risk models are limited in that they incorporate only a small fraction of dataavailable on any given patient  Using breast density as a proxy for the detailed information embedded in themammogram is extremely limited, as breast density assessment is subjective, varies widely across radiologists, andrestricts the rich information contained in the digital images to a single crude value  Patients of the same ageassigned the same density score can have mammogram images that appear drastically different and can have verydifferent future risk profiles  While previous studies have explored automated methods to assess breast density,these efforts reduce the complex data contained in the mammogram into a few statistics, which are not sufficientlyrich to distinguish patients who will and will not develop breast cancer  Deep learning models can operate over fullresolution mammogram images to assess a patient's future breast cancer risk  Rather than manually identifyingdiscriminative image patterns, machine learning models can discover these patterns directly from the data Specifically, models are trained with full resolution mammograms and the outcome of interest, namely whether the patient developed breast cancer with in five years from the date of the examination  Our recent work demonstratesthat application of novel artificial intelligence applications to imaging data can significantly improve breast cancerrisk prediction  In addition, unlike traditional models, our DL model performs equally well across varied races, ages,and family histories and we have built a clinical platform which is currently in use to support implementation of ourrisk model into clinical care  The COVID-19 pandemic has revealed severe inequities in healthcare while providingopportunities for essential reform  In breast cancer care, preliminary, conservative estimates predict the disruption of breast cancer screening due to the COVID-19 pandemic will result in a significant upward stage shift of cancersdiagnosed and more than 5,000 breast cancer deaths in the U S  alone  Due to severely limited healthcare resources during pandemics, and to protect patients and healthcare workers,state governments urge providers to focus cancer screening efforts on those patients at higher risk  Thesemandates are necessary responses to support fair allocation of scarce resources to maximize benefits for allpatients across the full spectrum of healthcare needs  AI-based breast cancer risk models have the potential tosupport more effective and more equitable mammographic screening for breast cancer during these times of severely restricted access to screening