SUMMARY Magnetic Resonance Imaging (MRI) is the most sensitive imaging modality for breast cancer diagnosis to date. Women with a strong family history or related genetic mutations have an elevated risk of breast cancer and are recommended to participate in yearly MRI screenings. However, the rate of detection in this high-risk cohort is small, prompting a desire to reduce unnecessary MRI exams. The basic hypothesis of this project is that within the screening cohort the individual risk of a future cancer can be estimated based on the appearance of breast MRI and mammograms today. In preliminary work we have already identified low-risk women that could have omitted a screening session without missing a new cancer. The discovery of this lower-risk subgroup was made possible by modern deep-learning tools developed in preliminary work. Memorial Sloan Kettering Cancer Center (MSK) has accrued a database of approximately 70,000 breast MRI exams over 18 years along with the patients’ clinical outcomes. This unprecedented resource enables the training of modern machine learning “from the ground-up” to extract and classify volumetric MRI features. The specific aims of this project are as follows. Aim 1 (Data curation): Systematic analysis of the large dataset accrued at MSK requires careful curation including image content, image quality, pathology results, clinical follow-up, as well as demographic and genomic information. The outcome of this Aim is a curated dataset that can broadly benefit future technical efforts in breast diagnosis. Aim 2 (Deep learning): To make risk stratification quantitative we propose to analyze the MRI scans using modern deep networks that have been trained to identify the location and extent of a cancer. We will then transfer the MRI features of these trained networks as well as networks trained on mammograms to the task of diagnosis and risk assessment. The intended outcome of this Aim are predictive models with human-level performance at diagnosis and segmentation. Aim 3 (Risk adjusted screening): To reduce the burden of screening while maintaining sensitivity we will estimate the risk of finding a malignant tumor in the future, based on the present MRI exam and most recent mammogram as well as patient information. The machine-estimated risk will be used in a retrospective analysis to determine the primary outcome, namely, the number of exams that could have been omitted by scheduling a longer screening interval without compromising sensitivity. This will be repeated on newly accrued data at MSK, Duke and Johns Hopkins University (JHU) as secondary sites. Once validated, the risk-prediction model will be publicly released to encourage data sharing and clinical adoption. The preliminary work performed over the last two years has brought together a unique interdisciplinary team including clinical investigators on breast MRI at MSK, and machine-learning and medical imaging experts at CCNY, Duke and JHU. The platform technology that will be developed here is applicable beyond breast cancer, and the transfer learning approach applicable in particular to cancers with more limited datasets.

NARRATIVE The goal of this project is to detect breast cancer as early as possible while limiting the burden of screening in high-risk women. To this end, risk will be estimated from magnetic resonance images of the breast as well as mammograms using deep learning techniques. A retrospective analysis of a very large dataset will determine if some women could have avoided unnecessary scans without missing newly developing cancers.