Deep learning in breast imaging

Abstract

Millions of breast imaging exams are performed each year in an effort to reduce morbidity and mortality rates of breast cancer. Breast imaging exams are mainly performed for cancer screening, diagnostic workup of suspicious findings, evaluating extent of disease in recently diagnosed breast cancer patients, and determining treatment response. Yet, the interpretation of breast imaging can be subjective, tedious, time-consuming, and prone to human error. Retrospective and small reader studies suggest that deep learning (DL) has great potential to perform medical imaging tasks at or above human-level performance, and may be used to automate aspects of the breast cancer screening process, improve cancer detection rates, decrease unnecessary callbacks and biopsies, optimize patient risk assessment, and open up new possibilities for disease prognostication. Prospective trials are urgently needed to validate these proposed tools, and to evaluate whether they are ready for real-world clinical use. Additionally, new regulatory frameworks are needed to address the unique ethical, medicolegal, and quality control issues that DL algorithms present. In this article, we review the basics of DL, describe recent DL breast imaging applications including cancer detection and risk prediction, and finally conclude by discussing the challenges and future directions of artificial intelligence-based systems in the field of breast cancer.

Artificial Intelligence-Enhanced Breast MRI

“…Handcrafted radiomics analysis coupled with ML extracts imaging features, which are then used to identify a phenotypical fingerprint or “radiomics signature.” In contrast, DL uses a complex network inspired by the human brain architecture to devise its features 43,44 . The typical radiomics and ML workflow starts with the input of the image of interest.…”

Genetic Factors in the Screening and Imaging for Breast Cancer

“…Multiple DL-based models, including Hybrid DL and Image-Only DL, were developed. A recent and promising model is Mirai [ 29 ].…”

Synergizing photon-counting CT with deep learning: potential enhancements in medical imaging

“…Deep learning algorithms can be employed to predict clinical outcomes and monitor treatment responses in medical imaging (29,30). For example, deep learning models can be trained to predict the likelihood of malignancy in lung nodules or the risk of recurrence in patients with breast cancer based on imaging features (29,30).…”