Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction - Jones, Meredith A.; Islam, Warid; Faiz, Rozwat; Chen, Xuxin; Zheng, Bin (Frontiers in Oncology, 2022)
Breast cancer remains the most diagnosed cancer in women. Advances in medical imaging modalities and technologies have greatly aided in the early detection of breast cancer and the decline of patient mortality rates. However, reading and interpreting breast images remains difficult due to the high heterogeneity of breast tumors and fibro-glandular tissue, which results in lower cancer detection sensitivity and specificity and large inter-reader variability. In order to help overcome these clinical challenges, researchers have made great efforts to develop computer-aided detection and/or diagnosis (CAD) schemes of breast images to provide radiologists with decision-making support tools. Recent rapid advances in high throughput data analysis methods and artificial intelligence (AI) technologies in the medical imaging field has led to an exponential increase of research interest in the development of new radiomics and AI-based CAD schemes of breast images for a broad range of topics. In this review paper, we focus on reviewing recent advances in better understanding the association between radiomics features and tumor microenvironment and the progress in developing new CAD schemes in three realms of breast cancer: predicting breast cancer risk, the likelihood of tumor malignancy, and tumor response to treatment. The outlook and three major challenges of applying new AI-based CAD schemes to clinical practice are also discussed. Through this review we conclude that although developing new AI-based CAD schemes of breast images has achieved significant progress and promising results, several obstacles to applying these new CAD schemes to clinical practice remain. Therefore, more research effort is needed in future studies.