Artificial intelligence in mammographic phenotyping of breast cancer risk: a narrative review

Abstract

Background: Improved breast cancer risk assessment models are needed to enable personalized screening strategies that achieve better harm-to-benefit ratio based on earlier detection and better breast cancer outcomes than existing screening guidelines. Computational mammographic phenotypes have demonstrated a promising role in breast cancer risk prediction. With the recent exponential growth of computational efficiency, the artificial intelligence (AI) revolution, driven by the introduction of deep learning, has expanded the utility of imaging in predictive models. Consequently, AI-based imaging-derived data has led to some of the most promising tools for precision breast cancer screening.

Main body: This review aims to synthesize the current state-of-the-art applications of AI in mammographic phenotyping of breast cancer risk. We discuss the fundamentals of AI and explore the computing advancements that have made AI-based image analysis essential in refining breast cancer risk assessment. Specifically, we discuss the use of data derived from digital mammography as well as digital breast tomosynthesis. Different aspects of breast cancer risk assessment are targeted including (a) robust and reproducible evaluations of breast density, a well-established breast cancer risk factor, (b) assessment of a woman's inherent breast cancer risk, and (c) identification of women who are likely to be diagnosed with breast cancers after a negative or routine screen due to masking or the rapid and aggressive growth of a tumor. Lastly, we discuss AI challenges unique to the computational analysis of mammographic imaging as well as future directions for this promising research field.

Conclusions: We provide a useful reference for AI researchers investigating image-based breast cancer risk assessment while indicating key priorities and challenges that, if properly addressed, could accelerate the implementation of AI-assisted risk stratification to future refine and individualize breast cancer screening strategies.

Keywords: Artificial intelligence; Breast cancer; Breast cancer risk; Breast density; Breast tomosynthesis; Deep learning; Digital mammography; Mammographic density; Mammographic imaging.

Fig. 1

Diagram explaining the relationship between the different techniques in the field of artificial intelligence

Fig. 2

AI-based BI-RADS density classification. A A visual display of the range of BI-RADS density classifications for AI models trained with different architectures and training parameters for 50 patients in the testing set. The radiologist interpretation is displayed in the first row. The average breast density rating across all models and radiologist interpretations is displayed in the last row and was used to order the patients from least dense (left) to most dense (right). B The distribution of predicted breast density labels in the testing set differed for experiments with random class sampling (left) compared with equal class sampling (right) at each minibatch. ****P < .001. E. dense = extremely dense; H. dense = heterogeneously dense [30]. [Reprinted with permission from Elsevier (License Number: 5138920035119)]

Fig. 3

Example of AI-enabled density segmentation map from FFDM (estimated breast percent density, PD = 47%)

Fig. 4

Use of the four standard mammographic views in long-term risk assessment via artificial intelligence [46]. [Reprinted with permission from The American Association for the Advancement of Science (License Number: 5138920821187)]