Applying artificial intelligence technology to assist with breast cancer diagnosis and prognosis prediction

Abstract

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, particularly radiomics and deep learning techniques, have led to an exponential increase in the development of new AI-based models of breast images that cover a broad range of application 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 AI-based quantitative image feature analysis models 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 models of breast images to clinical practice are also discussed. Through this review we conclude that although developing new AI-based models of breast images has achieved significant progress and promising results, several obstacles to applying these new AI-based models to clinical practice remain. Therefore, more research effort is needed in future studies.

Keywords: breast cancer; computer aided detection; computer aided diagnosis; deep learning; machine learning; mammography.

Figure 1

Examples of benign and malignant masses seen on mammograms. Modified from (58).

Figure 2

Results of mapping radiomic features extracted from DCE-MRI images of breast cancer to genomic markers. (A) Each line represents a statistically significant association between nodes. Each node represents either a genomic feature or radiomic phenotype. The size of the node reflects the number of connections relative to other nodes in its circle. (B) Displays the number of significant associated between the 6 different radiomic categories and the genomic features (43).

Figure 3

A block diagram displaying the transfer learning process. A model is trained in the source domain using a large diverse dataset. The information learned by the model is transferred to the target domain and used on a new task. The two main methods for transfer learning are feature extraction and fine tuning. For the feature extraction method, a feature map is extracted from the convolutional base taken from the source model and used to train a separate machine learning classifier. There are two ways to use transfer learning by fine tuning. The first is freezing the initial layers in the convolutional base from the source model and fine tuning the final layers using the target domain dataset then training a separate classifier. The second method does the same, except instead of training a new machine learning classifier, new fully connected layers will be added and trained using the target domain data.

Figure 4

Illustration of heatmaps displaying the regions within a tumor that were used to predict the probability of pathological complete response. (A, B) show the results when using the CNNs trained on only the pre-contrast images. (C, D) show the results when using the CNN trained using a combination of pre-contrast and post-contrast images. (A, C) display cases that were correctly identified as pCR, while (B, D) are cases that were correctly identified as non-pCR. Modified from (96).