Digital Analysis in Breast Imaging

Abstract

Breast imaging is a multimodal approach that plays an essential role in the diagnosis of breast cancer. Mammography, sonography, magnetic resonance, and image-guided biopsy are imaging techniques used to search for malignant changes in the breast or precursors of malignant changes in, e.g., screening programs or follow-ups after breast cancer treatment. However, these methods still have some disadvantages such as interobserver variability and the mammography sensitivity in women with radiologically dense breasts. In order to overcome these difficulties and decrease the number of false positive findings, improvements in imaging analysis with the help of artificial intelligence are constantly being developed and tested. In addition, the extraction and correlation of imaging features with special tumor characteristics and genetics of the patients in order to get more information about treatment response, prognosis, and also cancer risk are coming more and more in focus. The aim of this review is to address recent developments in digital analysis of images and demonstrate their potential value in multimodal breast imaging.

Keywords: Artificial intelligence; Big data; Breast cancer; Computer analysis.

Fig. 1

Outline of a state-of-the-art radiomics analysis [88]. Briefly, a tumor is outlined or contoured on tomographic images. In the next step, a range of quantitative image features is computed, typically comprising shape features, signal intensity distributions, and image texture. Finally, machine learning is used to establish a link between this high-dimensional data space and the relevant clinical endpoint or outcome.