Radiomics and artificial intelligence in breast imaging: a survey

Tianyu Zhang, Tao Tan, Riccardo Samperna, Zhang Li, Yuan Gao, Xin Wang, Luyi Han, Qifeng Yu, Regina G.H. Beets-Tan, Ritse M. Mann

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

Medical imaging techniques, such as mammography, ultrasound and magnetic resonance imaging, plays an integral role in the detection and characterization of breast cancer. Although computers are believed to gain an important role in the assessment of medical images for breast evaluation for at least two decades, their impact on performance has not lived up to expectations yet. With the continuous and rapid development of computer science, artificial intelligence (AI) approaches, like machine learning and deep learning, have been introduced for the analysis of medical images. Because of the remarkable advances in data extraction and analysis in medical imaging compared to conventional feature-based techniques, AI has reignited the interest in automated breast image interpretation. Extensive research is conducted on accurate detection and classification of breast lesions, and more specifically, the predictive and prognostic features of breast cancer by radiomics. Radiomics exploits the fact that image data is nowadays numerical and can also be used to generate quantitative biomarkers. In this comprehensive review, we cover the progress, application and challenge of radiomics and AI in breast cancer diagnosis in recent years, as well as the impact and significance of AI on future breast cancer research.

Original languageEnglish
Pages (from-to)857-892
Number of pages36
JournalArtificial Intelligence Review
Volume56
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Artificial Intelligence
  • Breast cancer
  • Magnetic resonance imaging
  • Mammography
  • Radiomics
  • Ultrasound

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