Overview of radiomics in breast cancer diagnosis and prognostication. (February 2020)

• Record Type: Journal Article
• Authors: Tagliafico, Alberto Stefano; Piana, Michele; Schenone, Daniela; Lai, Rita; Massone, Anna Maria; Houssami, Nehmat
• Is Part Of: Breast. Volume 49(2020)
• Page Start: 74
• Page End: 80
• Publication Date: 2020-02
• Subjects: Breast cancer -- Prediction -- Digital breast tomosynthesis -- Radiomics -- Magnetic resonance imaging -- Artificial intelligence; Breast -- Diseases -- Periodicals; Breast -- Tumors -- Periodicals; Breast -- Periodicals; Electronic journals; Periodicals; 616
• DOI: 10.1016/j.breast.2019.10.018
• Languages: English
• ISSNs: 0960-9776

Abstract

Abstract: Diagnosis of early invasive breast cancer relies on radiology and clinical evaluation, supplemented by biopsy confirmation. At least three issues burden this approach: a) suboptimal sensitivity and suboptimal positive predictive power of radiology screening and diagnostic approaches, respectively; b) invasiveness of biopsy with discomfort for women undergoing diagnostic tests; c) long turnaround time for recall tests. In the screening setting, radiology sensitivity is suboptimal, and when a suspicious lesion is detected and a biopsy is recommended, the positive predictive value of radiology is modest. Recent technological advances in medical imaging, especially in the field of artificial intelligence applied to image analysis, hold promise in addressing clinical challenges in cancer detection, assessment of treatment response, and monitoring disease progression. Radiomics include feature extraction from clinical images; these features are related to tumor size, shape, intensity, and texture, collectively providing comprehensive tumor characterization, the so-called radiomics signature of the tumor. Radiomics is based on the hypothesis that extracted quantitative data derives from mechanisms occurring at genetic and molecular levels. In this article we focus on the role and potential of radiomics in breast cancer diagnosis and prognostication. Highlights: In the screening setting, radiology sensitivity is suboptimal. Artificial intelligence hold promise in cancer diagnosis and prognostication. Radiomics include feature extraction from clinical images.