Radiomics in breast cancer: Current advances and future directions
Ying-Jia Qi, Guan-Hua Su, Chao You, Xu Zhang, Yi Xiao, Yi-Zhou Jiang, Zhi‐Ming Shao
Abstract
Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research. Qi et al. summarize the workflow of radiomics in breast cancer based on multiple imaging modalities including MG, US, MRI, etc. They also discuss the potential clinical application of radiomics, the prospect of radio-multi-omics, as well as the existing challenges and future directions in integrating radiomics into clinical practice.