Radiomics and Deep Learning: Bridging Breast Cancer Imaging Phenotypes and Genomic Heterogeneity.
Authors
Affiliations (6)
Affiliations (6)
- Department of Oncology, The Third Hospital of Mianyang (Sichuan Mental Health Center), Mianyang, Sichuan, 621000, People's Republic of China.
- Department of Radiology, The First People's Hospital of Neijiang, Neijaing, Sichuan, 641000, People's Republic of China.
- Department of Radiology, Yongchuan Hospital of Chongqing Medical University, Yongchuan, Chongqing, 402160, People's Republic of China.
- Department of Oncology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, 661699, People's Republic of China.
- Department of Radiology, No. 926 Hospital, Joint Logistics Support Force of PLA, Kaiyuan, Yunnan, 661699, People's Republic of China.
- Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medicine University (Zhejiang Provincial Hospital of Chinese Medicine), ZheJiang, Hangzhou, 310006, People's Republic of China.
Abstract
Breast cancer is a highly heterogeneous malignancy. Radiomics, combined with deep learning techniques, harnesses the power of advanced medical imaging to extract rich quantitative features that noninvasively capture tumor imaging phenotypes. This approach facilitates the integration of macroscopic imaging data with genomic heterogeneity, thereby bridging the gap between tumor radiographic appearance and underlying molecular profiles. This review systematically outlines the radiomics workflow in breast cancer, encompassing image acquisition, tumor segmentation, feature extraction, feature selection, and modeling strategies. Emphasis is placed on the role of deep learning in automating feature extraction and enabling multimodal data fusion to enhance predictive accuracy. By examining current research progress, we elucidate methods to uncover latent information related to gene mutations, immune microenvironment characteristics, and other genomic alterations from imaging data. This synthesis highlights the potential of radiogenomics to provide novel insights and tools for precision medicine, ultimately fostering personalized diagnosis, prognosis, and treatment planning in breast cancer care.