MRI radiogenomics for intelligent diagnosis of breast tumors and accurate prediction of neoadjuvant chemotherapy responses-a review. (February 2022) - Yin, Xiao-Xia; Hadjiloucas, Sillas; Zhang, Yanchun; Tian, Zhihong
Highlights: This paper overviews multidimensional mining algorithms in relation to Magnetic Resonance Imaging (MRI) radiogenomic. The work addresses a new problem in radiogenomics mining: how to combine structural radiomics information with non-structural genomics information for improving the accuracy and efficacy of Neoadjuvant Chemotherapy (NAC). The approaches discussed incorporate some of the latest developments in of current signal processing and artificial intelligence and have significant potential in advancing and provide a development platform for future cutting-edge biomedical radiogenomics analysis. Abstract: Background and objective: This paper aims to overview multidimensional mining algorithms in relation to Magnetic Resonance Imaging (MRI) radiogenomics for computer aided detection and diagnosis of breast tumours. The work also aims to address a new problem in radiogenomics mining: how to combine structural radiomics information with non-structural genomics information for improving the accuracy and efficacy of Neoadjuvant Chemotherapy (NAC). Methods: This requires the automated extraction of parameters from non-structural breast radiomics data, and finding feature vectors with diagnostic value, which then are combined with genomics data. In order to address the problem of weakly labelled tumour images, a Generative Adiversarial Networks (GAN) based deep learning strategy is proposed for the classification of tumour types; this has significant potential for providing accurate real-time identification of tumorous regions from MRI scans. In order to efficiently integrate in a deep learning framework different features from radiogenomics datasets at multiple spatio-temporal resolutions, pyramid structured and multi-scale densely connected U-Nets are proposed. A bidirectional gated recurrent unit (BiGRU) combined with an attention based deep learning approach is also proposed. Results: The aim is to accurately predict NAC responses by combining imaging and genomic datasets. The approaches discussed incorporate some of the latest developments in of current signal processing and artificial intelligence and have significant potential in advancing and provide a development platform for future cutting-edge biomedical radiogenomics analysis. Conclusions: The association of genotypic and phenotypic features is at the core of the emergent field of Precision Medicine. It makes use of advances in biomedical big data analysis, which enables the correlation between disease-associated phenotypic characteristics, genetics polymorphism and gene activation to be revealed.