## 2 Research Status and Development Trends for Computer Aided Detection and Diagnosis of MRI
### Integration of Radionics with DCE-MRI Datasets  
Radiomics research is currently a very promising and emergent research topic in imaging informatics. The concept of radiomics was first proposed in 2012 by [PERSON] et al. [15]. In the past decade, [PERSON] et al. [16] found that the wider proliferation of pattern recognition tools, the increase in the size of data sets and the high-throughput extraction methodologies have dramatically benefitted computed tomography (CT), positron emission tomography (PET) and magnetic resonance imaging (MRI), along with other medical imaging modalities, and enabled the generation of predictive models for clinical use. With the development of high-throughput quantitative feature extraction technology, it became possible to transform images into data that can be mined to provide decision support. The features that are most commonly mined usually include first, second, and higher-order statistics. Combining these data extracted from individual patients with similar data from other patients using bioinformatics tools, researchers are able to develop models that may potentially improve the diagnostic, prognostic and predictive accuracy. In contrast to the traditional method of processing medical images through visual interpretation, further analysis is possible on the basis of radiomics based imaging, which incorporates patho-physiological information, thus being of high clinical relevance. Breast cancer is a complex disease caused by the progressive accumulation of multiple genetic mutations and epigenetic disorders of key genes triggered by cascade reactions. There are substantial differences between individual oncological patients according to the age and the phenotypic expression of the disease. [PERSON] [17] and other researchers have shown that the diagnostic performance of DCE-MRI and diffusion-weighted imaging (DWI) can predict the biological behavior of individual breast tumours and evaluate the suitability of the treatment plan. [PERSON] [18] found that on high-resolution DWI of breast cancer patients, the mean apparent diffusion coefficient (ADC) value of ER-negative tumours was significantly higher than that of ER-positive tumours (\(p\) = 0.005), which suggested that the ADC values on tumour and peritumural stroma allow a good distinction of different biological labels. [PERSON] [19] and other studies have found that the kurtosis features extracted from DCE-MRI are significantly related to the Oncotype DxRS that can be genetically detected. Oncotype DxRS is an effective biomarker which can be used to guide the formulation of treatment plans, and enables doctors to predict the possibility of cancer recurrence and the efficacy of chemotherapy on the basis of image features. [PERSON] [20] partitioned breast tumours on DCE-MRI into multiple sub-regions according to different enhancement characteristics. Through the extraction of a large number of features for analysis, it has been found that rapid inflow and outflow sub-regions allow the prediction of patients' response to neo-adjuvant chemotherapy with area under the receiver operating characteristic curve (AUC) of 0.79. Radiomics usually begins with data acquisition. It relies on the analysis of a large volume of medical images combined with locally or globally extracted specific biomarkers, and on the examination of the expression of genes associated with specific pathways, e.g., mTOR, as well as on the corresponding clinical data at the tissue or organism level to reveal the correlations that exist between them. Thus radiomics may provide a more robust quantitative and objective support for decisions surrounding cancer detection and treatment [15]. This is accomplished by fusing the quantitative information from medical images and combining the imaging features with clinical information, genomic information and biochemical information, to generate radiomic biomarkers. Such an approach is a newly emergent modality that meets clinical requirements and has significant potential to improve diagnostics and cancer treatments [8].