Image Data Mining for Extracting Relations between Radiomic Features and Subtypes of Breast Cancer

With the progress of post-genomic research, the relationship between various tumors and genes has been elucidated. However, in the field of radiology, there is not much research to understand the molecular and genetic backgrounds involved in the image phenotype of a lesion. The purpose of this study is to develop image data mining technology to analyze the relationship between image phenotype and genotype of a lesion. Fat-suppressed T1-weighted images of 49 cases were collected from TCGA-BRCA (The Cancer Genome Atlas Breast Invasive Carcinoma) public database. The slice with the largest tumor diameter was selected from the MRI and the tumor regions were manually segmented. A total 371 radiomic features including size, shape, texture, etc. were calculated from the tumor region. By using CART (Classification and Regression Trees) algorithm with these radiomic features as input data, a classification tree that outputs 5 breast cancer subtypes were automatically generated. The overall accuracy of the classification tree for identifying 5 breast cancers was 83.7% (41/49). By applying the proposed method, it is possible to visualize the relationship between image phenotypes and breast cancer subtypes.