Quantitative Radiomic Phenotyping of Cervix Cancer

Large volumes of raw scan images are stored in DICOM RT format in Picture Archiving and Communication System in imaging department. Thus, it is made available to data mining procedures. Raw data does not contain any tumor volumes defined. Hence the transverse CT images are segmented slice by slice using any automatic or semiautomatic algorithms. Many radiomic features are computed from the images after segmentation. The features range from volume, shape, surface density and intensity, texture, tumor location, relations with the surrounding tissues. Redundant information can be eliminated by proper quantification of data acquired. A feature selection algorithm accelerates the evaluation of these features. The data in the radiomic features were compared for any similarity. The repetition of similarity is evaluated when occurs in the same time frame. An outcome variable is defined in supervised analysis of data points to create a predictive model. Graphical representation of final results is deduced from unsupervised analysis. Now the new patient CT data is given as inputs in an radiomic algorithm, that returns a value for tumor growth and disease free survival. Radiomic features can be grouped into five important characteristics such as;