Quantitative Radiomic Phenotyping of Cervix Cancer

Radiomics aims to extract huge amount of quantifiable features from medical images with data-characterization algorithms.1-5 Radiologists cannot appreciate all the disease characteristics in generic diagnostic images.6 Using these radiomic features, it is possible to uncover the physiognomies of disease progression. The hypothesis of radiomics is that the distinctive imaging features between disease forms may be useful for predicting prognosis and therapeutic response for various conditions, thus providing valuable information for personalized therapy.1,7,8 Radiomics emerged from oncology3 is the most cutting-edge innovation. However, the technique can be useful in any medical study involving tomographic imaging. This article analyzes currently available phenotyping algorithms for Lung and Head & Neck malignancies. And It can help researchers to understand the concept to apply the same for cervix cancer.