2020) that DRFs are independent predictor of survival and if combined with the clinical biomarker CA19-9 can improve treatment response prediction and increase the possibility for response-based treatment adaptation . Several studies have also showed that radiomic features are better at predicting treatment response than conventional measures, such as tumor volume and diameter, and the maximum radiotracer uptake on positron emission tomography (PET) imaging. Using this technique an algorithm has been developed, after initial training based on intra tumor lymphocyte density, to predict the probability of tumor response to immunotherapy, providing a demonstration of the clinical potential of radiomics as a powerful tool for personalized therapy in the emerging field of immunooncology. Other studies have also demonstrated the utility of radiomics for predicting immunotherapy response of NSCLC patients using pre-treatment CT and PET/CT images. Radiomics remains inferior to conventional techniques in some applications, suggesting the necessity of continued improvement and manipulation of Radiomics features to different clinical scenarios. For instance, Ludwig et al. (2020) demonstrated that morphological Radiomics features were inferior to previously established features in the discrimination of intracranial aneurysm rupture status from 3-dimensional rotational angiography. Prognostication Radiomic studies have shown that image-based markers have the potential to provide information orthogonal to staging and biomarkers and improve prognostication. Prediction risk of distant metastasis Metastatic potential of tumors may also be predicted by radiomic features. For example, thirty-five CT-based radiomic features were identified to be predictive of distant metastasis of lung cancer in a study by Coroller et al. in 2015. They thus concluded that radiomic features can be useful to identify patients with high risk of developing distant metastasis, guiding physicians to select the effective treatment for individual patients. Assessment of cancer genetics Lung tumor biological mechanisms may demonstrate distinct and complex imaging patterns. In particular, Aerts et al. (2014) showed that radiomic features were associated with biological gene sets, such as cell cycle phase, DNA recombination, regulation of immune system process, etc. Moreover, various mutations of glioblastoma (GBM), such as 1p/19q deletion, MGMT methylation, TP53, EGFR, and NF1, have been shown to be significantly predicted by magnetic resonance imaging (MRI) volumetric measures, including tumor volume, necrosis volume, and contrast enhancing volume. Image guided radiotherapy Radiomics offers the advantage to be non invasive and can therefore be repeated prospectively for a given patient more easily than invasive tumor biopsies. It has been suggested that radiomics could be a mean