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Background and purpose To predict treatment response and survival of NSCLC patients receiving stereotactic body radiation therapy (SBRT), we develop an unsupervised machine learning method for stratifying patients and extracting meta-features simultaneously based on imaging data. Material and methods This study was performed based on an 18 F-FDG-PET dataset of 100 consecutive patients who were treated with SBRT for early stage NSCLC. Each patient’s tumor was characterized by 722 radiomic features. An unsupervised two-way clustering method was used to identify groups of patients and radiomic features simultaneously. The groups of patients were compared in terms of survival and freedom from nodal failure. Meta-features were computed for building survival models to predict survival and free of nodal failure. Results Differences were found between 2 groups of patients when the patients were …