Unsupervised machine learning of radiomic features for predicting treatment response and overall survival of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy.

ABSTRACT: 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 18F-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 clustered into 3 groups in terms of both survival (p?=?0.003) and freedom from nodal failure (p?=?0.038). Average concordance measures for predicting survival and nodal failure were 0.640±0.029 and 0.664±0.063 respectively, better than those obtained by prediction models built upon clinical variables (p?

SUBMITTER: Li H 

PROVIDER: S-EPMC6261331 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature