Initiative Vs Guilt-Age 3 Stage Analysis - all not
Abstract Objectives To generate and validate state-of-the-art radiomics models for prediction of radiation-induced lung injury and oncologic outcome in non-small cell lung cancer NSCLC patients treated with robotic stereotactic body radiation therapy SBRT. Conclusion Despite the obvious difficulties in generalizing predictive models for oncologic outcome and toxicity, this analysis shows that carefully designed radiomics models for prediction of local lung fibrosis after SBRT of early stage lung cancer perform well across different institutions. Dose-fractionation schemes usually depend on tumor size and location and have been largely standardized by current guidelines [ 1 , 2 , 3 , 4 ]. Apart from an established dose—response relationship for local control [ 6 ], dosimetric and clinical factors have only shown limited capability in predicting these events [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 ]. Radiomics aims at extraction of biomarkers from high-dimensional analysis of digital images and has been extensively studied in lung cancer by using computed tomography CT or Fluor-Deoxyglucose Positron Emission Tomography FDG-PET of the chest [ 15 , 16 , 17 , 18 , 19 , 20 ]. Several studies have applied radiomic analysis in SBRT of NSCLC [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ], but so far, the clinical impact of the developed algorithms has been low due to low reproducibility of the results [ 35 ], lack of standardization of the extracted radiomic features and lack of external validation on data from other institutions. The availability of open-source software solutions allows the extraction of standardized radiomic features and generation of complex, non-linear models which are able to account for complex interactions between features and have the potential to achieve high performance. The primary objective of the present study was to build a model for the development of radiation-induced lung injury by use of state-of-the-art feature extraction and machine-learning algorithms in order to determine the extra value of imaging tumor biomarkers when used in addition to dosimetric and clinical factors in a cohort of patients with NSCLC treated by robotic SBRT. Initiative Vs Guilt-Age 3 Stage Analysis
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