  Lung cancer is the leading cause of cancer death in the world. Accurate
determination of the EGFR (epidermal growth factor receptor) mutation status is
highly relevant for the proper treatment of this patients. Purpose: The aim of
this study was to predict the mutational status of the EGFR in non-small cell
lung cancer patients using radiomics features extracted from PET-CT images.
Methods: Retrospective study that involve 34 patients with lung cancer
confirmed by histology and EGFR status mutation assessment. A total of 2.205
radiomics features were extracted from manual segmentation of the PET-CT images
using pyradiomics library. Both computed tomography and positron emission
tomography images were used. All images were acquired with intravenous
iodinated contrast and F18-FDG. Preprocessing includes resampling,
normalization, and discretization of the pixel intensity. Three methods were
used for the feature selection process: backward selection (set 1), forward
selection (set 2), and feature importance analysis of random forest model (set
3). Nine machine learning methods were used for radiomics model building.
Results: 35.2% of patients had EGFR mutation, without significant differences
in age, gender, tumor size and SUVmax. After the feature selection process 6, 7
and 17 radiomics features were selected, respectively in each group. The best
performances were obtained by Ridge Regression in set 1: AUC of 0.826 (95% CI,
0.811 - 0.839), Random Forest in set 2: AUC of 0.823 (95% CI, 0.808 - 0.838)
and Neural Network in set 3: AUC of 0.821 (95% CI, 0.808 - 0.835). Conclusion:
The radiomics features analysis has the potential of predicting clinically
relevant mutations in lung cancer patients through a non-invasive methodology.
