  The aim of this study was to develop radiomic models using PET/CT radiomic
features with different machine learning approaches for finding best predictive
epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene
(KRAS) mutation status. Patients images including PET and CT [diagnostic (CTD)
and low dose CT (CTA)] were pre-processed using wavelet (WAV), Laplacian of
Gaussian (LOG) and 64 bin discretization (BIN) (alone or in combinations) and
several features from images were extracted. The prediction performance of
model was checked using the area under the receiver operator characteristic
(ROC) curve (AUC). Results showed a wide range of radiomic model AUC
performances up to 0.75 in prediction of EGFR and KRAS mutation status.
Combination of K-Best and variance threshold feature selector with logistic
regression (LREG) classifier in diagnostic CT scan led to the best performance
in EGFR (CTD-BIN+B-KB+LREG, AUC: mean 0.75 sd 0.10) and KRAS
(CTD-BIN-LOG-WAV+B-VT+LREG, AUC: mean 0.75 sd 0.07) respectively. Additionally,
incorporating PET, kept AUC values at ~0.74. When considering conventional
features only, highest predictive performance was achieved by PET SUVpeak (AUC:
0.69) for EGFR and by PET MTV (AUC: 0.55) for KRAS. In comparison with
conventional PET parameters such as standard uptake value, radiomic models were
found as more predictive. Our findings demonstrated that non-invasive and
reliable radiomics analysis can be successfully used to predict EGFR and KRAS
mutation status in NSCLC patients.
