  Aim: In the present work, we aimed to evaluate a comprehensive radiomics
framework that enabled prediction of EGFR and KRAS mutation status in NSCLC
cancer patients based on PET and CT multi-modalities radiomic features and
machine learning (ML) algorithms. Methods: Our study involved 211 NSCLC cancer
patient with PET and CTD images. More than twenty thousand radiomic features
from different image-feature sets were extracted Feature value was normalized
to obtain Z-scores, followed by student t-test students for comparison, high
correlated features were eliminated and the False discovery rate (FDR)
correction were performed Six feature selection methods and twelve classifiers
were used to predict gene status in patient and model evaluation was reported
on independent validation sets (68 patients). Results: The best predictive
power of conventional PET parameters was achieved by SUVpeak (AUC: 0.69,
P-value = 0.0002) and MTV (AUC: 0.55, P-value = 0.0011) for EGFR and KRAS,
respectively. Univariate analysis of radiomics features improved prediction
power up to AUC: 75 (q-value: 0.003, Short Run Emphasis feature of GLRLM from
LOG preprocessed image of PET with sigma value 1.5) and AUC: 0.71 (q-value
0.00005, The Large Dependence Low Gray Level Emphasis from GLDM in LOG
preprocessed image of CTD sigma value 5) for EGFR and KRAS, respectively.
Furthermore, the machine learning algorithm improved the perdition power up to
AUC: 0.82 for EGFR (LOG preprocessed of PET image set with sigma 3 with VT
feature selector and SGD classifier) and AUC: 0.83 for KRAS (CT image set with
sigma 3.5 with SM feature selector and SGD classifier). Conclusion: We
demonstrated that radiomic features extracted from different image-feature sets
could be used for EGFR and KRAS mutation status prediction in NSCLC patients,
and showed that they have more predictive power than conventional imaging
parameters.

Medical Physics;Machine Learning;Biological Physics;Genomics