Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer.

Journal of Nuclear Medicine

PubMedID: 27688480

Yip SS, Kim J, Coroller T, Parmar C, Rios Velazquez E, Huynh E, Mak R, Aerts HJ. Associations between somatic mutations and metabolic imaging phenotypes in non-small cell lung cancer. J Nucl Med. 2016;.
PURPOSE
Positron emission tomography (PET)-based radiomics has been used to non-invasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of (18)F-FDG-PET-based radiomic features for somatic mutations in non-small cell lung cancer (NSCLC) patients.

METHODS
348 NSCLC patients underwent diagnostic (18)F-FDG-PET/CT scans and were tested for genetic mutations. 13% (44/348) and 28% (96/348) of patients were found to harbor an EGFR (EGFR+) or KRAS (KRAS+) mutation, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume (MTV) and maximum standard uptake value (SUVmax)). The association between imaging features and mutation status (e.g. EGFR+ vs. EGFR-) was assessed using the Wilcoxon rank-sum test. The ability of each imaging feature to predict mutation status was evaluated by the area under the receiver operating curve (AUC) and its significance was compared to a random guess (AUC=0.5) using the Noether's test. All p-values were corrected for multiple hypothesis testing by controlling the false discovery rate (FDRWilcoxon, FDRNoether) with a significance threshold of 10%.

RESULTS
Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDRWilcoxon=0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR+ vs EGFR-, AUC=0.67, FDRNoether=0.0032), as well as differentiating between KRAS+ and EGFR+ (AUC=0.65, FDRnoether=0.05). None of the features were associated with or predictive of KRAS mutation status (KRAS+ vs. KRAS-, AUC=0.50-0.54).

CONCLUSION
Our results indicate that EGFR mutations may drive different metabolic tumor phenotypes that are captured in PET images, whereas KRAS mutated tumors do not. This proof-of-concept study sheds light on genotype-phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing non-invasive imaging biomarkers for somatic mutations.