Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer

Abstract

PET-based radiomics have been used to noninvasively 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 18F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic 18F-FDG PET scans and were tested for genetic mutations. Thirteen percent (44/348) and 28% (96/348) of patients were found to harbor epidermal growth factor receptor (EGFR) or Kristen rat sarcoma viral (KRAS) mutations, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume and maximum SUV). The association between imaging features and mutation status (e.g., EGFR-positive [EGFR+] vs. EGFR-negative) 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 with a random guess (AUC = 0.5) using the Noether 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-negative, AUC = 0.67, FDRNoether = 0.0032), as well as differentiating between KRAS-positive and EGFR+ (AUC = 0.65, FDRNoether = 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, 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 noninvasive imaging biomarkers for somatic mutations.

Keywords: PET imaging; phenotype; radiomics; somatic mutation.

© 2017 by the Society of Nuclear Medicine and Molecular Imaging.

Figures

FIGURE 1.

From left to right are patients with EGFR mutation, KRAS mutation, and EGFR– and KRAS– tumors. Stage I and III tumors are shown in top and bottom rows, respectively. Arrows indicate locations of lung tumors.

FIGURE 2.

Comparison of PET features between mutation statuses. Wilcoxon rank-sum test was used to determine whether there was a significant difference in the PET feature between the mutation statuses. FDRWilcoxon ≤ 0.10 is indicated by *. Values of all PET features were normalized using z-transformation. Entries in columns of EGFR+ vs. EGFR–, EGFR+ vs. KRAS+, and KRAS+ vs. KRAS− represent differences of medians of transformed measures. For example, in EGFR+ vs. EGFR– column, entry values < 0 indicate that median value of PET feature for EGFR+ is lower than EGFR–. LGSZE = low gray small zone emphasis; LGLZE = low gray large zone emphasis; LRE = long run emphasis; LRLGE = long run low gray emphasis; LZE = large zone emphasis; RunPct = run percentage; SZE = small zone emphasis; SZV = size zone variability.

FIGURE 3.

AUC. * indicates that AUC is significantly > 0.50 (random guessing) assessed with Noether test (FDRNoether ≤ 0.10). Many of the features significantly predict EGFR+ tumors; however, they are not able to predict KRAS+ tumors. LGSZE = low gray small zone emphasis; LGLZE = low gray large zone emphasis; LRE = long run emphasis; LRLGE = long run low gray emphasis; LZE = large zone emphasis; RunPct = run percentage; SZE = small zone emphasis; SZV = size zone variability.