Associations Between Somatic Mutations and Metabolic Imaging Phenotypes in Non-Small Cell Lung Cancer.
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 of18F-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 diagnostic18F-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. AllPvalues 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.
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts Stephen_Yip@dfci.harvard.edu.
Stephen S F
SS
Yip
Department of Radiology, University of Michigan Health System, Ann Arbor, Michigan; and.
John
J
Kim
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.
Thibaud P
TP
Coroller
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.
Chintan
C
Parmar
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.
Emmanuel Rios
ER
Velazquez
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.
Elizabeth
E
Huynh
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.
Raymond H
RH
Mak
Department of Radiation Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.|Department of Radiology, Dana-Farber Cancer Institute, Brigham and Women's Hospital, and Harvard Medical School, Boston, Massachusetts.
Hugo J W L
HJ
Aerts
D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002289:Carcinoma, Non-Small-Cell Lung / Q000000981:diagnostic imaging* / Q000235:genetics*; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D008175:Lung Neoplasms / Q000000981:diagnostic imaging* / Q000235:genetics*; D008297:Male; D008875:Middle Aged; D009154:Mutation; D010641:Phenotype; D049268:Positron-Emission Tomography; D012189:Retrospective Studies
D019788:Fluorodeoxyglucose F18
PET imaging; phenotype; radiomics; somatic mutation
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