Correlation of F-18 fluorodeoxyglucose-positron emission tomography maximal standardized uptake value and EGFR mutations in advanced lung adenocarcinoma

Huang, Chun-Ta; Yen, Ruoh‐Fang; Cheng, Mei‐Fang; Hsu, Ya‐Chieh; Wei, Pin-Fei; Tsai, Yi-Ju; Tsai, Meng‐Feng; Shih, Jin‐Yuan; Yang, Chih-Hsin; Yang, Pan‐Chyr

doi:10.1007/s12032-008-9160-1

Cited by 63 publications

(65 citation statements)

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“…Therefore, the effect of ethnicity is reflected in the frequency of EGFR1 mutations in each cohort, where the previous studies had a higher percentage of EGFR1 mutants than in our study (23% in the cohort of Lee et al (43), 40% in Chung et al (44), 64% in Huang et al (21), and 24% in Lee et al (42) vs. only 13% in our study). However, despite the differences between each study, it is clear that the conclusions on using conventional PETbased features for prediction of mutation status remain debatable.…”

Section: Discussioncontrasting

confidence: 50%

“…Our study contained the largest patient cohort (348 patients) compared with the other studies (206 patients in Lee et al (43), 106 patients in Chung et al (44), 77 patients in Huang et al (21), and 214 patients in Lee et al (42)). Furthermore, the previous studies were from Asian institutions whereas our study was from a North American cohort, which resulted in more than 90% of our patients being Caucasian (Table 1).…”

Section: Discussionmentioning

confidence: 99%

“…In addition, increased glucose transport and glycolysis have been observed in KRAS-mutated colorectal and pancreatic cancer cell lines (16,17). Thus, previous studies have investigated the associations between these mutations and SUV measures from PET images; however, there have been conflicting findings (18)(19)(20)(21)(22). These inconsistent reports may be due to the fact that simple SUV measures fail to capture the spatial relationships between image voxels, which may be more informative of the biology of these mutations and describe the degree of tumor heterogeneity (23,24).…”

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

et al. 2016

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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 18 F-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 18 F-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 [EGFR1] 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 5 0.5) using the Noether test. All P values were corrected for multiple hypothesis testing by controlling the false-discovery rate (FDR Wilcoxon , FDR Noether ) with a significance threshold of 10%. Results: Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDR Wilcoxon 5 0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR1 vs. EGFR-negative, AUC 5 0.67, FDR Noether 5 0.0032), as well as differentiating between KRAS-positive and EGFR1 (AUC 5 0.65, FDR Noether 5 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, AUC 5 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.

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confidence: 99%

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

et al. 2016

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“…Prior studies correlating FDG uptake with EGFR mutation status in Taiwan and Korea have had conflicting results [21,22]. In a Taiwanese study of 77 patients with adenocarcinoma including 49 mutant EGFR and 28 wildtype EGFR patients, FDG uptake was significantly higher in mutant EGFR cancers (mean SUV max , 10.5 Ϯ 4.7) than in wild-type EGFR cancers (8.0 Ϯ 3.3) [21]. A cutoff SUV max Ն9.5 was identified as predictive of a mutant EGFR status.…”

Section: Discussionmentioning

confidence: 99%

Role of 18F-Fluorodeoxyglucose Positron Emission Tomography in Predicting Epidermal Growth Factor Receptor Mutations in Non-Small Cell Lung Cancer

Mak¹,

Digumarthy

Muzikansky

et al. 2011

The Oncologist

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Purpose. To compare 18 F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging characteristics in non-small cell lung cancer (NSCLC) with or without epidermal growth factor receptor (EGFR) mutations.Methods. We retrospectively identified NSCLC patients who underwent EGFR mutation testing and pretreatment FDG-PET and CT scans. The maximum standard uptake value (SUV max ) of the primary tumor and any metastases was measured and normalized to the SUV of blood in the pulmonary artery. We compared normalized SUV max values between EGFRmutant and wild-type patients and modeled radiographic and clinical predictors of EGFR mutation status. Receiver operator characteristic (ROC) curves were used to identify potential SUV cutoffs predictive of genotype. Results.We included 100 patients (24 EGFRmutant and 76 wild-type). There was a trend for higher normalized SUV max in the primary tumors among patients with EGFR-wild-type versus mutant (median, 3.4; range, 0.6 -12.8; versus median, 2.9; range, 0.4 -5.0; p ‫؍‬ .09). Normalized SUV max of nodal and distant metastases, and CT characteristics were not associated with genotype. On multivariate analysis, low normalized SUV max of the primary tumor was predictive for EGFR mutation (odds ratio, 0.72; 95% confidence interval, 0.53-0.98; p ‫؍‬ .034). ROC curve analyses yielded an area under the curve of 0.62, and identified a potential cutoff of >5.0 to distinguish wild-type from mutant tumors.Conclusions. In this retrospective study, high FDG avidity (normalized SUV max >5) correlated with EGFR-wild-type genotype. Although genotyping reCorrespondence: Lecia V. Sequist, M.D., M.P.H.,

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“…Additional authors have explored radiolabelled nuclear techniques of direct imaging of EGFR expression [18,19]. Indirect EGFR correlations with an FDG signature have also been reported [20][21][22][23][24].…”

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Radiogenomics and Radioproteomics

Djekidel¹

2012

OMICS J Radiology

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Correlation of F-18 fluorodeoxyglucose-positron emission tomography maximal standardized uptake value and EGFR mutations in advanced lung adenocarcinoma

Abstract: Among Asian patients with advanced lung adenocarcinoma, those with higher SUV(MAX) on the [(18)F]FDG PET are more likely to carry EGFR mutations.

Cited by 63 publications

References 36 publications

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

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

Role of 18F-Fluorodeoxyglucose Positron Emission Tomography in Predicting Epidermal Growth Factor Receptor Mutations in Non-Small Cell Lung Cancer

Radiogenomics and Radioproteomics

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