Research Papers:

Prediction of EGFR and KRAS mutation in non-small cell lung cancer using quantitative 18F FDG-PET/CT metrics

Ryogo Minamimoto, Mehran Jamali, Olivier Gevaert, Sebastian Echegaray, Amanda Khuong, Chuong D. Hoang, Joseph B. Shrager, Sylvia K. Plevritis, Daniel L. Rubin, Ann N. Leung, Sandy Napel and Andrew Quon _

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Oncotarget. 2017; 8:52792-52801. https://doi.org/10.18632/oncotarget.17782

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Ryogo Minamimoto1, Mehran Jamali1, Olivier Gevaert1, Sebastian Echegaray1, Amanda Khuong2, Chuong D. Hoang2, Joseph B. Shrager2, Sylvia K. Plevritis1, Daniel L. Rubin1, Ann N. Leung1, Sandy Napel1 and Andrew Quon1

1Department of Radiology, Stanford University, Stanford, CA, USA

2Department of Cardiothoracic Surgery, Stanford University, Stanford, CA, USA

Correspondence to:

Andrew Quon, email: aquon@stanford.edu

Keywords: 18FDG-PET/CT, heterogeneity, KRAS gene mutation, EGFR gene mutation, NSCLC

Received: September 16, 2016     Accepted: March 20, 2017     Published: May 10, 2017


This study investigated the relationship between epidermal growth factor receptor (EGFR) and Kirsten rat sarcoma viral oncogene homolog (KRAS) mutations in non-small-cell lung cancer (NSCLC) and quantitative FDG-PET/CT parameters including tumor heterogeneity. 131 patients with NSCLC underwent staging FDG-PET/CT followed by tumor resection and histopathological analysis that included testing for the EGFR and KRAS gene mutations. Patient and lesion characteristics, including smoking habits and FDG uptake parameters, were correlated to each gene mutation. Never-smoker (P < 0.001) or low pack-year smoking history (p = 0.002) and female gender (p = 0.047) were predictive factors for the presence of the EGFR mutations. Being a current or former smoker was a predictive factor for the KRAS mutations (p = 0.018). The maximum standardized uptake value (SUVmax) of FDG uptake in lung lesions was a predictive factor of the EGFR mutations (p = 0.029), while metabolic tumor volume and total lesion glycolysis were not predictive. Amongst several tumor heterogeneity metrics included in our analysis, inverse coefficient of variation (1/COV) was a predictive factor (p < 0.02) of EGFR mutations status, independent of metabolic tumor diameter. Multivariate analysis showed that being a never-smoker was the most significant factor (p < 0.001) for the EGFR mutations in lung cancer overall. The tumor heterogeneity metric 1/COV and SUVmax were both predictive for the EGFR mutations in NSCLC in a univariate analysis. Overall, smoking status was the most significant factor for the presence of the EGFR and KRAS mutations in lung cancer.

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