Predictive Power of a Radiomic Signature Based on 18F-FDG PET/CT Images for EGFR Mutational Status in NSCLC

Abstract

Radiomics has become an area of interest for tumor characterization in 18F-Fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) imaging. The aim of the present study was to demonstrate how imaging phenotypes was connected to somatic mutations through an integrated analysis of 115 non-small cell lung cancer (NSCLC) patients with somatic mutation testings and engineered computed PET/CT image analytics. A total of 38 radiomic features quantifying tumor morphological, grayscale statistic, and texture features were extracted from the segmented entire-tumor region of interest (ROI) of the primary PET/CT images. The ensembles for boosting machine learning scheme were employed for classification, and the least absolute shrink age and selection operator (LASSO) method was used to select the most predictive radiomic features for the classifiers. A radiomic signature based on both PET and CT radiomic features outperformed individual radiomic features, the PET or CT radiomic signature, and the conventional PET parameters including the maximum standardized uptake value (SUVmax), SUVmean, SUVpeak, metabolic tumor volume (MTV), and total lesion glycolysis (TLG), in discriminating between mutant-type of epidermal growth factor receptor (EGFR) and wild-type of EGFR- cases with an AUC of 0.805, an accuracy of 80.798%, a sensitivity of 0.826 and a specificity of 0.783. Consistently, a combined radiomic signature with clinical factors exhibited a further improved performance in EGFR mutation differentiation in NSCLC. In conclusion, tumor imaging phenotypes that are driven by somatic mutations may be predicted by radiomics based on PET/CT images.

Performance of 18F-FDG PET/CT Radiomics for Predicting EGFR Mutation Status in Patients With Non-Small Cell Lung Cancer

“…Gender as only clinical characteristic was selected in combine model of our study. The addition of clinical characteristics to PET/CT radiomics model, to varying degrees, increase the diagnostic performance of diagnostic model in previous studies ( 22 , 23 ) and our study, which finally reached 82.6% of diagnostic accuracy in Li X’s study ( 22 ), 80.0% in Zhang J’s study ( 23 ), and 75.3% in our study for predicting EGFR mutation status. It suggested that the combined model might be an alternative indicator of EGFR mutations when tissue samples are not available.…”

“…In summary, the NSCLCs with EGFR mutation had lower glucose metabolism and density, with more heterogeneity on both PET and CT images than those with wild-type EGFR. Owing to the bi-modal image features, PET/CT radiomics model in recent studies (0.79 in Zhang J’s study ( 23 ); 0.80 in Li X’s study ( 22 ); 0.77 in our study) has showed higher AUC than those generated by PET (0.67 in Yip, SS’s study ( 21 )) or CT (0.69 in Rios Velazquez, E’s study ( 19 ); 0.56-0.75 in Sacconi, B’s study ( 31 )) radiomics features alone for predicting EGFR mutation status. However, compared with larger sample size in CT radiomics research, the current sample size in PET/CT radiomics-related studies is generally limited, and thus the generalization ability of PET/CT radiomics-based model remains to be further tested.…”

Incorporating radiomic feature of pretreatment 18F-FDG PET improves survival stratification in patients with EGFR-mutated lung adenocarcinoma

“…On the other hand, presence of pleural effusion and a SUV entropy > 5. 36 were significantly associated with shorter PFS. The significant clinical and imaging variables in the univariate analysis were fitted into the multivariate Cox regression model.…”

Value of combining PET/CT and clinicopathological features in predicting EGFR mutation in Lung Adenocarcinoma with Bone Metastasis

“…When combining with other accessible factors, an AUC of predicting EGFR mutations could reach 0.84. Several studies [8,[12][13][14][15][16][17][18][19][20][21][22][23] had evaluated the value of FDG uptake for predicting EGFR status in NSCLC. As described in Table 3, the results were not very consistent.…”

“…Using FDG tumor uptake as a predictor of EGFR status could be worth exploring. Several studies have investigated the association between SUVmax and EGFR mutations [8,[14][15][16][17][18][19][20][21][22][23][24][25]. However, the results were controversial and conflicting.…”