Value of 18F–FDG PET/CT for predicting EGFR mutations and positive ALK expression in patients with non-small cell lung cancer: a retrospective analysis of 849 Chinese patients

Abstract

PurposeEpidermal growth factor receptor (EGFR) mutations and the anaplastic lymphoma kinase (ALK) rearrangement are the two most common druggable targets in non-small cell lung cancer (NSCLC). However, genetic testing is sometimes unavailable. Previous studies regarding the predictive role of 18F–FDG PET/CT for EGFR mutations in NSCLC patients are conflicting. We investigated whether or not 18F–FDG PET could be a valuable noninvasive method to predict EGFR mutations and ALK positivity in NSCLC using the largest patient cohort to date.MethodsWe retrospectively reviewed and included 849 NSCLC patients who were tested for EGFR mutations or ALK status and subjected to 18F–FDG PET/CT prior to treatment. The differences in several clinical characteristics and three parameters based on 18F–FDG PET/CT, including the maximal standard uptake value (SUVmax) of the primary tumor (pSUVmax), lymph node (nSUVmax) and distant metastasis (mSUVmax), between the different subgroups were analyzed. Multivariate logistic regression analysis was performed to identify predictors of EGFR mutations and ALK positivity.ResultsEGFR mutations were identified in 371 patients (45.9%). EGFR mutations were found more frequently in females, non-smokers, adenocarcinomas and stage I disease. Low pSUVmax, nSUVmax and mSUVmax were significantly associated with EGFR mutations. Multivariate analysis demonstrated that pSUVmax < 7.0, female sex, non-smoker status and adenocarcinoma were predictors of EGFR mutations. The receiver operating characteristic (ROC) curve yielded area under the curve (AUC) values of 0.557 and 0.697 for low pSUVmax alone and the combination of the four factors, respectively. ALK-positive patients tended to have a high nSUVmax. Younger age and distant metastasis were the only two independent predictors of ALK positivity.ConclusionWe demonstrated that low pSUVmax is associated with mutant EGFR status and could be integrated with other clinical factors to enhance the discriminability on the EGFR mutation status in some NSCLC patients whose EGFR testing is unavailable.

Correlation between EGFR mutation status and F18‐fluorodeoxyglucose positron emission tomography‐computed tomography image features in lung adenocarcinoma

“…Second, different driver gene mutations may result in distinct pathway activation and glycolytic features. Previous studies have reported that NSCLC patients with tumors harbouring K‐ras mutation or ALK rearrangement showed significantly higher 18 F‐FDG uptake than wild‐type patients . We did not take into consideration the roles of these drivers, which could cause bias in FDG uptake measurements in patients with EGFR ‐wild to some extent.…”

“…However, previous data concerning the association between 18 F-FDG uptake and EGFR mutation in lung cancer are conflicting and the correlation has not been satisfactorily evaluated. [6][7][8][9][10][11] Further studies are needed to validate these results. Therefore, we conducted this retrospective study to investigate whether or not 18 F-FDG PET could be a valuable method for predicting EGFR mutation in lung adenocarcinomas.…”

Development and validation of a model to predict tyrosine kinase inhibitor‐sensitive EGFR mutations of non‐small cell lung cancer based on multi‐institutional data

“…integrated age, gender, clinical stage, tobacco consumption, time since quitting smoking, and predominant pathologic subtype to develop the first model to predict EGFR activating mutations in Asian patients with AC . Some recent studies also tried to build prediction models of EGFR mutation on the basis of pretreatment radiomic features of NSCLC patients …”

“…tried to build prediction models of EGFR mutation on the basis of pretreatment radiomic features of NSCLC patients. [15][16][17][18] Although the most common mutation types, del19 and L8585R mutations, are sensitive to TKIs, some uncommon types have also been found to be resistant. The exon 20 insertion is the main cause of primary resistance to all frequently used TKIs.…”

What can artificial intelligence teach us about the molecular mechanisms underlying disease?

“…Genetic mutations, including EGFR mutations and anaplastic lymphoma kinase (ALK) gene rearrangements, that are associated with improved response to certain TKIs, are associated with image features derived from FDG PET in NSCLC [46]. With EGFR, findings are conflicting, with some studies predominantly showing high FDG uptake in EGFRmutated tumours, reflecting increased glycolysis through AKT signalling [47], and others showing lower uptake [48]. As well as standard features, such as SUVmax, heterogeneity parameters also show associations with EGFR mutation status [49,50] and value in predicting response and survival following treatment with TKIs [51,52].…”