Correlation between (18)f-fluorodeoxyglucose uptake and epidermal growth factor receptor mutations in advanced lung cancer

Prognostic significance of integrating total metabolic tumor volume and EGFR mutation status in patients with lung adenocarcinoma

Background

The objective of this study was to investigate the prognostic significance of total metabolic tumor volume (TMTV) derived from baseline 18F-2-fluoro-2-deoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT), in conjunction with epidermal growth factor receptor (EGFR) mutation status, among patients with lung adenocarcinoma (LUAD).

Methods

We performed a retrospective analysis on 141 patients with LUAD (74 males, 67 females, median age 67 (range 34-86)) who underwent 18F-FDG PET/CT and had their EGFR mutation status determined. Optimal cutoff points for TMTV were determined using time-dependent receiver operating characteristic curve analysis. The survival difference was compared using Cox regression analysis and Kaplan‒Meier curves.

Results

The EGFR mutant patients (n = 79, 56.0%) exhibited significantly higher 2-year progression-free survival (PFS) and overall survival (OS) rates compared to those with EGFR wild-type (n = 62, 44.0%), with rates of 74.2% vs 69.2% (P = 0.029) and 86.1% vs 67.7% (P = 0.009), respectively. The optimal cutoff values of TMTV were 36.42 cm3 for PFS and 37.51 cm3 for OS. Patients with high TMTV exhibited significantly inferior 2-year PFS and OS, with rates of 22.4% and 38.1%, respectively, compared to those with low TMTV, who had rates of 85.8% and 95.0% (both P < 0.001). In both the EGFR mutant and wild-type groups, patients exhibiting high TMTV demonstrated significantly inferior 2-year PFS and OS compared to those with low TMTV. In multivariate analysis, EGFR mutation status (hazard ratio, HR, 0.41, 95% confidence interval, CI [0.18-0.94], P = 0.034) and TMTV (HR 8.08, 95% CI [2.34-28.0], P < 0.001) were independent prognostic factors of OS, whereas TMTV was also an independent prognosticator of PFS (HR 2.59, 95% CI [1.30-5.13], P = 0.007).

Conclusion

Our study demonstrates that the integration of TMTV on baseline 18F-FDG PET/CT with EGFR mutation status improves the accuracy of prognostic evaluation for patients with LUAD.

The role of artificial intelligence based on PET/CT radiomics in NSCLC: Disease management, opportunities, and challenges

Objectives

Lung cancer has been widely characterized through radiomics and artificial intelligence (AI). This review aims to summarize the published studies of AI based on positron emission tomography/computed tomography (PET/CT) radiomics in non-small-cell lung cancer (NSCLC).

Materials and methods

A comprehensive search of literature published between 2012 and 2022 was conducted on the PubMed database. There were no language or publication status restrictions on the search. About 127 articles in the search results were screened and gradually excluded according to the exclusion criteria. Finally, this review included 39 articles for analysis.

Results

Classification is conducted according to purposes and several studies were identified at each stage of disease:1) Cancer detection (n=8), 2) histology and stage of cancer (n=11), 3) metastases (n=6), 4) genotype (n=6), 5) treatment outcome and survival (n=8). There is a wide range of heterogeneity among studies due to differences in patient sources, evaluation criteria and workflow of radiomics. On the whole, most models show diagnostic performance comparable to or even better than experts, and the common problems are repeatability and clinical transformability.

Conclusion

AI-based PET/CT Radiomics play potential roles in NSCLC clinical management. However, there is still a long way to go before being translated into clinical application. Large-scale, multi-center, prospective research is the direction of future efforts, while we need to face the risk of repeatability of radiomics features and the limitation of access to large databases.

A Review of the Correlation Between Epidermal Growth Factor Receptor Mutation Status and 18F-FDG Metabolic Activity in Non-Small Cell Lung Cancer

PET/CT with 18F-2-fluoro-2-deoxyglucose (18F-FDG) has been proposed as a promising modality for diagnosing and monitoring treatment response and evaluating prognosis for patients with non-small cell lung cancer (NSCLC). The status of epidermal growth factor receptor (EGFR) mutation is a critical signal for the treatment strategies of patients with NSCLC. Higher response rates and prolonged progression-free survival could be obtained in patients with NSCLC harboring EGFR mutations treated with tyrosine kinase inhibitors (TKIs) when compared with traditional cytotoxic chemotherapy. However, patients with EGFR mutation treated with TKIs inevitably develop drug resistance, so predicting the duration of resistance is of great importance for selecting individual treatment strategies. Several semiquantitative metabolic parameters, e.g., maximum standard uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG), measured by PET/CT to reflect 18F-FDG metabolic activity, have been demonstrated to be powerful in predicting the status of EGFR mutation, monitoring treatment response of TKIs, and assessing the outcome of patients with NSCLC. In this review, we summarize the biological and clinical correlations between EGFR mutation status and 18F-FDG metabolic activity in NSCLC. The metabolic activity of 18F-FDG, as an extrinsic manifestation of NSCLC, could reflect the mutation status of intrinsic factor EGFR. Both of them play a critical role in guiding the implementation of treatment modalities and evaluating therapy efficacy and outcome for patients with NSCLC.

Lung adenocarcinoma: development of nomograms based on PET/CT images for prediction of epidermal growth factor receptor mutation status and subtypes

OBJECTIVE

To develop nomograms that combine clinical characteristics, computed tomographic (CT) features and 18F-fluorodeoxyglucose PET (18F-FDG PET) metabolic parameters for individual prediction of epidermal growth factor receptor (EGFR) mutation status and exon 19 deletion mutation and exon 21 point mutation (21 L858R) subtypes in lung adenocarcinoma.

METHODS

In total 124 lung adenocarcinoma patients who underwent EGFR mutation testing and whole-body 18F-FDG PET/CT were enrolled. Each patient's clinical characteristics (age, sex, smoking history, etc.), CT features (size, location, margins, etc.) and four metabolic parameters (SUVmax, SUVmean, MTV and TLG) were recorded and analyzed. Logistic regression analyses were performed to screen for significant predictors of EGFR mutation status and subtypes, and these predictors were presented as easy-to-use nomograms.

RESULTS

According to the results of multiple regression analysis, three nomograms for individualized prediction of EGFR mutation status and subtypes were constructed. The area under curve values of three nomograms were 0.852 (95% CI, 0.783-0.920), 0.857 (95% CI, 0.778-0.937) and 0.893 (95% CI, 0.819-0.968) of EGFR mutation vs. wild-type, 19 deletion mutation vs. wild-type and 21 L858R vs. wild-type, respectively. Only calcification showed significant differences between the EGFR 19 deletion and 21 L858R mutations.

CONCLUSION

EGFR 21 L858R mutation was more likely to be nonsolid texture with air bronchograms and pleural retraction on CT images. And they were more likely to be associated with lower FDG metabolic activity compared with those wild-types. The sex difference was mainly caused by the 19 deletion mutation, and calcification was more frequent in them.

Prediction of EGFR Mutation Status Based on 18F-FDG PET/CT Imaging Using Deep Learning-Based Model in Lung Adenocarcinoma

Objective

The purpose of this study was to develop a deep learning-based system to automatically predict epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma in ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT).

Methods

Three hundred and one lung adenocarcinoma patients with EGFR mutation status were enrolled in this study. Two deep learning models (SE_CT and SE_PET) were developed with Squeeze-and-Excitation Residual Network (SE-ResNet) module for the prediction of EGFR mutation with CT and PET images, respectively. The deep learning models were trained with a training data set of 198 patients and tested with a testing data set of 103 patients. Stacked generalization was used to integrate the results of SE_CT and SE_PET.

Results

The AUCs of the SE_CT and SE_PET were 0.72 (95% CI, 0.62–0.80) and 0.74 (95% CI, 0.65–0.82) in the testing data set, respectively. After integrating SE_CT and SE_PET with stacked generalization, the AUC was further improved to 0.84 (95% CI, 0.75–0.90), significantly higher than SE_CT (p<0.05).

Conclusion

The stacking model based on ¹⁸F-FDG PET/CT images is capable to predict EGFR mutation status of patients with lung adenocarcinoma automatically and non-invasively. The proposed model in this study showed the potential to help clinicians identify suitable advanced patients with lung adenocarcinoma for EGFR‐targeted therapy.

Primary metabolic tumor volume from 18F-FDG PET/CT associated with epidermal growth factor receptor mutation in lung adenocarcinoma patients

PURPOSE

To explore the potential parameters from F-FDG PET/CT that might be associated with the epidermal growth factor receptor (EGFR) gene mutation status in lung adenocarcinoma (ADC) patients.

METHODS

Data of the test cohort of 191 patients and the validation cohort of 55 patients with newly diagnosed ADC were retrospectively reviewed. All patients underwent F-FDG PET/CT scans and EGFR mutation tests prior to treatment. The metabolic parameters obtained from F-FDG PET/CT combining with clinical characteristics were analyzed by using univariate and multivariate logistic regression analyses. Then two cohorts were enrolled to validate the predictive model by area under the receiver-operating characteristic curve (AUC), respectively.

RESULTS

EGFR mutation-positive was seen of 33.0% (63/191) and 32.7% (18/55) in two cohorts, respectively. In univariate analysis, female, nonsmokers, metabolic parameters of primary tumor [mean standardized uptake value, metabolic tumor volume (pMTV), and total lesion glycolysis], non-necrosis of primary tumor, and serum tumor markers [carbohydrate antigen 19-9, squamous cell carcinoma antigen, and precursor of gastrin releasing peptide (proGRP)] were significantly relevant with EGFR mutation. In multivariate analysis with adjustment of age and TNM stage, pMTV (<8.13 cm), proGRP (≥38.44 pg/ml) and women were independent significant predictors for EGFR mutation. The AUC for the predictive value of these factors was 0.739 [95% confidence interval (CI) 0.665-0.813] in the cohort of 191 patients and 0.716 (95% CI 0.567-0.865) in the cohort of 55 patients, respectively.

CONCLUSION

Low pMTV (<8.13 cm) was an independent predictor and could be integrated with women and high proGRP (≥38.44 pg/ml) to enhance the discriminability on the EGFR mutation status in ADC patients.

Can 18F-FDG PET/CT predict EGFR status in patients with non-small cell lung cancer? A systematic review and meta-analysis

OBJECTIVES

This study aimed to explore the diagnostic significance of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography (PET)/CT for predicting the presence of epidermal growth factor receptor (EGFR) mutations in patients with non-small cell lung cancer (NSCLC).

DESIGN

A systematic review and meta-analysis.

DATA SOURCES

The PubMed, EMBASE and Cochrane library databases were searched from the earliest available date to December 2020.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

The review included primary studies that compared the mean maximum of standard uptake value (SUV_max) between wild-type and mutant EGFR, and evaluated the diagnostic value of ¹⁸F-FDG PET/CT using SUV_max for prediction of EGFR status in patients with NSCLC.

DATA EXTRACTION AND SYNTHESIS

The main analysis was to assess the sensitivity and specificity, the positive diagnostic likelihood ratio (DLR+) and DLR-, as well as the diagnostic OR (DOR) of SUV_max in prediction of EGFR mutations. Each data point of the summary receiver operator characteristic (SROC) graph was derived from a separate study. A random effects model was used for statistical analysis of the data, and then diagnostic performance for prediction was further assessed.

RESULTS

Across 15 studies (3574 patients), the pooled sensitivity for ¹⁸F-FDG PET/CT was 0.70 (95% CI 0.60 to 0.79) with a pooled specificity of 0.59 (95% CI 0.52 to 0.66). The overall DLR+ was 1.74 (95% CI 1.49 to 2.03) and DLR- was 0.50 (95% CI 0.38 to 0.65). The pooled DOR was 3.50 (95% CI 2.37 to 5.17). The area under the SROC curve was 0.68 (95% CI 0.64 to 0.72). The likelihood ratio scatter plot based on average sensitivity and specificity was in the lower right quadrant.

CONCLUSION

Meta-analysis results showed ¹⁸F-FDG PET/CT had low pooled sensitivity and specificity. The low DOR and the likelihood ratio scatter plot indicated that ¹⁸F-FDG PET/CT should be used with caution when predicting EGFR mutations in patients with NSCLC.

The diagnostic and predictive efficacy of 18F-FDG PET/CT metabolic parameters for EGFR mutation status in non-small-cell lung cancer: A meta-analysis

PURPOSE

To investigate the predictive performance of the maximum standardized uptake value (SUV_max) and mean standardized uptake value (SUV_mean) of primary lesions based on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) for EGFR mutation status in patients with non-small cell lung cancer (NSCLC).

METHODS

The PubMed/Medline, Embase, Cochrane Library and Web of Science databases were searched as of January 1, 2021. Studies whose reported data could be used to construct contingency tables were included. Study characteristics were extracted, and methodological quality assessment was conducted by two separate reviewers using the Quality Assessment of Diagnostic Accuracy Studies. The pooled sensitivity, specificity and area under the summary receiver operating characteristic curve (AUROC) were calculated. The possible causes of heterogeneity were analysed by meta-regression.

RESULTS

The 18 included studies had a total of 4024 patients. The majority of the studies showed a low to unclear risk of bias and concerns of applicability. For differentiating EGFR-mutant NSCLC from wild-type NSCLC, the pooled sensitivity and specificity were 71 % and 60 % for SUV_max and 64 % and 63 % for SUV_mean, respectively. The summary AUROCs of SUV_max and SUV_mean were 0.69 (95 % CI, 0.65-0.73) and 0.68 (95 % CI, 0.64-0.72), respectively. The meta-regression analysis indicated that blindness to EGFR mutation test results, the number of readers and the number of PET/CT scanners were possible causes of heterogeneity.

CONCLUSIONS

Our meta-analysis implied that SUV_max and SUV_mean of primary lesions from ¹⁸F-FDG PET/CT harboured moderate predictive efficacy for the EGFR mutation status of NSCLC.

18F-FDG PET/CT SUVmax and serum CEA levels as predictors for EGFR mutation state in Chinese patients with non-small cell lung cancer

The epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) contribute to an increased response rate, compared with chemotherapy, in patients with inhibitor-sensitive EGFR mutations. The present study evaluated the association between the maximum standardized uptake value (SUV_max) of ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (FDG PET/CT), as well as serum carcinoembryonic antigen (CEA) levels and EGFR mutations prior to treatment, in patients with non-small cell lung cancer (NSCLC). Patients with histologically confirmed NSCLC (n=167), who underwent an ¹⁸F-FDG PET/CT scan, EGFR mutation analysis and a serum CEA test participated in the present study. Multivariate logistic regression analysis was used to analyze predictors of EGFR mutations. Receiver-operating characteristic (ROC) curve analysis was performed to determine the efficient cut-off value. Survival rate analysis was evaluated according to SUV_max and EGFR mutation status. A decreased SUV_max and an increased CEA level was observed in patients with EGFR-mutations, compared with patients with wild-type primary lesions and metastatic lymph nodes. The exon 19 EGFR mutation was associated with increased SUV_max, compared with the exon 21 L858R mutation. The ROC analysis indicated that an ¹⁸F-FDG PET/CT uptake SUV_max >11.5 may be a predictor of the wild-type EGFR genotype and increased CEA levels (CEA >9.4 ng/ml) were associated with EGFR mutations. Furthermore, patients with no smoking history, low SUV_max of the primary tumor, metastatic lymph nodes and a high CEA level were significantly associated with EGFR mutation status. The results of the present study indicated that patients with advanced NSCLC, particularly Chinese patients, with decreased SUV_max and increased CEA levels are associated with EGFR mutations, which may serve as predictors for the EGFR-TKI therapeutic response.

Predicting EGFR mutation subtypes in lung adenocarcinoma using 18F-FDG PET/CT radiomic features

Background

Identification of epidermal growth factor receptor (EGFR) mutation types is crucial before tyrosine kinase inhibitors (TKIs) treatment. Radiomics is a new strategy to noninvasively predict the genetic status of cancer. In this study, we aimed to develop a predictive model based on ¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (¹⁸F-FDG PET/CT) radiomic features to identify the specific EGFR mutation subtypes.

Methods

We retrospectively studied ¹⁸F-FDG PET/CT images of 148 patients with isolated lung lesions, which were scanned in two hospitals with different CT scan setting (slice thickness: 3 and 5 mm, respectively). The tumor regions were manually segmented on PET/CT images, and 1,570 radiomic features (1,470 from CT and 100 from PET) were extracted from the tumor regions. Seven hundred and ninety-four radiomic features insensitive to different CT settings were first selected using the Mann white U test, and collinear features were further removed from them by recursively calculating the variation inflation factor. Then, multiple supervised machine learning models were applied to identify prognostic radiomic features through: (I) a multi-variate random forest to select features of high importance in discriminating different EGFR mutation status; (II) a logistic regression model to select features of the highest predictive value of the EGFR subtypes. The EGFR mutation predicting model was constructed from prognostic radiomic features using the popular Xgboost machine-learning algorithm and validated using 3-fold cross-validation. The performance of predicting model was analyzed using the receiver operating characteristic curve (ROC) and measured with the area under the curve (AUC).

Results

Two sets of prognostic radiomic features were found for specific EGFR mutation subtypes: 5 radiomic features for EGFR exon 19 deletions, and 5 radiomic features for EGFR exon 21 L858R missense. The corresponding radiomic predictors achieved the prediction accuracies of 0.77 and 0.92 in terms of AUC, respectively. Combing these two predictors, the overall model for predicting EGFR mutation positivity was also constructed, and the AUC was 0.87.

Conclusions

In our study, we established predictive models based on radiomic analysis of 18F-FDG PET/CT images. And it achieved a satisfying prediction power in the identification of EGFR mutation status as well as the certain EGFR mutation subtypes in lung cancer.

Influx rate constant of 18F-FDG increases in metastatic lymph nodes of non-small cell lung cancer patients

PURPOSE

Primary tumor (PT) and metastatic lymph node (MLN) status have a great influence on diagnosis and treatment of lung cancer. Our main purpose was to investigate the imaging characteristics of PT or MLN by applying the ¹⁸F-FDG PET dynamic modeling approach for non-small cell lung cancer (NSCLC).

METHODS

Dynamic ¹⁸F-FDG PET scans were performed for 76 lung cancer patients, and 62 NSCLC cases were finally included in this study: 37 with newly diagnosed early and locally advanced lung cancer without distant metastases (group M0) and 25 metastatic lung cancer (group M1). Patlak graphic analysis (K_i calculation) based on the dynamic modeling and SUV analysis from conventional static data were performed.

RESULTS

For PT, both K_i^PT (0.050 ± 0.005 vs 0.026 ± 0.004 min^-1, p < 0.001) and SUV^PT (8.41 ± 0.64 vs 5.23 ± 0.73, p < 0.01) showed significant higher values in group M1 than M0. For MLN, K_i^MLN showed significant higher values in M1 than M0 (0.033 ± 0.005 vs 0.016 ± 0.003 min^-1, p < 0.01), while no significant differences were found for SUV^MLN between M0 and M1 (4.22 ± 0.49 vs 5.57 ± 0.59, p > 0.05). Both SUV ^PT and K_i^PT showed significant high values in squamous cell carcinoma than adenocarcinoma, but neither SUV^PT nor K_i^PT showed significant differences between EGFR mutants versus wild types. The overall Spearman analysis for SUV and K_i from different groups showed variable correlation (r = 0.46-0.94).

CONCLUSION

The dynamic modeling for MLN (K_i^MLN) showed more sensitive than the static analysis (SUV) to detect metastatic lymph nodes in NSCLC, although both methods were sensitive for PT. This methodology of non-invasive imaging may become an important tool to evaluate MLN and PT status for patients who cannot undergo histological examination.

CLINICAL TRIAL REGISTRATION

The clinical trial registration number is NCT03679936 (http://www.clinicaltrials.gov/).

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

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.

Correlations Study Between 18F-FDG PET/CT Metabolic Parameters Predicting Epidermal Growth Factor Receptor Mutation Status and Prognosis in Lung Adenocarcinoma

Purpose: This study assessed the ability of metabolic parameters from ¹⁸Fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) and clinicopathological data to predict epidermal growth factor receptor (EGFR) expression/mutation status in patients with lung adenocarcinoma and to develop a prognostic model based on differences in EGFR expression status, to enable individualized targeted molecular therapy. Patients and Methods: Metabolic parameters and clinicopathological data from 200 patients diagnosed with lung adenocarcinoma between July 2009 and November 2016, who underwent ¹⁸F-FDG PET/CT and EGFR mutation testing, were retrospectively evaluated. Multivariate logistic regression was applied to significant variables to establish a prediction model for EGFR mutation status. Overall survival for both mutant and wild-type EGFR was analyzed to establish a multifactor Cox regression model. Results: Of the 200 patients, 115 (58%) exhibited EGFR mutations and 85 (42%) were wild-type. Among selected metabolic parameters, metabolic tumor volume (MTV) demonstrated a significant difference between wild-type and mutant EGFR mutation status, with an area under the receiver operating characteristic curve (AUC) of 0.60, which increased to 0.70 after clinical data (smoking status) were combined. Survival analysis of wild-type and mutant EGFR yielded mean survival times of 34.451 (95% CI 28.654-40.249) and 53.714 (95% CI 44.331-63.098) months, respectively. Multivariate Cox regression revealed that mutation type, tumor stage, and thyroid transcription factor-1 (TTF-1) expression status were the main factors influencing patient prognosis. The hazard ratio for mutant EGFR was 0.511 (95% CI 0.303-0.862) times that of wild-type, and the risk of death was lower for mutant EGFR than for wild-type. The risk of death was lower in TTF-1-positive than in TTF-1-negative patients. Conclusion: ¹⁸F-FDG PET/CT metabolic parameters combined with clinicopathological data demonstrated moderate diagnostic efficacy in predicting EGFR mutation status and were associated with prognosis in mutant and wild-type EGFR non-small-cell lung cancer (NSCLC), thus providing a reference for individualized targeted molecular therapy.

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

PURPOSE

Epidermal 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.

METHODS

We 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.

RESULTS

EGFR 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.

CONCLUSION

We 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.

A PET imaging approach for determining EGFR mutation status for improved lung cancer patient management

Tumor heterogeneity and changes in epidermal growth factor receptor (EGFR) mutation status over time challenge the design of effective EGFR tyrosine kinase inhibitor (TKI) treatment strategies for non-small cell lung cancer (NSCLC). Therefore, there is an urgent need to develop techniques for comprehensive tumor EGFR profiling in real time, particularly in lung cancer precision medicine trials. We report a positron emission tomography (PET) tracer, N-(3-chloro-4-fluorophenyl)-7-(2-(2-(2-(2-18F-fluoroethoxy) ethoxy) ethoxy) ethoxy)-6-methoxyquinazolin-4-amine (18F-MPG), with high specificity to activating EGFR mutant kinase. We evaluate the feasibility of using 18F-MPG PET for noninvasive imaging and quantification of EGFR-activating mutation status in preclinical models of NSCLC and in patients with primary and metastatic NSCLC tumors. 18F-MPG PET in NSCLC animal models showed a significant correlation (R2 = 0.9050) between 18F-MPG uptake and activating EGFR mutation status. In clinical studies with NSCLC patients (n = 75), the concordance between the detection of EGFR activation by 18F-MPG PET/computed tomography (CT) and tissue biopsy reached 84.29%. There was a greater response to EGFR-TKIs (81.58% versus 6.06%) and longer median progression-free survival (348 days versus 183 days) in NSCLC patients when 18F-MPG PET/CT SUVmax (maximum standard uptake value) was ≥2.23 versus <2.23. Our study demonstrates that 18F-MPG PET/CT is a powerful method for precise quantification of EGFR-activating mutation status in NSCLC patients, and it is a promising strategy for noninvasively identifying patients sensitive to EGFR-TKIs and for monitoring the efficacy of EGFR-TKI therapy.

The role of metabolic tumor volume (MTV) measured by [18F] FDG PET/CT in predicting EGFR gene mutation status in non-small cell lung cancer

Many noninvasive methods have been explored to determine the mutation status of the epidermal growth factor receptor (EGFR) gene, which is important for individualized treatment of non-small cell lung cancer (NSCLC). We evaluated whether metabolic tumor volume (MTV), a parameter measured by [18F] fluorodeoxyglucose positron emission tomography/computed tomography (PET/CT) might help predict EGFR mutation status in NSCLC. Overall, 87 patients who underwent EGFR genotyping and pretreatment PET/CT between January 2013 and September 2016 were reviewed. Clinicopathologic characteristics and metabolic parameters including MTV were evaluated. Univariate and multivariate analyses were used to assess the independent variables that predict mutation status to create prediction models. Forty-one patients (41/87) were identified as having EGFR mutations. The multivariate analysis showed that patients with lower MTV (MTV≤11.0 cm3, p=0.001) who were non-smokers (p=0.037) and had a peripheral tumor location (p=0.033) were more likely to have EGFR mutations. Prediction models using these criteria for EGFR mutation yielded a high AUC (0.805, 95% CI 0.712–0.899), which suggests that the analysis had good discrimination. In conclusion, NSCLC patients with EGFR mutations showed significantly lower MTV than patients with wild-type EGFR. Prediction models based on MTV and clinicopathologic characteristics could provide more information for the identification of EGFR mutations.

PET Imaging-Based Phenotyping as a Predictive Biomarker of Response to Tyrosine Kinase Inhibitor Therapy in Non-small Cell Lung Cancer: Are We There Yet?

The increased understanding of the molecular pathology of different malignancies, especially lung cancer, has directed investigational efforts to center on the identification of different molecular targets and on the development of targeted therapies against these targets. A good representative is the epidermal growth factor receptor (EGFR); a major driver of non-small cell lung cancer tumorigenesis. Today, tumor growth inhibition is possible after treating lung tumors expressing somatic mutations of the EGFR gene with tyrosine kinase inhibitors (TKI). This opened the doors to biomarker-directed precision or personalized treatments for lung cancer patients. The success of these targeted anticancer therapies depends in part on being able to identify biomarkers and their patho-molecular make-up in order to select patients that could respond to specific therapeutic agents. While the identification of reliable biomarkers is crucial to predict response to treatment before it begins, it is also essential to be able to monitor treatment early during therapy to avoid the toxicity and morbidity of futile treatment in non-responding patients. In this context, we share our perspective on the role of PET imaging-based phenotyping in the personalized care of lung cancer patients to non-invasively direct and monitor the treatment efficacy of TKIs in clinical practice.

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 of ¹⁸F-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 ¹⁸F-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. 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 = 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, FDR_Noether = 0.0032), as well as differentiating between KRAS-positive and EGFR+ (AUC = 0.65, FDR_Noether = 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.