Value of ¹⁸F-FDG uptake on PET/CT and CEA level to predict epidermal growth factor receptor mutations in pulmonary adenocarcinoma

Correlation between carcinoembryonic antigen (CEA) expression and EGFR mutations in non-small-cell lung cancer: a meta-analysis

OBJECTIVES

The purpose of this meta-analysis was to investigate the relationship between serum carcinoembryonic antigen (CEA) expression and epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC).

METHODS

Databases such as PubMed, Cochrane, EMBASE and Google Scholar were systematically searched to identify studies assessing the association of serum CEA expression with EGFR mutations. Across 19 studies, 4168 patients were included between CEA expression and EGFR mutations odds ratio (OR) conjoint analysis of correlations.

RESULTS

Compared with CEA-negative NSCLC, CEA-positive tumors had an increased EGFR mutation rate (OR = 1.85, 95% confidence interval: 1.48-2.32, P < 0.00001). This association was observed in both stage IIIB/IV patients (OR = 1.60, 95% CI: 1.18-2.15, P = 0.002) and stage I-IIIA (OR = 1.67, 95% CI: 1.01-2.77, P = 0.05) patients. In addition, CEA expression was associated with exon 19 (OR = 1.97, 95% CI: 1.25-3.11, P = 0.003) and exon 21 (OR = 1.51, 95% CI: 1.07-2.12, P = 0.02) EGFR mutations. In ADC pathological type had also showed the correlation (OR = 1.84, 95% CI: 1.31-2.57, P = 0.0004).

CONCLUSIONS

This meta-analysis indicated that serum CEA expression was associated with EGFR mutations in NSCLC patients. The results of this study suggest that CEA level may play a predictive role in the EGFR mutation status of NSCLC patients. Detecting serum CEA expression levels can give a good suggestion to those patients who are confused about whether to undergo EGFR mutation tests. Moreover, it may help better plan of the follow-up treatment.

Efficient 18F-fluorodeoxyglucose positron emission tomography/computed tomography-based machine learning model for predicting epidermal growth factor receptor mutations in non-small cell lung cancer

BACKGROUND

Beyond the human eye's limitations, radiomics provides more information that can be used for diagnosis. We develop a personalized and efficient model based on 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) to predict epidermal growth factor receptor (EGFR) mutations to help identify which non-small cell cancer (NSCLC) patients are candidates for EGFR-tyrosine kinase inhibitors (TKIs) therapy.

METHODS

We retrospectively included 100 patients with NSCLC and randomized them according to 70 patients in the training group and 30 patients in the validation group. The least absolute shrinkage and selection operator logistic regression (LLR) algorithm and support vector machine (SVM) classifier were used to build the models and predict whether EGFR is mutated or not. The predictive efficacy of the LLR algorithm-based model and the SVM classifier-based model was evaluated by plotting the receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC).

RESULTS

The AUC, sensitivity and specificity of our radiomics model by LLR algorithm were 0.792, 0.967, and 0.600 for the training group and 0.643, 1.00, and 0.378 for the validation group, respectively, in predicting EGFR mutations. The AUC was 0.838 for the training group and 0.696 for the validation group after combining radiomics features with clinical features. The prediction results based on the SVM classifier showed that the validation group had the best performance when based on radial kernel function with AUC, sensitivity, and specificity of 0.741, 0.667, and 0.825, respectively.

CONCLUSIONS

Radiomics models based on 18F-FDG PET/CT modeled with different machine learning algorithms can improve the predictive efficacy of the models. Models that combine clinical features are more clinically valuable.

Tumor-liver interface in MRI of liver metastasis enables prediction of EGFR mutation in patients with lung cancer: A proof-of-concept study

BACKGROUND

Preoperative prediction of the epidermal growth factor receptor (EGFR) status in non-small-cell lung cancer (NSCLC) patients with liver metastasis (LM) may have potential clinical values for assisting in treatment decision-making.

PURPOSE

To explore the value of tumor-liver interface (TLI)-based magnetic resonance imaging (MRI) radiomics for detecting the EGFR mutation in NSCLC patients with LM.

METHODS

This retrospective study included 123 and 44 patients from hospital 1 (between Feb. 2018 and Dec. 2021) and hospital 2 (between Nov. 2015 and Aug. 2022), respectively. The patients received contrast-enhanced T1-weighted (CET1) and T2-weighted (T2W) liver MRI scans before treatment. Radiomics features were extracted from MRI images of TLI and the whole tumor region, separately. The least absolute shrinkage and selection operator (LASSO) regression was used to screen the features and establish radiomics signatures (RSs) based on TLI (RS-TLI) and the whole tumor (RS-W). The RSs were evaluated by the receiver operating characteristic (ROC) curve analysis.

RESULTS

A total of 5 and 6 features were identified highly correlated with the EGFR mutation status from TLI and the whole tumor, respectively. The RS-TLI showed better prediction performance than RS-W in the training (AUCs, RS-TLI vs. RS-W, 0.842 vs. 0.797), internal validation (AUCs, RS-TLI vs. RS-W, 0.771 vs. 0.676) and external validation (AUCs, RS-TLI vs. RS-W, 0.733 vs. 0.679) cohort.

CONCLUSION

Our study demonstrated that TLI-based radiomics can improve prediction performance of the EGFR mutation in lung cancer patients with LM. The established multi-parametric MRI radiomics models may be used as new markers that can potentially assist in personalized treatment planning.

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.

FDG PET texture indices as imaging biomarkers for epidermal growth factor receptor mutation status in lung adenocarcinoma

Identifying the epidermal growth factor receptor (EGFR) mutation status is important for the optimal treatment of patients with EGFR mutations. We investigated the relationship between ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography (PET) texture indices and EGFR mutation status in patients with newly diagnosed lung adenocarcinoma. We retrospectively analyzed data of patients with newly diagnosed lung adenocarcinoma who underwent pretreatment FDG PET/computed tomography and EGFR mutation testing between August 2014 and November 2020. Patients were divided into mutated EGFR and wild-type EGFR groups. The maximum standardized uptake value (SUVmax) and 31 texture indices for the primary tumor were calculated from PET images and compared between the two groups. Of the 66 patients included, 22 had mutated EGFR and 44 had wild-type EGFR. The SUVmax did not significantly differ between the two groups. Among the 31 evaluated texture indices, the following five showed a statistically significant difference between the groups: correlation (P = 0.003), gray-level nonuniformity for run (P = 0.042), run length nonuniformity (P = 0.02), coarseness (P = 0.006), and gray-level nonuniformity for zone (P = 0.04). Based on the preliminary results of this study in a small patient population, FDG PET texture indices may be potential imaging biomarkers for the EGFR mutation status in patients with newly diagnosed lung adenocarcinoma.

Identification of EGFR mutation status in male patients with non-small-cell lung cancer: role of 18F-FDG PET/CT and serum tumor markers CYFRA21-1 and SCC-Ag

BACKGROUND

The high incidence of epidermal growth factor receptor (EGFR) mutations is usually found in female patients with lung adenocarcinoma who have never-smoked. However, reports concerning male patients are scarce. Thus, this study aimed to explore a novel approach based on ¹⁸F-fluoro-2-deoxy-2-deoxyglucose (¹⁸F-FDG) PET/CT and serum tumor markers (STMs) to determine EGFR mutation status in male patients with non-small-cell lung cancer (NSCLC).

METHODS

A total of 121 male patients with NSCLC were analyzed between October 2019 and March 2022. All patients underwent ¹⁸F-FDG PET/CT scan before treatment and monitored 8 STMs (cytokeratin 19 fragment [CYFRA21-1], squamous cell carcinoma-related antigen [SCC-Ag], carcinoembryonic antigen [CEA], neuron-specific enolase [NSE], carbohydrate antigen [CA] 50, CA125, CA72-4, and ferritin). A comparison was done between EGFR mutant and wild-type patients in terms of the maximum standardized uptake value of primary tumors (pSUV_max) and 8 STMs. We performed receiver operating characteristic (ROC) curve and multiple logistic regression analyses to determine predictors for EGFR mutation status.

RESULTS

EGFR mutations were detected in 39 patients (32.2%). Compared with patients with EGFR wild-type, EGFR-mutant patients had lower concentrations of serum CYRFA21-1 (2.65 vs. 4.01, P = 0.002) and SCC-Ag (0.67 vs. 1.05, P = 0.006). No significant differences of CEA, NSE, CA 50, CA125, CA72-4 and ferritin were found between the two groups. The presence of EGFR mutations was significantly associated with low pSUV_max (< 8.75), low serum SCC-Ag (< 0.79 ng/mL) and CYFRA21-1 (< 2.91 ng/mL) concentrations. The area under ROC curve values were 0.679, 0.655, 0.685 and 0.754, respectively, for low CYFRA21-1, SCC-Ag, pSUV_max and the combination of these three factors.

CONCLUSIONS

We demonstrated that low concentrations of CYFRA21-1 and SCC-Ag, as well as low pSUV_max, were associated with EGFR mutations, and that the combination of these factors resulted in a higher differentiation of EGFR mutation status in male patients with NSCLC.

Association Analysis of Maximum Standardized Uptake Values Based on 18F-FDG PET/CT and EGFR Mutation Status in Lung Adenocarcinoma

(1) Background: To investigate the association between maximum standardized uptake value (SUVmax) based on 18F-FDG PET/CT and EGFR mutation status in lung adenocarcinoma. (2) Methods: A total of 366 patients were retrospectively collected and divided into the EGFR mutation group (n = 228) and EGFR wild-type group (n = 138) according to their EGFR mutation status. The two groups’ general information and PET/CT imaging parameters were compared. A hierarchical binary logistic regression model was used to assess the interaction effect on the relationship between SUVmax and EGFR mutation in different subgroups. Univariate and multivariate logistic regression was used to analyze the association between SUVmax and EGFR mutation. After adjusting for confounding factors, a generalized additive model and smooth curve fitting were applied to address possible non-linearities. (3) Results: Smoking status significantly affected the relationship between SUVmax and EGFR mutation (p for interaction = 0.012), with an interaction effect. After adjusting for age, gender, nodule type, bronchial sign, and CEA grouping, in the smoking subgroup, curve fitting results showed that the relationship between SUVmax and EGFR mutation was approximately linear (df = 1.000, c2 = 3.897, p = 0.048); with the increase in SUVmax, the probability of EGFR mutation gradually decreased, and the OR value was 0.952 (95%CI: 0.908–0.999; p = 0.045). (4) Conclusions: Smoking status can affect the relationship between SUVmax and EGFR mutation status in lung adenocarcinoma, especially in the positive smoking history subgroup. Fully understanding the effect of smoking status will help to improve the accuracy of SUVmax in predicting EGFR mutations.

Predictive value of intratumor metabolic and heterogeneity parameters on [18F]FDG PET/CT for EGFR mutations in patients with lung adenocarcinoma

PURPOSE

This study aimed to investigate the value of metabolic and heterogeneity parameters of 2-deoxy-2[¹⁸F]fluoro-D-glucose ([¹⁸F]FDG) positron emission tomography/computed tomography (PET/CT) in predicting epidermal growth factor receptor (EGFR) mutations in patients with lung adenocarcinoma (ADC).

MATERIALS AND METHODS

A retrospective analysis was performed on 157 patients with lung ADC between September 2015 and June 2021, who had undergone both EGFR mutation testing and [¹⁸F]FDG PET/CT examination. Metabolic and heterogeneity parameters were measured and calculated, including maximum diameter (Dmax), maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume (MTV), total lesion glycolysis (TLG), and heterogeneity factor (HF). Relationships between PET/CT parameters and EGFR mutation status were evaluated and a multivariate logistic regression analysis was analyzed to establish a combined prediction model.

RESULTS

108 (68.8%) patients exhibited EGFR mutations. EGFR mutations were more likely to occur in females (51.9% vs. 48.1%, P = 0.007), non-smokers (83.3% vs. 16.7%, P < 0.001) and right lobes (55.6% vs. 44.4%, P = 0.017). High Dmax, MTV and HF and low SUVmean were significantly correlated with EGFR mutations, and the areas under the ROC curve (AUCs) measuring 0.647, 0.701, 0.757, and 0.661, respectively. Multivariate logistic regression analysis suggested that non-smokers (OR = 0.30, P = 0.034), low SUVmean (≤ 7.75, OR = 0.63, P < 0.001) and high HF (≥ 4.21, OR = 1.80, P = 0.027) were independent predictors of EGFR mutations. The AUC of the combined prediction model measured up to 0.863, significantly higher than that of a single parameter.

CONCLUSIONS

EGFR mutant in lung ADC patients showed more intratumor heterogeneity (HF) than EGFR wild type, which was combined clinical feature (non-smokers), and metabolic parameter (SUVmean) may be helpful in predicting EGFR mutation status, thus playing a guiding role in EGFR-tyrosine kinase inhibitors (EGFR-TKIs) targeted therapies.

Characterization of orthotopic xenograft tumor of glioma stem cells (GSCs) on MRI, PET and immunohistochemical staining

Introduction

The orthotopic xenograft tumors of human glioma stem cells (GSCs) is a recent glioma model with genotype and phenotypic characteristics close to human gliomas. This study aimed to explore the imaging and immunohistochemical characteristics of GSCs gliomas.

Methods

The rats underwent MRI and ¹⁸F-FDG PET scan in 6^th-8^th weeks after GSCs implantation. The MRI morphologic, DWI and PET features of the tumor lesions were assessed. In addition, the immunohistochemical features of the tumor tissues were further analyzed.

Results

Twenty-five tumor lesions were identified in 20 tumor-bearing rats. On structural MRI, the average tumor size was 30.04±17.31mm², and the intensity was inhomogeneous in 76.00% (19/25) of the lesions. The proportion of the lesions mainly presented as solid, cystic and patchy tumor were 60.00% (15/25), 16.00% (4/25) and 24.00% (6/25), respectively. The boundary was unclear in 88.00% (22/25), and peritumoral mass effect was observed in 92.00% (23/25) of the lesions. On DWI, 80.00% (20/25) of the lesions showed increased intensity. Of the 14 lesions in the 11 rats underwent PET scan, 57.14% (8/14) showed increased FDG uptake. On immunohistochemical staining, the expression of Ki-67 was strong in all the lesions (51.67%±11.82%). Positive EGFR and VEGF expression were observed in 64.71% (11/17) and 52.94% (9/17) of the rats, whereas MGMT and HIF-1α showed negative expression in all the lesions.

Discussion

GSC gliomas showed significant heterogeneity and invasiveness on imaging, and exhibited strong expression of Ki-67, partial expression of EGFR and VEGF, and weak expression of MGMT and HIF-1α on immunohistochemical staining.

Development and validation of MRI-based radiomics signatures as new markers for preoperative assessment of EGFR mutation and subtypes from bone metastases

Background

This study aimed to develop and externally validate contrast-enhanced (CE) T1-weighted MRI-based radiomics for the identification of epidermal growth factor receptor (EGFR) mutation, exon-19 deletion and exon-21 L858R mutation from MR imaging of spinal bone metastasis from primary lung adenocarcinoma.

Methods

A total of 159 patients from our hospital between January 2017 and September 2021 formed a primary set, and 24 patients from another center between January 2017 and October 2021 formed an independent validation set. Radiomics features were extracted from the CET1 MRI using the Pyradiomics method. The least absolute shrinkage and selection operator (LASSO) regression was applied for selecting the most predictive features. Radiomics signatures (RSs) were developed based on the primary training set to predict EGFR mutations and differentiate between exon-19 deletion and exon-21 L858R. The RSs were validated on the internal and external validation sets using the Receiver Operating Characteristic (ROC) curve analysis.

Results

Eight, three, and five most predictive features were selected to build RS-EGFR, RS-19, and RS-21 for predicting EGFR mutation, exon-19 deletion and exon-21 L858R, respectively. The RSs generated favorable prediction efficacies for the primary (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.851 vs. 0.816 vs. 0.814) and external validation (AUCs, RS-EGFR vs. RS-19 vs. RS-21, 0.807 vs. 0.742 vs. 0.792) sets.

Conclusions

Radiomics features from the CE MRI could be used to detect the EGFR mutation, increasing the certainty of identifying exon-19 deletion and exon-21 L858R mutations based on spinal metastasis MR imaging.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09985-4.

PET/CT Radiomic Features: A Potential Biomarker for EGFR Mutation Status and Survival Outcome Prediction in NSCLC Patients Treated With TKIs

Backgrounds

Epidermal growth factor receptor (EGFR) mutation profiles play a vital role in treatment strategy decisions for non–small cell lung cancer (NSCLC). The purpose of this study was to evaluate the predictive efficacy of baseline ¹⁸F-FDG PET/CT-based radiomics analysis for EGFR mutation status, mutation site, and the survival benefit of targeted therapy.

Methods

A sum of 313 NSCLC patients with pre-treatment ¹⁸F-FDG PET/CT scans and genetic mutations detection were retrospectively studied. Clinical and PET metabolic parameters were incorporated into independent predictors of determining mutation status and mutation site. The dataset was randomly allocated into the training and the validation sets in a 7:3 ratio. Three-dimensional (3D) radiomics features were extracted from each PET- and CT-volume of interests (VOI) singularly, and then a radiomics signature (RS) associated with EGFR mutation profiles is built by feature selection. Three different prediction models based on support vector machine (SVM), decision tree (DT), and random forest (RF) classifiers were established. Furthermore, nomograms for estimation of overall survival (OS) and progression-free survival (PFS) were established by integrating PET/CT radiomics score (Rad-score), metabolic parameters, and clinical factors. Predictive performance was assessed by the receiver operating characteristic (ROC) analysis and the calibration curve analysis. The decision curve analysis (DCA) was applied to estimate and compare the clinical usefulness of nomograms.

Results

Three hundred thirteen NSCLC patients were classified into a training set (n=218) and a validation set (n=95). Multivariate analysis demonstrated that SUVmax and sex were independent indicators of EGFR mutation status and mutation site. Eight CT-derived RS, six PET-derived RS, and two clinical factors were retained to develop integrated models, which exhibited excellent ability to distinguish between EGFR wild type (EGFR-WT), EGFR 19 mutation type (EGFR-19-MT), and EGFR 21 mutation type (EGFR-21-MT). The SVM model outperformed the RF model and the DT model, yielding training area under the curves (AUC) of EGFR-WT, EGFR-19-WT, and EGFR-21-WT, with 0.881, 0.851, and 0.849, respectively, and validation AUCs of 0.926, 0.805 and 0.859, respectively. For prediction of OS, the integrated nomogram is superior to the clinical nomogram and the radiomics nomogram, with C-indexes of 0.80 in the training set and 0.83 in the validation set, respectively.

Conclusions

The PET/CT-based radiomics analysis might provide a novel approach to predict EGFR mutation status and mutation site in NSCLC patients and could serve as useful predictors for the patients’ survival outcome of targeted therapy in clinical practice.

Clinical value of 18F-FDG PET/CT in IgG4-related disease

OBJECTIVE

To investigate the clinical value of ¹⁸F-fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in IgG4-related disease (IgG4-RD).

METHODS

Seventy two patients diagnosed with IgG4-RD who underwent PET/CT were included. Correlations between clinical variables and PET/CT findings were analyzed by Spearman's correlation test. Conventional radiology was compared to PET/CT to evaluate detection discrepancies. The detection ability of insidious organ involvement by PET/CT at disease onset was investigated. The utility value of PET/CT for the 2019 ACR/EULAR classification criteria was analyzed with the multivariate logistic analysis and ROC curve.

RESULTS

SUV_max of main involved organ was positively correlated with IgG4-RD Responder Index (IgG4-RD RI), serum and tissue IgG4 levels and IgG4/IgG ratio, serum eosinophils counts and number of involved organs, while negatively correlated with serum IgM levels. PET/CT was superior in detecting organ/tissue involvements including prostate, gastrointestinal tract and lung compared with conventional imaging. For patients with pancreato-hepato-biliary or head-neck involvements at onset, PET/CT showed superiority in detecting insidious lesions. Multivariate analysis showed that disease duration, multiple-organ involvement, SUV_max of main involved organ and mean SUV_max of all involved organs were significantly associated with the fulfillment of the 2019 ACR/EULAR classification criteria. ROC curves indicated that the cut-off value for SUV_max of main involved organ and mean SUV_max of all involved organs for fulfillment of the 2019 ACR/EULAR classification criteria for IgG4-RD were 4.1 and 3.5, respectively.

CONCLUSION

¹⁸F-FDG PET/CT has potential capacity to monitor disease activity, evaluate organ involvements and assist in the classification criteria in IgG4-RD.

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.

MRI-Based Radiomics Nomogram as a Potential Biomarker to Predict the EGFR Mutations in Exon 19 and 21 Based on Thoracic Spinal Metastases in Lung Adenocarcinoma

RATIONALE AND OBJECTIVES

Preoperative identifications of epidermal growth factor receptor (EGFR) mutation subtypes based on the MRI image of spinal metastases are needed to provide individualized therapy, but has not been previously investigated. This study aims to develop and evaluate an MRI-based radiomics nomogram for differentiating the exon 19 and 21 in EGFR mutation from spinal bone metastases in patients with primary lung adenocarcinoma.

MATERIALS AND METHODS

A total of 76 patients underwent T1-weighted and T2-weighted fat-suppressed MRI scans were enrolled in this study, 38 were positive for EGFR mutation in exon 19 and 38 were in exon 21.MRI imaging features were extracted and selected from each MRI pulse sequence, and used to form the radiomics signature. A radiomics nomogram was developed integrating the radiomics signature and important clinical factors with receiver operating characteristic, calibration and decision curve analysis to assess the nomogram. Clinical characteristics were analyzed with Mann-Whitney U and Chi-Square tests to identify the most important factors.

RESULTS

A total of 6 features were selected as the most discriminative predictors from the two MRI pulse sequences. The nomogram integrating the combined radiomics signature, age and CEA level generated good prediction performance in the training (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.90 vs. 0.87 vs. 0.59) and validation (AUCs, nomogram vs. combined radiomics signature vs. clinical model, 0.88 vs. 0.86 vs. 0.72) cohort. DCA analysis confirmed the potential clinical utility of the nomogram.

CONCLUSION

This study demonstrated that MRI features from spinal bone metastases can be used to prognosticate EGFR mutation subtypes in exon 19 and 21. The developed pre-treatment nomogram can potentially guide treatments for lung adenocarcinoma patients.

Correlation of epidermal growth factor receptor mutation status and PD-L1 expression with [18F]FDG PET using volume-based parameters in non-small cell lung cancer

OBJECTIVE

We investigated the relationship between 2-deoxy-2-[18F]fluoro-D-glucose (FDG) PET using volume-based parameters and epidermal growth factor receptor (EGFR) mutation status, programmed death-ligand-1 (PD-L1) expression level, and their combination, in pretreated non-small cell lung cancer (NSCLC).

METHODS

FDG PET findings and EGFR mutation status and PD-L1 expression level were investigated retrospectively in 93 patients with newly diagnosed NSCLC (77 adenocarcinomas, 16 squamous cell carcinomas). Tumors were divided into six groups: EGFR mutant/negative PD-L1, EGFR mutant/low PD-L1, EGFR mutant/high PD-L1, EGFR wild/negative PD-L1, EGFR wild/low PD-L1, and EGFR wild/high PD-L1. The maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG) for primary tumor were measured from PET images. The EGFR mutation status and PD-L1 expression level were estimated in tumor tissue specimens and compared with the PET parameters.

RESULTS

None of the PET parameters differed significantly between EGFR-mutated and wild-type EGFR. According to the PD-L1 level, significant differences were detected in SUVmax (P = 0.001) and TLG (P = 0.016), but not MTV. Comparing all six groups, significant difference was detected in only SUVmax (P = 0.011).

CONCLUSION

Based on the preliminary results of this study, FDG PET may help in the prediction of PD-L1 expression level, but not EGFR mutation status, in patients with newly diagnosed NSCLC. The SUVmax rather than MTV or TLG, may be of value in predicting the six groups according to the combination of EGFR mutation status and PD-L1 expression level.

Predictive value of multiple metabolic and heterogeneity parameters of 18F-FDG PET/CT for EGFR mutations in non-small cell lung cancer

OBJECTIVES

To explore the value of multiple metabolic and heterogeneity parameters of 2-deoxy-2-[fluorine-18] fluoro-D-glucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) in predicting epidermal growth factor receptor gene (EGFR) mutations in non-small cell lung cancer (NSCLC).

MATERIALS AND METHODS

A retrospective analysis was performed by reviewing 98 patients with NSCLC who underwent EGFR mutation testing and ¹⁸F-FDG PET/CT examination in our hospital between March 2016 and March 2021. Patients were divided into an EGFR-mutant group and a wild-type group. A multivariate logistic regression analysis was performed to screen and construct a prediction model. The diagnostic performance of the model was evaluated using a receiver-operating characteristic (ROC) curve.

RESULTS

The study found that EGFR mutations were more likely to occur in women, non-smokers, and patients with peripheral lesions, shorter maximum tumor diameter, adenocarcinoma, and T1 stage cancer. Low maximum standardized uptake value (SUVmax), mean standardized uptake value (SUVmean), metabolic tumor volume, total lesion glycolysis, and high coefficient of variation (COV) were significantly correlated with EGFR mutations, and the area under the ROC curve (AUC) was 0.622, 0.638, 0.679, 0.687, and 0.672, respectively. Multivariate logistic regression analysis indicated that non-smokers (odds ratio (OR) = 0.109, P = 0.014), peripheral lesions (OR = 6.917, P = 0.022), low SUVmax (≤ 7.85, OR = 5.471, P = 0.001), SUVmean (≤ 5.34, OR = 0.044, P = 0.000), and high COV (≥ 106.08, OR = 0.996, P = 0.045) were independent predictors of EGFR mutations. The AUC of the prediction model established by combining the above factors was 0.926; the diagnostic efficiency was significantly higher than that of a single parameter.

CONCLUSION

Among the metabolic and heterogeneity parameters of ¹⁸F-FDG PET/CT, low SUVmax, SUVmean, and high COV were significantly associated with EGFR mutations, and the predictive value of EGFR mutations could be enhanced when combined with clinicopathological features.

Subregional radiomics analysis for the detection of the EGFR mutation on thoracic spinal metastases from lung cancer

The present study intended to use radiomic analysis of spinal metastasis subregions to detect epidermal growth factor receptor (EGFR) mutation. In total, 94 patients with thoracic spinal metastasis originated from primary lung adenocarcinoma (2017-2020) were studied. All patients underwent T1-weighted (T1W) and T2 fat-suppressed (T2FS) MRI scans. The spinal metastases (tumor region) were subdivided into phenotypically consistent subregions based on patient- and population-level clustering: Three subregions, S1, S2 and S3, and the total tumor region. Radiomics features were extracted from each subregion and from the whole tumor region as well. Least shrinkage and selection operator (LASSO) regression were used for feature selection and radiomics signature definition. Detection performance of S3 was better than all other regions using T1W (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.720 versus 0.764 versus 0.786 versus 0.758) and T2FS (AUCs, S1 versus S2 versus S3 versus whole tumor, 0.791 versus 0.708 versus 0.838 versus 0.797) MRI. The multi-regional radiomics signature derived from the joint of inner subregion S3 from T1W and T2FS MRI achieved the best detection capabilities with AUCs of 0.879 (ACC = 0.774, SEN = 0.838, SPE = 0.840) and 0.777 (ACC = 0.688, SEN = 0.947, SPE = 0.615) in the training and test sets, respectively. Our study revealed that MRI-based radiomic analysis of spinal metastasis subregions has the potential to detect the EGFR mutation in patients with primary lung adenocarcinoma.

Frequent EGFR Mutations and Better Prognosis in Positron Emission Tomography-Negative, Solid-Type Lung Cancer

BACKGROUND

The differential diagnosis of a solitary solid-type lung nodule is diverse. 18F-fluorodeoxyglucose positron emission tomography (PET) has a high sensitivity in the diagnosis of solid-type lung cancers; however, PET-negative, solid-type lung cancers are rarely observed. In this study, we analyzed the clinical/genetic features and prognosis of PET-negative, solid-type lung cancers.

PATIENTS AND METHODS

Between January 2007 and February 2020, 709 patients with solid-type lung cancers (tumor size ≥2.0 cm) underwent pulmonary resection. Clinical, genetic, and prognostic features were evaluated in 27 patients (3.8%) with tumors showing negative PET results defined as SUVmax <2.0.

RESULTS

All 27 patients had lung adenocarcinoma; 23 had invasive adenocarcinomas and 4 had invasive mucinous adenocarcinomas. The PET-negative group showed high frequencies of females and never-smokers. Recurrence-free survival was significantly better in the PET-negative group compared with PET-positive counterparts extracted using propensity score matching from patients who underwent pulmonary resection during the same period (P = .0052). Furthermore, 83% of PET-negative, solid-type invasive lung adenocarcinoma patients harbored EGFR mutation, which was significantly higher than that of PET-positive, solid-type invasive lung adenocarcinoma patients (38%, n = 225) who received EGFR mutation testing in our cohort (P < .0001). PET-negative, solid-type lung adenocarcinoma patients with EGFR mutations had significantly better recurrence-free survival compared with PET-positive, solid-type lung adenocarcinoma patients with EGFR mutations extracted using propensity score matching (P = .0030).

CONCLUSION

PET-negative, solid-type lung cancers are characterized with a high incidence of EGFR mutation and a better prognosis compared with PET-positive, solid-type lung cancer.

Computed Tomography Imaging Characteristics: Potential Indicators of Epidermal Growth Factor Receptor Mutation in Lung Adenocarcinoma

PURPOSE

The purpose of this study was to investigate the correlation between computed tomography imaging characteristics in lung adenocarcinoma and epidermal growth factor receptor (EGFR) mutations.

METHODS

A total of 124 patients with lung adenocarcinoma and known EGFR mutation status were collected in this retrospective study. Computed tomography quantitative parameters of each tumor, including total volume, total surface, surface-to-volume ratio (SVR), average diameter, maximum diameter, and average density, were determined using computer-aided detection software. The correlation between the EGFR mutation status and imaging characteristics was assessed. The predictive value of these imaging characteristics for EGFR mutation was calculated using the area under the receiver operating characteristic curve.

RESULT

Fifty-eight of 124 patients showed EGFR mutations. Patients who are female (P < 0.001) and nonsmokers (P < 0.001) and those with serum carcinoembryonic antigen (CEA) level of ≥5 (P = 0.035) were likely to have EGFR mutation. Computed tomography features including air bronchogram (P = 0.035), absence of cavitation (P = 0.010), and absence of pulmonary emphysema (P = 0.002) and quantitative parameters, such as smaller total surface (P = 0.002), smaller total volume (P = 0.001), higher SVR (P = 0.003), and smaller average diameter (P = 0.001), were associated with EGFR mutation. Logistic regression analysis revealed that the most significant independent prognostic factors of EGFR mutation for the model were nonsmoking (P = 0.035), CEA level of ≥5 (P = 0.004), presence of air bronchogram (P = 0.040), absence of cavitation (P = 0.021), and high SVR (P = 0.014). The area under the receiver operating characteristic curve, sensitivity, and specificity of the model for predicting EGFR mutation were 0.827, 75.8%, and 82.8%, respectively.

CONCLUSIONS

EGFR-mutated adenocarcinoma showed significantly increased CEA level, presence of air bronchogram, absence of cavitation, and higher quantitative parameter SVR than those with wild-type EGFR.

MRI-based radiomics analysis for predicting the EGFR mutation based on thoracic spinal metastases in lung adenocarcinoma patients

PURPOSE

This study aims to develop and evaluate multi-parametric MRI-based radiomics for preoperative identification of epidermal growth factor receptor (EGFR) mutation, which is important in treatment planning for patients with thoracic spinal metastases from primary lung adenocarcinoma.

METHODS

A total of 110 patients were enrolled between January 2016 and March 2019 as a primary cohort. A time-independent validation cohort was conducted containing 52 patients consecutively enrolled from July 2019 to April 2021. The patients were pathologically diagnosed with thoracic spinal metastases from primary lung adenocarcinoma; all underwent T1-weighted (T1W), T2-weighted (T2W), and T2-weighted fat-suppressed (T2FS) MRI scans of the thoracic spinal. Handcrafted and deep learning-based features were extracted and selected from each MRI modality, and used to build the radiomics signature. Various machine learning classifiers were developed and compared. A clinical-radiomics nomogram integrating the combined rad signature and the most important clinical factor was constructed with receiver operating characteristic (ROC), calibration, and decision curves analysis (DCA) to evaluate the prediction performance.

RESULTS

The combined radiomics signature derived from the joint of three modalities can effectively classify EGFR mutation and EGFR wild-type patients, with an area under the ROC curve (AUC) of 0.886 (95% confidence interval [CI]: 0.826-0.947, SEN =0.935, SPE =0.688) in the training group and 0.803 (95% CI: 0.682-0.924, SEN = 0.700, SPE = 0.818) in the time-independent validation group. The nomogram incorporating the combined radiomics signature and smoking status achieved the best prediction performance in the training (AUC = 0.888, 95% CI: 0.849-0.958, SEN = 0.839, SPE = 0.792) and time-independent validation (AUC = 0.821, 95% CI: 0.692-0.929, SEN = 0.667, SPE = 0.909) cohorts. The DCA confirmed potential clinical usefulness of our nomogram.

CONCLUSION

Our study demonstrated the potential of multi-parametric MRI-based radiomics on preoperatively predicting the EGFR mutation. The proposed nomogram model can be considered as a new biomarker to guide the selection of individual treatment strategies for patients with thoracic spinal metastases from primary lung adenocarcinoma.

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.

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.

Value of radiomics model based on multi-parametric magnetic resonance imaging in predicting epidermal growth factor receptor mutation status in patients with lung adenocarcinoma

Background

The epidermal growth factor receptor (EGFR) is an important therapeutic target for patients with non-small-cell lung cancer (NSCLC). Radiomics and radiogenomics have emerged as attractive research topics aiming to extract mineable high-dimensional features from medical images and show potential to correlate with the gene mutation. Herein, we aim to develop a magnetic resonance imaging (MRI)-based radiomics model for pretreatment prediction of the EGFR status in patients with lung adenocarcinoma.

Methods

A total of 92 patients with pathologically confirmed lung adenocarcinoma were retrospectively enrolled in this study. EGFR genotype was analyzed by sequence testing. All patients were randomized into training and test group in a 7:3 ratio using the R software. Radiomics features were extracted from T2 weighted imaging (T2WI), diffusion-weighted imaging (DWI), and apparent diffusion coefficient (ADC); radiomics signatures were built using the least absolute shrinkage and selection operator (LASSO) and logistic regression. Preoperative clinical factors and image features associated with EGFR were also evaluated. A nomogram including sex, smoking status, and radiomics signatures was constructed. A total of five radiomics models were built, and the area under the curve (AUC) was used to evaluate their performance of EGFR mutation prediction.

Results

Among the three single-sequence models, the ADC model showed the best prediction performance. The AUCs of the ADC, DWI, T2WI prediction model in the test cohort were 0.805 (95% CI: 0.610 to 1.000), 0.722 (95% CI: 0.519 to 0.924), and 0.655 (95% CI: 0.438 to 0.872), respectively. Compared with the single-sequence model, the multi-sequence prediction model showed better performed [AUC_test =0.838 (95% CI: 0.685 to 0.992)]. The AUC of the nomogram in the training group was 0.925 (95% CI: 0.855 to 0.994) and 0.727 (95% CI: 0.531 to 0.924) in the test group, respectively.

Conclusions

The radiomics model based on MRI might have the potential to predict EGFR mutation in patients with lung adenocarcinoma. The multi-sequence model had better performance than other models.

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.

Biological Significance of 18F-FDG PET/CT Maximum Standard Uptake Value for Predicting EGFR Mutation Status in Non-Small Cell Lung Cancer Patients

Purpose

To investigate the potential of maximum standardized uptake value (SUVmax) in predicting epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) patients.

Methods

Clinical data of 311 NSCLC patients who had undergone both EGFR mutation test and ¹⁸F-FDG PET/CT scans between January 2013 and December 2017 at our hospital were retrospectively analyzed. Patients were sub-grouped by their origin of SUVmax. Univariate and multivariate analyses were performed to investigate the association between clinical factors and EGFR mutations. Receiver operating characteristic curve (ROC) analysis was performed to confirm the predictive value of clinical factors. In vitro experiments were performed to confirm the correlation between EGFR mutations and glycolysis.

Results

EGFR-mutant patients had higher SUVmax than the wild-type patients in both primary tumors and metastases. In the multivariate analysis, SUVmax, gender and histopathologic type were determined as independent predictors of EGFR mutation status for patients whose SUVmax were obtained from the primary tumors; while for patients whose SUVmax were obtained from the metastases, SUVmax, smoking status and histopathologic type were regarded as independent predictors. ROC analysis showed that SUVmax of the primary tumors (cut off >10.92), not of the metastases, has better predictive value than other clinical factors in predicting EGFR mutation status. The predict performance was improved after combined SUVmax with other independent predictors. In addition, our in vitro experiments demonstrated that lung cancer cells with EGFR mutations have higher aerobic glycolysis level than wild-type cells.

Conclusion

SUVmax of the primary tumors has the potential to serve as a biomarker to predict EGFR mutation status in NSCLC patients.

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

Objective

To assess the performance of pretreatment ¹⁸F-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomics features for predicting EGFR mutation status in patients with non-small cell lung cancer (NSCLC).

Patients and Methods

We enrolled total 173 patients with histologically proven NSCLC who underwent preoperative ¹⁸F-FDG PET/CT. Tumor tissues of all patients were tested for EGFR mutation status. A PET/CT radiomics prediction model was established through multi-step feature selection. The predictive performances of radiomics model, clinical features and conventional PET-derived semi-quantitative parameters were compared using receiver operating curves (ROCs) analysis.

Results

Four CT and two PET radiomics features were finally selected to build the PET/CT radiomics model. Compared with area under the ROC curve (AUC) equal to 0.664, 0.683 and 0.662 for clinical features, maximum standardized uptake values (SUV_max) and total lesion glycolysis (TLG), the PET/CT radiomics model showed better performance to discriminate between EGFR positive and negative mutations with the AUC of 0.769 and the accuracy of 67.06% after 10-fold cross-validation. The combined model, based on the PET/CT radiomics and clinical feature (gender) further improved the AUC to 0.827 and the accuracy to 75.29%. Only one PET radiomics feature demonstrated significant but low predictive ability (AUC = 0.661) for differentiating 19 Del from 21 L858R mutation subtypes.

Conclusions

EGFR mutations status in patients with NSCLC could be well predicted by the combined model based on ¹⁸F-FDG PET/CT radiomics and clinical feature, providing an alternative useful method for the selection of targeted therapy.

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.

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

Purpose: Epidermal growth factor receptor (EGFR) mutation is the most common target for precision treatment in metastatic lung adenocarcinoma. We investigated the predictive role of ¹⁸F-FDG PET/CT and clinicopathological features for EGFR mutations in lung adenocarcinoma with bone metastasis. Methods: Seventy-five lung adenocarcinoma patients with histologically confirmed bone metastasis were included. They all received EGFR status test and PET/CT before systemic treatment. The differences of maximum standardized uptake value (SUVmax) in primary tumor (pSUVmax), regional lymph node (nSUVmax) and bone metastasis (bmSUVmax) between different EGFR status groups were compared, alongside with common clinicopathological features. Multivariate logistic regression analysis was performed to evaluate predictors of EGFR mutations. Results: EGFR mutations were found in 37 patients (49.3%). EGFR mutations were more common in females, non-smokers, expression of Thyroid Transcription Factor-1 (TTF-1) and NaspinA. Low bmSUVmax was significantly associated with EGFR mutations, while no significant difference was observed in pSUVmax and nSUVmax. Multivariate analysis showed that bmSUVmax ≤7, non-smoking, expression of TTF-1 were predictors of EGFR mutations. The area under the curve (AUC) of receiver operating characteristic (ROC) curve was 0.84 for the combination of the three factors. Conclusion: Low bmSUVmax is more frequently in EGFR mutations, and bmSUVmax is an independent predictor of EGFR mutations. Combining bmSUVmax with other clinicopathological features could forecast the EGFR status in lung adenocarcinoma with unavailable EGFR gene testing.

Relationship of radiometabolic biomarkers to KRAS mutation status and ALK rearrangements in cases of lung adenocarcinoma

PURPOSE

Rapid diagnosis of genetic mutations is important for targeted therapies such as EGFR tyrosine kinase inhibitors. KRAS mutation and ALK rearrangement are also important in determining treatment. The purpose of our study was to evaluate the diagnostic value of 18F-FDG PET to predict KRAS mutation and ALK rearrangement in order to determine the frequency of these genetic markers in our lung adenocarcinoma cases and contribute to forthcoming meta-analysis studies.

METHODS

A total of 218 patients with lung adenocarcinoma (EGFR analyzed) who were seen at our clinic between 2012 and 2014 were included in the study. The results of the 18 F-FDG-PET scans for each patient were retrospectively recorded with the associated medical documents. ALK rearrangements were analyzed in 166 of the 218 patients, while 50 of the 218 patients were analyzed for KRAS mutational status. SPSS 15.0 for Windows was used for statistical analysis.

RESULTS

FDG avidity was higher in cases with KRAS mutations and ALK rearrangements than those without, but the difference was not significant. ALK rearrangements were more common in younger, female, and nonsmoking patients with lung adenocarcinoma.

CONCLUSIONS

The small numbers of KRAS mutations and ALK rearrangements are the limitation of this study for evaluation of diagnostic imaging. The frequency of these genetic alterations was as reported in the literature. We believe that our work will contribute to future meta-analysis.

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.

Association of radiomic features with epidermal growth factor receptor mutation status in non-small cell lung cancer and survival treated with tyrosine kinase inhibitors

Since the discovery of the fact that tyrosine kinase inhibitors could improve progression-free survival for patients with advanced non-small cell lung cancer compared with traditional chemotherapy, it has been extremely important to identify epidermal growth factor receptor mutation status in treatment stratification. Although lack of sufficient biopsy samples limit the precise detection of epidermal growth factor receptor mutation status in clinical practice, and it is difficult to identify the sensitive patients who confer favorable response to tyrosine kinase inhibitors. An increasing number of scholars tried to deal with these problems using methods based on the non-invasive imaging including computed tomography and PET to find the association with epidermal growth factor receptor mutation status and survival treated with tyrosine kinase inhibitor in non-small cell lung cancer. Although the conclusions have not reached a consensus, quantitative and high-throughput radiomics have brought us a new direction and might successfully help identify patients undergoing tyrosine kinase inhibitors who could get significant benefits.

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.

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

Background

The purpose of this study was to investigate an association between EGFR mutation status and

¹⁸F‐fluorodeoxyglucose positron emission tomography‐computed tomography (¹⁸F‐FDG PET‐CT) image features in lung adenocarcinoma.

Methods

Retrospective analysis of the data of 139 patients with lung adenocarcinoma confirmed by surgical pathology who underwent preoperative ¹⁸F‐FDG PET‐CT was conducted. Correlations between EGFR mutation status, clinical characteristics, and PET‐CT parameters, including the maximum standardized uptake value (SUVmax), the mean of the SUV (SUVmean), the peak of the SUV (SUVpeak) of the primary tumor, and the ratio of SUVmax between the primary tumor and the mediastinal blood pool (SUVratio), were statistically analyzed. Multivariate logistic regression analysis was performed to identify predictors of EGFR mutation. Receiver operating characteristic curves of statistical quantitative parameters were compared.

Results

EGFR mutations were detected in 74 (53.2%) of the 139 lung adenocarcinomas and were more frequent in non‐smoking patients. Univariate analysis showed that the SUVmax, SUVmean, SUVpeak, and SUVratio were lower in EGFR‐mutated than in wild‐type tumors. The receiver operating characteristic curves showed no significant differences between their diagnostic efficiencies. Multivariate logistic regression analysis showed that being a never smoker was an independent predictor of EGFR mutation.

Conclusion

Quantitative parameters based on ¹⁸F‐FDG PET‐CT have modest power to predict the presence of EGFR mutation in lung adenocarcinoma; however, when compared to smoking history, they are not good or significant predictive factors.

Prediction of EGFR mutations by conventional CT-features in advanced pulmonary adenocarcinoma

OBJECTIVE

This study assessed the ability of conventional computed tomography (CT) features (including primary tumors, metastatic lesions, lymph nodes, and emphysema) to predict epidermal growth factor receptor (EGFR) mutations in advanced pulmonary adenocarcinoma.

METHODS

Patients who were diagnosed with advanced pulmonary adenocarcinoma between January 2017 and August 2017 and had undergone a chest CT and EGFR mutation testing were enrolled in this retrospective study. Qualitative and quantitative CT-features and clinical characteristics evaluated in this study included: primary tumor location, size, and morphology (including degree of lobulation, density, calcification, cavitation, vacuole sign, and air bronchogram), size and distribution of lung and pleural metastatic nodules, size and status of hilar and mediastinal lymph nodes, emphysema, organs with distant metastasis, and patient age, sex, and smoking history.

RESULTS

Of 201 patients, 107 (53.23%) were EGFR-mutation positive. The multivariate logistic regression indicated that EGFR mutations were significantly associated with smaller lymph nodes, a lower percentage of deep lobulation of the primary tumor and partial fusion of lymph nodes, and absence of emphysema. The area under the curve of logistic regression model for predicting EGFR mutations was 0.898.

CONCLUSIONS

Conventional CT-features, including emphysema, degree of primary tumor lobulation, and lymph node size and status, help to predict the presence or absence of EGFR mutations in advanced pulmonary adenocarcinoma. Additionally, these same CT-features demonstrated that the CT manifestations of the EGFR mutant group were of relatively lower malignancy and less invasive as compared to the wild-type EGFR group.

EGFR mutation decreases FDG uptake in non‑small cell lung cancer via the NOX4/ROS/GLUT1 axis

[18F]fluoro‑2‑deoxyglucose (FDG) positron emission tomography (PET)‑computed tomography (CT) is a functional imaging modality based on glucose metabolism. The association between the maximum standardized uptake value (SUVmax) from 18F‑FDG PET‑CT scanning and epidermal growth factor receptor (EGFR) mutation status has, to the best of our knowledge, not previously been fully elucidated, and the potential mechanisms by which EGFR mutations alter FDG uptake are largely unknown. A total of 157 patients who were pathologically diagnosed with non‑small cell lung cancer (NSCLC) who underwent EGFR mutation testing and PET‑CT pretreatment between June 2015 and October 2017 were retrospectively analyzed. χ2 and univariate analyses were performed to identify the contributors to EGFR mutation. The receiver operating characteristic (ROC) curve was analyzed, and the area under the curve (AUC) was calculated. Glucose transporter 1 (GLUT1) and NADPH oxidase 4 (NOX4) expression, and reactive oxygen species (ROS) activity were detected in the A549 (wild‑type), PC‑9 (EGFR mutation‑positive, EGFR exon 19del) and NCI‑H1975 (EGFR mutation‑positive, combined with L858R and T790M substitution) cell lines. A total of 109 patients who met the criteria were enrolled, and 63 of those tested as EGFR mutation‑positive. The SUVmax values were significantly lower in patients with EGFR mutations (mean, 6.52±0.38) compared with in patients with wild‑type EGFR (mean, 9.37±0.31; P<0.001). Using univariate analysis, EGFR mutation status was significantly associated with sex, smoking status, tumor histology and SUVmax of the primary tumor. In the multivariate analysis, smoking status (never‑smoking), histopathology (adenocarcinoma) and SUVmax (≤9.91) were the statistically significant predictors of EGFR mutations. ROC curve analysis identified that the SUVmax cut‑off point was 9.92, for which the AUC was 0.75 (95% confidence interval, 0.68‑0.83). Reverse transcription‑polymerase chain reaction indicated that the GLUT1 mRNA decreased in the PC‑9 and NCI‑H1975 cell lines compared with the A549 cell line (0.82±0.07 and 0.72±0.04 vs. 0.98±0.04, respectively; P<0.05) and decreased ROS activity was observed in the PC‑9 cell line. Furthermore, the expression of NOX4 mRNA decreased by 20% in PC‑9 (P<0.01) and by 14% (P<0.05) in NCI‑H1975 cells. In addition, NOX4 protein expression decreased by 13% in PC‑9 and by 16% in NCI‑H1975 cells (both P<0.05) compared with the A549 cell line. The SUVmax could be considered to effectively predict EGFR mutation status of patients with NSCLC, and the EGFR mutation status may alter FDG uptake partially via the NOX4/ROS/GLUT1 axis.

CD147-mediated glucose metabolic regulation contributes to the predictive role of 18 F-FDG PET/CT imaging for EGFR-TKI treatment sensitivity in NSCLC

The aim of this study is to investigate the role of CD147 in glucose metabolic regulation and its association with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment sensitivity prediction using ¹⁸ F-fluorodeoxyglucose (¹⁸ F-FDG) PET/CT imaging in non-small cell lung cancer (NSCLC). In this study, four human NSCLC cell lines with different EGFR-TKI responses were used to detect p-EGFR/EGFR and CD147 expression via Western blotting and flow cytometric analyses. Radioactive uptake of ¹⁸ F-FDG by established stable NSCLC cell lines (HCC827, H1975) with different levels of CD147 expression and the corresponding xenografts was assessed through γ-radioimmunoassays in vitro and micro-PET/CT imaging in vivo to study the role of CD147 in glucose metabolic reprogramming. Correlation analyses were performed to investigate the association between CD147 expression and PD-L1 expression in stable NSCLC cell lines. Higher CD147 expression was found in EGFR-TKI-sensitive NSCLC cell lines than in relatively resistant NSCLC cell lines (HCC827>PC9>A549>H1975). CD147 could promote ¹⁸ F-FDG uptake by HCC827 and H1975 cells in vitro and in vivo through an EGFR-initiated Akt/mTOR-dependent signaling pathway. Programmed cell death-ligand 1 (PD-L1) expression was positively correlated with CD147 expression in human NSCLC cell lines. EGFR-TKI treatment sensitivity prediction in NSCLC using ¹⁸ F-FDG PET/CT imaging significantly correlated with CD147-mediated glucose metabolic regulation via the Akt/mTOR-dependent pathway. Moreover, PD-L1 expression in NSCLC cell lines could be regulated by CD147, suggesting a potential immunosuppression induced by the upregulation of tumor glucose metabolism.

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.

Non-invasive tumor genotyping using radiogenomic biomarkers, a systematic review and oncology-wide pathway analysis

With targeted treatments playing an increasing role in oncology, the need arises for fast non-invasive genotyping in clinical practice. Radiogenomics is a rapidly evolving field of research aimed at identifying imaging biomarkers useful for non-invasive genotyping. Radiogenomic genotyping has the advantage that it can capture tumor heterogeneity, can be performed repeatedly for treatment monitoring, and can be performed in malignancies for which biopsy is not available. In this systematic review of 187 included articles, we compiled a database of radiogenomic associations and unraveled networks of imaging groups and gene pathways oncology-wide. Results indicated that ill-defined tumor margins and tumor heterogeneity can potentially be used as imaging biomarkers for 1p/19q codeletion in glioma, relevant for prognosis and disease profiling. In non-small cell lung cancer, FDG-PET uptake and CT-ground-glass-opacity features were associated with treatment-informing traits including EGFR-mutations and ALK-rearrangements. Oncology-wide gene pathway analysis revealed an association between contrast enhancement (imaging) and the targetable VEGF-signalling pathway. Although the need of independent validation remains a concern, radiogenomic biomarkers showed potential for prognosis prediction and targeted treatment selection. Quantitative imaging enhanced the potential of multiparametric radiogenomic models. A wealth of data has been compiled for guiding future research towards robust non-invasive genomic profiling.

Relation of EGFR Mutation Status to Metabolic Activity in Localized Lung Adenocarcinoma and Its Influence on the Use of FDG PET/CT Parameters in Prognosis

OBJECTIVE

The purposes of this study were to assess the relation between epidermal growth factor receptor (EGFR) mutation status and FDG PET/CT findings and to evaluate the influence of this relation on the use of FDG PET/CT parameters to establish a prognosis in cases of localized lung adenocarcinoma.

MATERIALS AND METHODS

Patients with stage I and II lung adenocarcinomas were retrospectively enrolled. At the initial FDG PET/CT examination, maximum and peak standardized uptake, tumor-to-background ratio, and volumetric parameters of metabolic tumor volume and total lesion glycolysis were measured.

RESULTS

The values of all the metabolic and volumetric FDG PET/CT parameters were significantly lower in EGFR mutant than in EGFR wild-type lung adenocarcinomas. All parameters were statistically significant for predicting recurrence-free survival. In multivariate analyses, peak standardized uptake and total lesion glycolysis were more significant prognostic factors than was TNM stage (p < 0.001). Optimal cutoff values of parameters for predicting recurrence-free survival were slightly different between the two groups.

CONCLUSION

EGFR mutation is related to low metabolic activity of localized lung adenocarcinoma at FDG PET/CT. Because of differences in the metabolic activity of EGFR mutant and wild-type tumors, EGFR mutation status must be considered when FDG PET/CT parameters are used for prognosis.

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.

Value of combining serum carcinoembryonic antigen and PET/CT in predicting EGFR mutation in non-small cell lung cancer

Background

We sought to investigate the associations between pretreatment serum Carcinoembryonic antigen (CEA) level, ¹⁸F-Fluoro-2-deoxyglucose (¹⁸F-FDG) uptake value of primary tumor and epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC).

Methods

We retrospectively reviewed medical records of 210 NSCLC patients who underwent EGFR mutation test and ¹⁸F-FDG positron emission tomography/computed tomography (PET/CT) scan before anti-tumor therapy. The associations between EGFR mutations and patients' characteristics, serum CEA, PET/CT imaging characteristics maximal standard uptake value (SUVmax) of the primary tumor were analyzed. Receiver-operating characteristic (ROC) curve was used to assess the predictive value of these factors.

Results

EGFR mutations were found in 70 patients (33.3%). EGFR mutations were more common in high CEA group (CEA ≥7.0 ng/mL) than in low CEA group (CEA <7.0 ng/mL) (40.4% vs. 27.6%; P=0.05). Females (P<0.001), non-smokers (P<0.001), patients with adenocarcinoma (P<0.001) and SUVmax <9.0 (P=0.001) were more likely to be EGFR mutation-positive. Multivariate analysis revealed that gender, tumor histology, pretreatment serum CEA level, and SUVmax were the most significant predictors for EGFR mutations. The ROC curve revealed that combining these four factors yielded a higher calculated AUC (0.80).

Conclusions

Gender, histology, pretreatment serum CEA level and SUVmax are significant predictors for EGFR mutations in NSCLC. Combining these factors in predicting EGFR mutations has a moderate diagnostic accuracy, and is helpful in guiding anti-tumor treatment.

Combining PET/CT with serum tumor markers to improve the evaluation of histological type of suspicious lung cancers

OBJECTIVE

Histological type is important for determining the management of patients with suspicious lung cancers. In this study, PET/CT combined with serum tumor markers were used to evaluate the histological type of lung lesions.

MATERIALS AND METHODS

Patients with suspicious lung cancers underwent 18F-FDG PET/CT and serum tumor markers detection. SUVmax of the tumor and serum levels of tumor markers were acquired. Differences in SUVmax and serum levels of tumor markers among different histological types of lung cancers and between EGFR mutation statues of adenocarcinoma were compared. The diagnostic efficiencies of SUVmax alone, each serum tumor marker alone, combined tumor markers and the combination of both methods were further assessed and compared.

RESULTS

SCC had the highest level of SUVmax, followed by SCLC and adenocarcinoma, and benign lesions had a lowest level. CYFRA21-1 and SCC-Ag were significantly higher in SCC, NSE was significantly higher in SCLC (P<0.001), and CEA was higher in adenocarcinoma (P = 0.343). The diagnostic efficiencies in evaluating histological types of suspicious lung cancers were insufficient when using each serum tumor marker or SUVmax alone. When combined, the AUC, sensitivity and specificity increased significantly (P<0.05 for all). Additionally, to adenocarcinoma, no significant difference was found between EGFR mutation statuses in SUVmax or serum tumor markers (P>0.05 for all).

CONCLUSIONS

SUVmax and serum tumor markers show values in evaluating the histological types of suspicious lung cancers. When properly combined, the diagnostic efficiency can increase significantly.

Correlation of EGFR or KRAS mutation status with 18F-FDG uptake on PET-CT scan in lung adenocarcinoma

Background

¹⁸F-fluoro-2-deoxy-glucose (¹⁸F-FDG) positron emission tomography (PET) is a functional imaging modality based on glucose metabolism. The correlation between EGFR or KRAS mutation status and the standardized uptake value (SUV) of ¹⁸F-FDG PET scanning has not been fully elucidated.

Methods

Correlations between EGFR or KRAS mutation status and clinicopathological factors including SUV_max were statistically analyzed in 734 surgically resected lung adenocarcinoma patients. Molecular causal relationships between EGFR or KRAS mutation status and glucose metabolism were then elucidated in 62 lung adenocarcinomas using cap analysis of gene expression (CAGE), a method to determine and quantify the transcription initiation activities of mRNA across the genome.

Results

EGFR and KRAS mutations were detected in 334 (46%) and 83 (11%) of the 734 lung adenocarcinomas, respectively. The remaining 317 (43%) patients had wild-type tumors for both genes. EGFR mutations were more frequent in tumors with lower SUV_max. In contrast, no relationship was noted between KRAS mutation status and SUV_max. CAGE revealed that 4 genes associated with glucose metabolism (GPI, G6PD, PKM2, and GAPDH) and 5 associated with the cell cycle (ANLN, PTTG1, CIT, KPNA2, and CDC25A) were positively correlated with SUV_max, although expression levels were lower in EGFR-mutated than in wild-type tumors. No similar relationships were noted with KRAS mutations.

Conclusions

EGFR-mutated adenocarcinomas are biologically indolent with potentially lower levels of glucose metabolism than wild-type tumors. Several genes associated with glucose metabolism and the cell cycle were specifically down-regulated in EGFR-mutated adenocarcinomas.

Relationship of Radiometabolic Biomarkers to KRAS Mutation Status and ALK Rearrangements in Cases of Lung Adenocarcinoma

Purpose Rapid diagnosis of genetic mutations is important for targeted therapies such as EGFR tyrosine kinase inhibitors. KRAS mutation and ALK rearrangement are also important in determining treatment. The purpose of our study was to evaluate the diagnostic value of 18F-FDG PET to predict KRAS mutation and ALK rearrangement in order to determine the frequency of these genetic markers in our lung adenocarcinoma cases and contribute to forthcoming meta-analysis studies. Methods A total of 218 patients with lung adenocarcinoma (EGFR analyzed) who were seen at our clinic between 2012 and 2014 were included in the study. The results of the 18 F-FDG-PET scans for each patient were retrospectively recorded with the associated medical documents. ALK rearrangements were analyzed in 166 of the 218 patients, while 50 of the 218 patients were analyzed for KRAS mutational status. SPSS 15.0 for Windows was used for statistical analysis. Results FDG avidity was higher in cases with KRAS mutations and ALK rearrangements than those without, but the difference was not significant. ALK rearrangements were more common in younger, female, and nonsmoking patients with lung adenocarcinoma. Conclusions The small numbers of KRAS mutations and ALK rearrangements are the limitation of this study for evaluation of diagnostic imaging. The frequency of these genetic alterations was as reported in the literature. We believe that our work will contribute to future meta-analysis.

Contribution of ¹⁸Fluorodeoxyglucose positron emission tomography uptake and TTF-1 expression in the evaluation of the EGFR mutation in patients with lung adenocarcinoma

AIM

The aim of this study is to evaluate the diagnostic value of PET-CT scan for the prediction of EGFR mutation status and the contribution of TTF-1 expression to PET-CT scan.

METHODS

We retrospectively studied 218 cases with a diagnosis of pulmonary adenocarcinoma between 2012-2014 which underwent EGFR analysis, TTF-1 and PET-CT before treatment.

RESULTS

The EGFR mutation was present in 28.9% (n= 63) of cases. TTF-1 positivity was 66.9% (n= 105). Standardized uptake value (SUV max) was 16.7 ± 6.8 in EGFR mutant type, 13.8 ± 7.6 in cases having no EGFR mutations. According to our evaluations, high SUVmax is positively correlated with EGFR mutation status. TTF-1 expression in multivariate analysis strengthens the accuracy of detecting an EGFR mutation.

CONCLUSION

PET-CT FDG uptake may, together with TTF-1 expression, help diagnosis in lung adenocarcinoma cases when evaluating for EGFR mutation status.

18F-FDG uptake for prediction EGFR mutation status in non-small cell lung cancer

Epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) are a response to EGFR-tyrosine kinase inhibitor. However, a lack of sufficient tumor tissue has been a limitation for determining EGFR mutation status in clinical practice. The objective of this study was to predict EGFR mutation status in NSCLC patients based on a model including maximum standardized uptake value (SUVmax) and clinical features.We retrospectively reviewed NSCLC patients undergoing EGFR mutation testing and pretreatment positron emission tomography/computed tomography between March 2009 and December 2013. The relationships of EGFR mutations with both SUVmax and patient characteristics were evaluated, and a multivariate logistic regression analysis was performed. The model was assessed by area under the receiver-operating characteristic curve (AUC) and was prospectively validated during January to June 2014.Three hundred and sixteen patients meeting the criteria were enrolled for model construction. The SUVmax values were significantly lower for EGFR mutations (mean, 9.5 ± 5.74) than for EGFR wild-type (mean, 12.7 ± 6.43; P < 0.001). ROC curve analysis showed that the SUVmax cutoff point was 8.1, for which the AUC was 0.65 (95% confidence interval [CI], 0.60-0.72). In addition, multivariate analysis also showed that low SUVmax (≤8.1) was a predictor of EGFR mutations, for which the AUC was 0.77, combining nonsmoking history and primary tumor size (≤5 cm). Eighty-five patients were enrolled to validate the predictive model, and the overall accuracy, sensitivity, and specificity were 77.6%, 64.6% (95% CI 40.7-82.8), and 82.5% (95% CI 70.9-91.0), respectively.The specific FDG uptake value could be considered to effectively predict EGFR mutation status of NSCLC patients by considering smoking history and primary tumor size when genetic tests are not available.