ABSTRACT

Conclusion:

The parameters of ¹⁸F-FDG PET/CT, especially the volumetric parameters, were found to be strong prognostic factors with statistical significance for predicting PFS. We believe that these parameters are important prognostic markers that should be evaluated together in the management and follow-up of patients with EGFR mutation-positive adenocarcinoma.

Results:

Metabolic tumor volume, modified Glasgow prognostic score and locally advanced disease were identified as independent prognostic parameters for PFS (p=0.004, p=0.029, p=0.016, respectively). A univariate Cox regression analysis revealed albumin/alkaline phosphatase and tumor size to be significant parameters for prognosis (p=0.033, p=0.043, respectively). A multivariate Cox regression analysis revealed that none of the parameters were predictive or OS.

Methods:

A retrospective examination was made of patients diagnosed with lung adenocarcinoma who underwent ¹⁸F-FDG PET/CT imaging for staging maximum four weeks before starting treatment, between January 2015 and July 2020. Included in the study were 68 patients identified histopathologically to have locally advanced/metastatic EGFR mutation-positive adenocarcinoma, and who underwent EGFR TKI therapy. The laboratory data of the patients, obtained 15 days before imaging performed for PET/CT staging, were evaluated.

Objectives:

This study makes a retrospective examination of exploring the prognostic value of ¹⁸fluorine-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) related metabolic-volumetric variables, nutritional status, and immune and inflammatory markers on progression-free survival (PFS) and overall survival (OS) in advanced adenocarcinoma patients with positive epidermal growth factor receptor (EGFR) mutations undergoing EGFR tyrosine kinase inhibitor (TKI) therapy.

Keywords:

18F-FDG PET/CT, lung cancer, adenocarcinoma, EGFR, progression-free survival, overall survival

Introduction

Non-small cell lung cancer (NSCLC), triggered by the activation of epidermal growth factor receptor (EGFR) mutations, accounts for approximately 10% of all NSCLC cases (1). Tyrosine kinase inhibitor (TKI) therapy is the first-line treatment for metastatic NSCLC with an EGFR mutation (2). EGFR signaling regulates the pathways of glucose metabolism in EGFR-mutated cancer cells, and EGFR TKIs reduce lactate production and glucose consumption (3). TKIs have been associated with longer progression-free survival (PFS) than chemotherapy in advanced NSCLC with EGFR mutations (2,4). The approved agents for TKI therapy include first-generation EGFR TKIs, erlotinib and gefitinib, and second-generation EGFR TKI, afatinib. The objective response rates to these agents in randomized clinical trials range from 56-74%, and the median time to progression is 9-13 months (5,6,7).

Recently, simple and accessible biomarkers related to systemic inflammation and nutritional status have been developed for predicting prognosis in various cancers (8). While the modified Glasgow prognostic score (mGPS), which is based on serum C-reactive protein (CRP) and albumin (ALB) concentrations, is considered a prognostic factor for most cancers (9), the prognostic nutritional index (PNI), which is calculated on the basis of ALB and total lymphocyte count, is more useful for predicting overall survival (OS) (10).

Lactate dehydrogenase (LDH) is another serum enzyme that is mainly involved in the conversion of pyruvate to lactate, and that has been linked to tumor metabolism (11). Several studies have established elevated LDH levels in various types of cancer, including NSCLC (12,13).

Immune and inflammatory responses have a characteristic significance for developing tumors in the body. Homeostasis and inflammation are among the numerous physiological and pathological pathways in which platelets are involved. There have been many studies associating an elevated platelet count with poor prognosis for various solid cancers, including those of the lung (14). The neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR), which are systemic inflammatory markers, play a prognostic role in many malignancies, such as malignant melanoma, esophageal cancer, prostate cancer, diffuse large B-cell lymphoma, breast cancer, nasopharyngeal cancer and NSCLC (15). There have also been many recent publications reporting the systemic immune-inflammation index (SII), which is based on platelet, lymphocyte and neutrophil counts, to be another important prognostic marker for various cancers (16).

¹⁸Fluorine-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT)is an imaging method that is used to diagnose and stage lung cancer and is based on the elevated glucose metabolism of tumor cells with increased expression of glucose transporter protein and hexokinase activity. In addition to diagnosis and staging, ¹⁸F-FDG-PET is being increasingly used to assess treatment response and to predict outcomes (17). Some studies recommend early assessment with ¹⁸F-FDG PET/CT as a criterion in the modification of tumor response during treatment (18,19). The volumetric parameters; metabolic tumor volume (MTV) and total lesion glycolysis (TLG) have been used to reflect the disease burden and tumor aggressiveness in NSCLC (20). The standardized uptake value (SUV) is a semi-quantitative determination of the normalized concentration of radioactivity, and maximum SUV (SUV_max) is the most widely applied parameter in clinical practice (21).

This study is conducted to explore the prognostic value of inflammatory markers and metabolic- and volume-based parameters related to ¹⁸F-FDG PET/CT on treatment response assessment and outcome prediction, while also establishing the prognostic value of these parameters in adenocarcinoma patients with EGFR mutations.

Materials and Methods

Patients diagnosed with NSCLC who underwent ¹⁸F-FDG PET/CT imaging for staging max four weeks before starting treatment between January 2015 and July 2020 were reviewed retrospectively. Subsequently, 68 patients who were found histopathologically to have locally advanced/metastatic EGFR mutation-positive adenocarcinoma and who underwent EGFR TKI therapy were included in the study. Patients were staged based on the TNM classification, according to the 8^th edition staging system recommended by the International Association for the Study of Lung Cancer. The final surveillance program will be conducted in December 2020. Excluded from the study were: 1-) Patients with diagnoses other that concurrent cancer; 2-) Patients who underwent surgery or received any treatment (e.g. chemotherapy, radiotherapy) before imaging; 3-) Patients with missing hospital records; 4-) Patients without ¹⁸F-FDG uptake at the site of the primary tumor; 5-) Patients with unknown status of EGFR gene mutation; and 6-) Patients who were followed up by an external center. In our retrospective study, the informed consent form was not documented, it was prepared in accordance with the local Clinical Practices guide and the current legislation, approval for the use of the patient data for publication was obtained from University of Health Sciences Turkey,Dr. Suat Seren Chest Diseases and Surgery Hospital Institutional Ethics Committee (approval no: 49109414-604.02).

Results

Discussion

This study found MTV, a volumetric parameter of ¹⁸F-FDG PET/CT performed for staging in 68 patients with advanced EGFR-mutated adenocarcinoma, to be an independent prognostic factor for PFS. We further identified the scoring method for mGPS according to CRP and ALB levels as another significant prognostic factor for PFS. ROC analysis results revealed MTV, TLG and NLR to have statistically high sensitivity and specificity in predicting progression. We believe that these parameters are important prognostic markers that should be evaluated together in the treatment management and follow-up of patients with EGFR mutation-positive advanced adenocarcinomas.

EGFR mutations play a decisive role in the systematic treatment of NSCLC. The treatment of EGFR-mutated NSCLC has improved significantly in recent years, with EGFR-TKIs being the primary therapy for patients with advanced EGFR-mutated NSCLC (22,23). Previous studies have clearly demonstrated the dramatic response of patients with advanced adenocarcinoma to treatment with EGFR TKIs (gefitinib, erlotinib and afatinib). The presence of somatic mutations in the EGFR gene is deemed the best predictor of the response to TKIs (5,24). Gefitinib, erlotinib, afatinib and osimertinib have significantly prolonged the PFS of patients with untreated advanced EGFR-mutated NSCLC, although discussions of the optimal sequence are continuing (25). Patients who are to benefit from EGFR TKI therapy should be selected carefully to avoid such critical side effects as interstitial lung disease (26).

The variation in the survival of patients with advanced adenocarcinoma is associated with multiple factors (EGFR mutations, metabolism changes, serum markers and gender). ¹⁸F-FDG PET/CT is a promising method and may reveal specific differences in metabolism in contrast to conventional methods when selecting patients with a better prognosis. ¹⁸F-FDG PET/CT has been increasingly identified as a prognostic biomarker for various malignancies in the assessment of early responses to treatment (27). Studies have shown that assessment with ¹⁸F-FDG PET/CT in NSCLC can predict PFS and OS in patients treated with TKIs in the early period (18,28). In another study, early ¹⁸F-FDG PET/CT was reported to predict the histopathological response in NSCLC patients treated with TKIs as neoadjuvant therapy (29).

Several studies (28,29) to date have evaluated the significance of ¹⁸F-FDG uptake in the prediction of EGFR mutations in NSCLC, some of which have focused on SUV_max, identifying low SUV_max as an independent predictor of EGFR mutations (30,31,32,33); while in another study, it was emphasized that a high SUV_max was a significant predictor of EGFR mutations (31). It has been suggested that these differences may be attributable to clinicopathological features, and so this study evaluated the metabolic and volumetric parameters from PET/CT with immune, inflammatory and nutritional parameters for assessing PFS and OS, and investigated the effects of these parameters on each other, with MTV and mGPS being identified as the most valuable prognostic parameters for PFS. Compared to other studies, we think that evaluating ¹⁸F-FDG PET/CT volumetric-metabolic and immune-inflammatory parameters in patients with NSCLC is more effective in determining the prognosis of the disease.

A recent study emphasized the important role of the systemic inflammation and the immune status of patients in cancer progression. Immune suppression and systemic inflammation at the onset of the disease are associated with a poor prognosis (34), and NLR, PLR, and LDH are the most effective and easily accessible markers for assessing inflammation and immune status (15).

There have been many studies reporting the prognostic value of ¹⁸F-FDG PET/CT based on metabolic parameters, not only in lung cancer treated with TKIs (35,36), but also in other lung cancers in general (37,38). Unlike SUV_max, MTV and TLG include metabolic load and disease extent, and thus can have a higher predictive value (39,40,41). Similar to our study, another study reported that ¹⁸F-FDG PET/CT volumetric parameters reflect both metabolic and tumor burdens, and thus had higher prognostic value than the metabolic activity values obtained by PET/CT (42,43) and tumor size (44) in lung cancers. Volumetric parameters, such as MTV and TLG, have been extensively studied in recent years. The prognostic role of MTV and TLG was meta-analyzed in patients with NSCLC at different stages (44). Volume-based parameters exhibit advantages in the measurement of metabolic tumor burden. Parameters obtained ¹⁸F-FDG PET/CT can be used to select patients at high risk of death and who may benefit from subsequent more aggressive treatments.

Furthermore, our study identified mGPS and NLR as significant prognostic factors for PFS. There have been other studies demonstrating that other available blood-based biomarkers, such as NLR, PLR and mGPS, reflect the inflammatory status associated with cancer, and can be used as prognostic factors in lung cancer (21,45). mGPS, which assesses both systemic inflammation and nutritional status, has been identified as a potential prognostic predictor of lung cancer, as evaluated in many studies (46,47). The utility of NLR as a predictor in cancer patients has not been well studied, although there is increasing evidence that molecular and cellular pathways involve inflammations that contribute to the proliferation, angiogenesis and metastasis of neoplastic cells (48,49). Moreover, circulating neutrophils release various inflammatory cytokines, including tumor necrosis factor-α and interleukin-6, leading to cancer progression (50). It may therefore be reasonable to claim that treatment with EGFR-TKI is more effective in EGFR-mutated NSCLC patients with low NLR than in those with high NLR. Our analysis also suggests that NLR may be associated with PFS in NSCLC patients.

Conclusion

The aim in this study was to determine the optimum prognostic factors for assessing treatment response in advanced EGFR-mutated adenocarcinoma patients treated with TKIs. ¹⁸F-FDG PET/CT volumetric parameters were found to have statistical significance in predicting PFS. We believe that these parameters are important prognostic markers that should be evaluated together in the management and follow-up of patients with EGFR-mutated adenocarcinoma. ¹⁸F-FDG PET/CT may be considered an appropriate guide when making treatment decisions.

Assessments

Age, gender and laboratory data [complete blood count, LDH, CRP, alkaline phosphatase (ALP), ALB obtained 15 days before ¹⁸F-FDG PET/CT imaging was retrieved from the electronic hospital records. The NLR, PLR, CRP-to-ALB ratio, and serum ALB-to-serum ALP ratio were calculated. The formulas used to calculate the SII and PNI were as follows: SSI = platelet count × neutrophil count/lymphocyte count; PNI = 10 × serum ALB level + 0.5 × lymphocyte count. PNI score was recorded as 0 if PNI ≥45, and 1 if PNI was <45; mGPS was recorded as 0 if CRP was ≤10 mg/L, one if CRP was >10 mg/L and ALB ≥35 g/L, and 2 if CRP was >10 mg/L and alb <35 g/L. The recorded parameters related to ¹⁸F-FDG PET/CT imaging included the longest diameter (mm) of the primary mass, MTV (cm³), TLG (g/mL × cm³), SUV_max and SUV_mean.

Positron Emission Tomography/Computed Tomography Protocol

Imaging was performed in a Philips Gemini TF 16-slice combined PET/CT scanner, with the same scanner used for all patients. Following a min 6 hours of fasting, 8-15 mCi ¹⁸F-FDG (2.5 MBq/kg body weight) was administered intravenously and the time between intravenous injection and scans was 60±5 minutes. The patient did an intravenous contrast agent. The first CT images (140 kV, 100 mAs, 5 mm sections) and then PET images were acquired. Attenuation-corrected emission data were obtained using non-contrast-enhanced data, extrapolated to 511 keV. PET images were acquired through emission scanning for 1.5 min per bed position, and a wholebody scan from skull vertex to the proximal thigh using 9 or 10 bed positions. The images were reconstructed with iterative algorithms over a 128x128 matrix.

Image Analysis

Hybrid images of the ¹⁸F-FDG PET/CT data were analyzed independently by two nuclear medicine specialists. The pattern and degree of primary mass uptake were evaluated and located. A 3D isocontour region of interest was drawn automatically on the lesion with the primary mass uptake in all three planes. While calculating the SUV_max, SUV_mean and the MTV included in the volume of interest, the area related to the 40% threshold was calculated automatically. TLG was calculated by multiplying MTV by the SUV_mean.

EGFR Mutation Assessment

Tissue samples acquired from paraffin-embedded specimens were collected in 1.5 mL vials, and DNA was extracted using a DNA Sample Preparation Kit (Cobas, Roche Molecular Systems, USA) and reverse transcription-polymerase chain reaction was performed. All procedures were conducted according to the manufacturer’s instructions (Cobas EGFR Mutation Test v2, Roche Molecular Systems, USA).

Statistical Analysis

Data were analyzed using the IBM SPSS Statistics (Version 26.0. Armonk, NY: IBM Corp.) and MedCalc Statistical Software version 16.4.3 (MedCalc Software BV, Ostend, Belgium; https://www.medcalc.org; 2016) software packages. Descriptive statistics were expressed as the unit number (n), percentage (%), mean (x-), standard deviation, standard error, median (M), minimum (min) and max values. The performance of prognostic markers in predicting recurrence and survival was evaluated by a receiver operating characteristic curve (ROC) analysis. The survival times of the patients were compared using the log-rank (Mantel-Cox) test of the Kaplan-Meier analysis, based on the optimum cut-off point for the markers found significant in the ROC analysis. Univariate and multivariate Cox regression analyses were used to determine the factors affecting PFS and OS. p values of <0.05 were considered statistically significant.

Patient Characteristics

Among the 68 patients with advanced EGFR-mutated adenocarcinoma were 40 (58.8%) female and 28 (41.2%) male patients, with a median age of 64.5 (31.0-85.0) years. 43 (63.2%) patients of 68 were non-smoker. Of the patients with advanced adenocarcinoma, 15 (22.1%) were classified as locally advanced and 53 (77.9%) as metastatic. Of the total, five (7.3%) patients had mutations in exon 18, 47 (69.1%) in exon 19, and 16 (23.5%) in exon 21. For EGFR TKI, 27 (39.7%) patients underwent afatinib therapy, 35 (51.5%) erlotinib therapy and six (8.8%) gefitinib therapy. During the follow-up, 66.2% of the patients experienced local or metastatic relapse and 13 (19.1%) died from disease progression. Patient characteristics are presented in Table 1.

¹⁸F-FDG PET/CT Parameters

Of 68 patients with advanced EGFR mutation adenocarcinoma, the median SUV_max value was 9.81 (3.50-38.10), the median MTV value was 25.66 (1.66-461.12), and the median TLG value was 158.19 (5.88-1826.04). The metabolic and volumetric parameters of the patients, as well as their immune and inflammatory parameters, are presented in Table 1.

Progression-free Survival Analysis

The median PFS was 13.9 (1.9-99.8) months overall. When the continuous variables were evaluated on the ROC curve drawn to determine progression, the analysis results revealed that the parameters with a significant area under the curve (AUC) values were MTV 0.725 [95% confidence interval (CI): 0.630-0.826, p=0.001], TLG 0.728 (95% CI: 0.606-0.828, p<0.001) and NLR 0.653 (95% CI: 0.528-0.765, p=0.019), which were predictive of progression (Table 2). MTV >7.04, TLG >78.68, NLR >4.73, an mGPS score of two and metastatic disease had statistically significantly high sensitivity and specificity in predicting of progression (Table 2). Optimum values were determined for MTV, TLG and NLR for use in the determination of progression, and patients were divided into groups based on these values. A Kaplan-Meier analysis revealed MTV, TLG, NLR, gender and locally advanced disease to be significant parameters, and further showing that PFS was significantly shorter in patients with MTV >7.04, TLG >78.68 and NLR >4.73 than in those with low values of these parameters (p=0.001, p=0.003, p=0.001, respectively). Metastatic patients had a shorter PFS than locally advanced patients (p=0.003); and those with an mGPS score of two were found to have a shorter PFS than those with a score of 0 (p=0.009) (Table 3). The univariate Cox regression analysis for systemic inflammation, and nutritional and volumetric parameters identified PLR, SII and tumor size was predictive of PFS (p=0.001, p=0.001, p=0.007, respectively) (Table 3). The Multivariate Cox regression analysis, in turn, identified MTV, mGPS and stage as independent prognostic factors for PFS (p=0.004, p=0.029, p=0.016, respectively) (Table 4). Among the volumetric parameters, MTV was determined to be a representative volumetric parameter; and among the general patient characteristics, age and gender had no statistically significant effect on PFS.

Overall Survival Analysis

The median OS was 21.9 (2.9-99.8) months. Among the general patient characteristics, age and gender had no statistically significant effect on OS.

When the continuous variables were evaluated on the ROC curve drawn according to survival, the parameters with a significant AUC values were MTV 0.715 (95% CI: 0.594-0.817, p=0.007), TLG 0.701 (%95 CI: 0.578-0.805, p=0.017), LDH 0.678 (%95 CI: 0.554-0.787, p=0.031), CRP/ALB 0.729 (95% CI: 0.607-0.829, p=0.007) and PNI 0.776 (95% CI: 0.658-0.868, p=0.001), which were predictive of survival MTV >41.02, TLG >384.8, LDH >222, CRP/ALB >3.956, and PNI >41.3 had statistically significantly high sensitivity and specificity in predicting survival (Table 2).

The Kaplan-Meier analysis for survival showed OS to be significantly shorter in patients with MTV >41.02, TLG >384.8, LDH >222 and CRP/ALB >3.956 than in those with low values of these parameters (p=0.001, p=0.002, p=0.040, p<0.001, p<0.001, p=0.001, respectively) (Table 4). A multivariate Cox regression analysis for systemic inflammation, and nutritional and volumetric parameters identified ALB/ALP and tumor size as significant parameters (p=0.033, p=0.043, respectively) (Table 4). The multivariate Cox regression analysis demonstrated that none of the parameters were predictive or OS (Table 4).

Study Limitations

Our study had certain limitations. The study protocol could not be strictly controlled because to its retrospective nature, although a standard imaging protocol was followed for all patients, and there was no difference due to homogeneous clinical management.

References

Shigematsu H, Lin L, Takahashi T, Nomura M, Suzuki M, Wistuba II, Fong KM, Lee H, Toyooka S, Shimizu N, Fujisawa T, Feng Z, Roth JA, Herz J, Minna JD, Gazdar AF. Clinical and biological features associated with epidermal growth factor receptor gene mutations in lung cancers. J Natl Cancer Inst 2005;97:339-346.

Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, Sunpaweravong P, Han B, Margono B, Ichinose Y, Nishiwaki Y, Ohe Y, Yang JJ, Chewaskulyong B, Jiang H, Duffield EL, Watkins CL, Armour AA, Fukuoka M. Gefitinib or carboplatin-paclitaxel in pulmonary adenocarcinoma. N Engl J Med 2009;361:947-957.

Makinoshima H, Takita M, Matsumoto S, Yagishita A, Owada S, Esumi H, Tsuchihara K. Epidermal growth factor receptor (EGFR) signaling regulates global metabolic pathways in EGFR-mutated lung adenocarcinoma. J Biol Chem 2014;289:20813-20823.

Pao W, Miller V, Zakowski M, Doherty J, Politi K, Sarkaria I, Singh B, Heelan R, Rusch V, Fulton L, Mardis E, Kupfer D, Wilson R, Kris M, Varmus H. EGF receptor gene mutations are common in lung cancers from “never smokers” and are associated with sensitivity of tumors to gefitinib and erlotinib. Proc Natl Acad Sci USA 2004;101:13306-13311.

Mitsudomi T, Morita S, Yatabe Y, Negoro S, Okamoto I, Tsurutani J, Seto T, Satouchi M, Tada H, Hirashima T, Asami K, Katakami N, Takada M, Yoshioka H, Shibata K, Kudoh S, Shimizu E, Saito H, Toyooka S, Nakagawa K, Fukuoka M; West Japan Oncology Group. Gefitinib versus cisplatin plus docetaxel in patients with non-small-cell lung cancer harbouring mutations of the epidermal growth factor receptor (WJTOG3405): an open label, randomised phase 3 trial. Lancet Oncol 2010;11:121-128.

Zhou C, Wu YL, Chen G, Feng J, Liu XQ, Wang C, Zhang S, Wang J, Zhou S, Ren S, Lu S, Zhang L, Hu C, Hu C, Luo Y, Chen L, Ye M, Huang J, Zhi X, Zhang Y, Xiu Q, Ma J, Zhang L, You C. Erlotinib versus chemotherapy as first-line treatment for patients with advanced EGFR mutation-positive non-small-cell lung cancer (OPTIMAL, CTONG-0802): a multicentre, open-label, randomised, phase 3 study. Lancet Oncol 2011;12:735-742.

Rosell R, Carcereny E, Gervais R, Vergnenegre A, Massuti B, Felip E, Palmero R, Garcia-Gomez R, Pallares C, Sanchez JM, Porta R, Cobo M, Garrido P, Longo F, Moran T, Insa A, De Marinis F, Corre R, Bover I, Illiano A, Dansin E, de Castro J, Milella M, Reguart N, Altavilla G, Jimenez U, Provencio M, Moreno MA, Terrasa J, Muñoz-Langa J, Valdivia J, Isla D, Domine M, Molinier O, Mazieres J, Baize N, Garcia-Campelo R, Robinet G, Rodriguez-Abreu D, Lopez-Vivanco G, Gebbia V, Ferrera-Delgado L, Bombaron P, Bernabe R, Bearz A, Artal A, Cortesi E, Rolfo C, Sanchez-Ronco M, Drozdowskyj A, Queralt C, de Aguirre I, Ramirez JL, Sanchez JJ, Molina MA, Taron M, Paz-Ares L; Spanish Lung Cancer Group in collaboration with Groupe Français de Pneumo-Cancérologie and Associazione Italiana Oncologia Toracica. Erlotinib versus standard chemotherapy as first-line treatment for European patients with advanced EGFR mutation-positive non-small-cell lung cancer (EURTAC): a multicentre, open-label, randomised phase 3 trial. Lancet Oncol 2012;13:239-246.

Minami S, Ogata Y, Ihara S, Yamamoto S, Komuta K. Pretreatment Glasgow prognostic score and prognostic nutritional index predict overall survival of patients with advanced small cell lung cancer. Lung Cancer (Auckl) 2017;8:249-257.

Laird BJ, Kaasa S, McMillan DC, Fallon MT, Hjermstad MJ, Fayers P, Klepstad P. Prognostic factors in patients with advanced cancer: a comparison of clinicopathological factors and the development of an inflammation-based prognostic system. Clin Cancer Res 2013;19:5456-5464.

Zhou T, Zhao Y, Zhao S, Yang Y, Huang Y, Hou X, Zhao H, Zhang L. Comparison of the prognostic value of systemic inflammation response markers in small cell lung cancer patients. J Cancer 2019;10:1685-1692.

Ferreira LM, Hebrant A, Dumont JE. Metabolic reprogramming of the tumor. Oncogene 2012;31:3999-4011.

Zhang X, Guo M, Fan J, Lv Z, Huang Q, Han J, Wu F, Hu G, Xu J, Jin Y. Prognostic significance of serum LDH in small cell lung cancer: a systematic review with meta-analysis. Cancer Biomark 2016;16:415-423.

Lee DS, Park KR, Kim SJ, Chung MJ, Lee YH, Chang JH, Kang JH, Hong SH, Kim MS, Kim YS. Serum lactate dehydrogenase levels at presentation in stage IV non-small cell lung cancer: predictive value of metastases and relation to survival outcomes. Tumour Biol 2016;37:619-625.

Buergy D, Wenz F, Groden C, Brockmann MA. Tumor-platelet interaction in solid tumors. Int J Cancer 2012;130:2747-2760.

Deng M, Ma X, Liang X, Zhu C, Wang M. Are pretreatment neutrophil-lymphocyte ratio and platelet-lymphocyte ratio useful in predicting the outcomes of patients with small-cell lung cancer? Oncotarget 2017;8:37200-37207.

Hong X, Cui B, Wang M, Yang Z, Wang L, Xu Q. Systemic immune-inflammation index, based on platelet counts and neutrophil-lymphocyte ratio, is useful for predicting prognosis in small cell lung cancer. Tohoku J Exp Med 2015;236:297-304.

Dose Schwarz J, Bader M, Jenicke L, Hemminger G, Jänicke F, Avril N. Early prediction of response to chemotherapy in metastatic breast cancer using sequential 18F-FDG PET. J Nucl Med 2005;46:1144-1150.

Mileshkin L, Hicks RJ, Hughes BG, Mitchell PL, Charu V, Gitlitz BJ, Macfarlane D, Solomon B, Amler LC, Yu W, Pirzkall A, Fine BM. Changes in 18F-fluorodeoxyglucose and 18F-fluorodeoxythymidine positron emission tomography imaging in patients with non-small cell lung cancer treated with erlotinib. Clin Cancer Res 2011;17:3304-3315.

Edet-Sanson A, Dubray B, Doyeux K, Back A, Hapdey S, Modzelewski R, Bohn P, Gardin I, Vera P. Serial assessment of FDG-PET FDG uptake and functional volume during radiotherapy (RT) in patients with non-small cell lung cancer (NSCLC). Radiother Oncol 2012;102:251-257.

Davison J, Mercier G, Russo G, Subramaniam RM. PET-based primary tumor volumetric parameters and survival of patients with non-small cell lung carcinoma. AJR Am J Roentgenol 2013;200:635-640.

Paidpally V, Chirindel A, Lam S, Agrawal N, Quon H, Subramaniam RM. FDG-PET/CT imaging biomarkers in head and neck squamous cell carcinoma. Imaging Med 2012;4:633-647.

Miyawaki M, Naoki K, Yoda S, Nakayama S, Satomi R, Sato T, Ikemura S, Ohgino K, Ishioka K, Arai D, Namkoong H, Otsuka K, Miyazaki M, Tani T, Kuroda A, Nishino M, Yasuda H, Kawada I, Koh H, Nakamura M, Terashima T, Sakamaki F, Sayama K, Betsuyaku T, Soejima K. Erlotinib as second- or third-line treatment in elderly patients with advanced non-small cell lung cancer: Keio Lung Oncology Group Study 001 (KLOG001). Mol Clin Oncol 2017;6:409-414.

Ramalingam SS, Vansteenkiste J, Planchard D, Cho BC, Gray JE, Ohe Y, Zhou C, Reungwetwattana T, Cheng Y, Chewaskulyong B, Shah R, Cobo M, Lee KH, Cheema P, Tiseo M, John T, Lin MC, Imamura F, Kurata T, Todd A, Hodge R, Saggese M, Rukazenkov Y, Soria JC; FLAURA Investigators. Overall survival with osimertinib in untreated, EGFR-mutated advanced NSCLC. N Engl J Med 2020;382:41-50.

Maemondo M, Inoue A, Kobayashi K, Sugawara S, Oizumi S, Isobe H, Gemma A, Harada M, Yoshizawa H, Kinoshita I, Fujita Y, Okinaga S, Hirano H, Yoshimori K, Harada T, Ogura T, Ando M, Miyazawa H, Tanaka T, Saijo Y, Hagiwara K, Morita S, Nukiwa T; North-East Japan Study Group. Gefitinib or chemotherapy for non-small-cell lung cancer with mutated EGFR. N Engl J Med 2010;362:2380-2388.

Takeda M, Nakagawa K. First- and second-generation EGFR-TKIs are all replaced to osimertinib in chemo-Naive EGFR mutation-positive non-small cell lung cancer? Int J Mol Sci 2019;20:146.

Cersosimo RJ. Gefitinib: an adverse effects profile. Expert Opin Drug Saf 2006;5:469-479.

Vansteenkiste J, Fischer BM, Dooms C, Mortensen J. Positron-emission tomography in prognostic and therapeutic assessment of lung cancer: systematic review. Lancet Oncol 2004;5:531-540.

Zander T, Scheffler M, Nogova L, Kobe C, Engel-Riedel W, Hellmich M, Papachristou I, Toepelt K, Draube A, Heukamp L, Buettner R, Ko YD, Ullrich RT, Smit E, Boellaard R, Lammertsma AA, Hallek M, Jacobs AH, Schlesinger A, Schulte K, Querings S, Stoelben E, Neumaier B, Thomas RK, Dietlein M, Wolf J. Early prediction of nonprogression in advanced non-small-cell lung cancer treated with erlotinib by using [(18)F]fluorodeoxyglucose and [(18)F]fluorothymidine positron emission tomography. J Clin Oncol 2011;29:1701-1708.

Aukema TS, Kappers I, Olmos RA, Codrington HE, van Tinteren H, van Pel R, Klomp HM; NEL Study Group. Is 18F-FDG PET/CT useful for the early prediction of histopathologic response to neoadjuvant erlotinib in patients with non-small cell lung cancer? J Nucl Med 2010;51:1344-1348.

Lv Z, Fan J, Xu J, Wu F, Huang Q, Guo M, Liao T, Liu S, Lan X, Liao S, Geng W, Jin Y. 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. Eur J Nucl Med Mol Imaging 2018;45:735-750.

Lee EY, Khong PL, Lee VH, Qian W, Yu X, Wong MP. Metabolic phenotype of stage IV lung adenocarcinoma: relationship with epidermal growth factor receptor mutation. Clin Nucl Med 2015;40:e190-e195.

Gu J, Xu S, Huang L, Li S, Wu J, Xu J, Feng J, Liu B, Zhou Y. Value of combining serum carcinoembryonic antigen and PET/CT in predicting EGFR mutation in non-small cell lung cancer. J Thorac Dis 2018;10:723-731.

Guan J, Xiao NJ, Chen M, Zhou WL, Zhang YW, Wang S, Dai YM, Li L, Zhang Y, Li QY, Li XZ, Yang M, Wu HB, Chen LH, Liu LY. 18F-FDG uptake for prediction EGFR mutation status in non-small cell lung cancer. Medicine (Baltimore) 2016;95:e4421.

Pinkerton JW, Kim RY, Robertson AAB, Hirota JA, Wood LG, Knight DA, Cooper MA, O’Neill LAJ, Horvat JC, Hansbro PM. Inflammasomes in the lung. Mol Immunol 2017;86:44-55.

van Gool MH, Aukema TS, Schaake EE, Rijna H, Codrington HE, Valdés Olmos RA, Teertstra HJ, van Pel R, Burgers SA, van Tinteren H, Klomp HM. (18)F-fluorodeoxyglucose positron emission tomography versus computed tomography in predicting histopathological response to epidermal growth factor receptor-tyrosine kinase inhibitor treatment in resectable non-small cell lung cancer. Ann Surg Oncol 2014;21:2831-2837.

Benz MR, Herrmann K, Walter F, Garon EB, Reckamp KL, Figlin R, Phelps ME, Weber WA, Czernin J, Allen-Auerbach MS. (18)F-FDG PET/CT for monitoring treatment responses to the epidermal growth factor receptor inhibitor erlotinib. J Nucl Med 2011;52:1684-1689.

Higashi K, Ueda Y, Arisaka Y, Sakuma T, Nambu Y, Oguchi M, Seki H, Taki S, Tonami H, Yamamoto I. 18F-FDG uptake as a biologic prognostic factor for recurrence in patients with surgically resected non-small cell lung cancer. J Nucl Med 2002;43:39-45.

Imamura Y, Azuma K, Kurata S, Hattori S, Sasada T, Kinoshita T, Okamoto M, Kawayama T, Kaida H, Ishibashi M, Aizawa H. Prognostic value of SUVmax measurements obtained by FDG-PET in patients with non-small cell lung cancer receiving chemotherapy. Lung Cancer 2011;71:49-54.

Chen HH, Chiu NT, Su WC, Guo HR, Lee BF. Prognostic value of whole-body total lesion glycolysis at pretreatment FDG PET/CT in non-small cell lung cancer. Radiology 2012;264:559-566.

Olivier A, Petyt G, Cortot A, Scherpereel A, Hossein-Foucher C. Higher predictive value of tumour and node [18F]-FDG PET metabolic volume and TLG in advanced lung cancer under chemotherapy. Nucl Med Commun 2014;35:908-915.

Liao S, Penney BC, Wroblewski K, Zhang H, Simon CA, Kampalath R, Shih MC, Shimada N, Chen S, Salgia R, Appelbaum DE, Suzuki K, Chen CT, Pu Y. Prognostic value of metabolic tumor burden on 18F-FDG PET in nonsurgical patients with non-small cell lung cancer. Eur J Nucl Med Mol Imaging 2012;39:27-38.

Oh JR, Seo JH, Chong A, Min JJ, Song HC, Kim YC, Bom HS. Whole-body metabolic tumour volume of 18F-FDG PET/CT improves the prediction of prognosis in small cell lung cancer. Eur J Nucl Med Mol Imaging 2012;39:925-935.

Yoo SW, Kim J, Chong A, Kwon SY, Min JJ, Song HC, Bom HS. Metabolic tumor volume measured by F-18 FDG PET/CT can further stratify the prognosis of patients with Stage IV non-small cell lung cancer. Nucl Med Mol Imaging 2012;46:286-293.

Im HJ, Pak K, Cheon GJ, Kang KW, Kim SJ, Kim IJ, Chung JK, Kim EE, Lee DS. Prognostic value of volumetric parameters of (18)F-FDG PET in non-small-cell lung cancer: a meta-analysis. Eur J Nucl Med Mol Imaging 2015;42:241-251.

Klemm F, Joyce JA. Microenvironmental regulation of therapeutic response in cancer. Trends Cell Biol 2015;25:198-213.

Wang J, Wang B, Chu H, Yao Y. Intrinsic resistance to EGFR tyrosine kinase inhibitors in advanced non-small-cell lung cancer with activating EGFR mutations. Onco Targets Ther 2016;9:3711-3726.

Igawa S, Sasaki J, Otani S, Ishihara M, Takakura A, Katagiri M, Masuda N. Impact of smoking history on the efficacy of gefitinib in patients with non-small cell lung cancer harboring activating epidermal growth factor receptor mutations. Oncology 2015;89:275-280.

Colotta F, Allavena P, Sica A, Garlanda C, Mantovani A. Cancer-related inflammation, the seventh hallmark of cancer: links to genetic instability. Carcinogenesis 2009;30:1073-1081.

Mantovani A, Allavena P, Sica A, Balkwill F. Cancer-related inflammation. Nature 2008;454:436-444.

Balkwill F, Mantovani A. Inflammation and cancer: back to Virchow? Lancet 2001;357:539-545.

The Prognostic Value of 18F-FDG PET/CT Metabolic Parameters in Predicting Treatment Response Before EGFR TKI Treatment in Patients with Advanced Lung Adenocarcinoma