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Prognostic factors of EGFR-mutated metastatic adenocarcinoma of lung
European Journal of Radiology 123 (2020) 108780
Contents lists available at ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Prognostic factors of EGFR-mutated metastatic adenocarcinoma of lung
T
Ng Kwok Sing*, Chu King Sun, Kung Boom Ting, Au Yong Ting Kun Nuclear Medicine Unit and Clinical PET Centre, Queen Elizabeth Hospital, Hong Kong, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Lung cancer FDG PET/CT Prognosis EGFR
Purpose: The first-line treatment of metastatic lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutation is tyrosine kinase inhibitor (TKI). This study aimed to evaluate potential factors affecting the progression-free survival under TKI treatment. Methods: Forty one patients with EGFR-mutated metastatic lung adenocarcinoma under first-line TKI treatment were retrospectively evaluated. Ten factors potentially influencing the progression-free survival were studied: patients’ age, gender, smoking history, number of comorbidities, performance status, tumor mutation site, maximum of standardized uptake value (SUVmax) of primary tumor in FDG PET/CT, serum CEA level, number of metastatic organs and presence of pleural/pericardial effusion. Mantel-Cox tests and waterfall plots were performed for statistical analyses. Results: Statistical evaluation demonstrated that primary SUVmax, serum CEA level, gender and smoking history were important prognostic factors, with corresponding p values of 0.001, 0.023, 0.034 and 0.041 respectively in Mantel-Cox analyses. Conclusion: Low primary SUVmax, low serum CEA level, female and never smoker were four prognostic factors suggestive of good response to TKI in mutated EGFR metastatic lung adenocarcinoma. SUVmax is probably the most important among the four factors.
1. Introduction Among all malignancies in the world, lung cancer has the highest incidence and mortality [1]. It is also one of the most aggressive cancers with low 5-year survival rate: 50% for stage 1A and 2% for stage IV [2,3]. Prognostic systems have been previously developed to predict stage [4], post-operative mortality [5], disease recurrence [6,7] and survival [4–6,8,9]. Prior prognostic systems have focused mainly on early and operable stages. Unfortunately, 80% of the newly diagnosed lung cancer patients belong to advanced stage [2]. The prognosis of advanced lung carcinoma depends on the treatment modalities, which in turn depends on the presence of genetic mutation in the tumor. If the mutation is druggable, molecular targeted therapies can be offered specifically. The most common mutation is epidermal growth receptor (EGFR), with prevalence of 23% in lung cancer [10]. Tyrosine kinase inhibitors (TKI) have been demonstrated to prolong the survival of EGFR-mutated patients [10–12]. In particular, female and never smoker were noticed to have higher treatment response rates because they had higher frequencies of EGFR mutation (odd ratios of 2.90 and 3.63 respectively) [13]. On the other hand, potential parameters affecting the efficacy of TKI have not been well understood. The current study aimed
to evaluate the prognostic factors affecting the progression-free survival of EGFR-mutated lung adenocarcinoma under TKI treatment. 18 Ffluoro-2-deoxy-D-glucose (FDG) positron emission tomography and computed tomography (PET-CT) is a valuable tool for staging as well as surveillance of lung cancer [14]. Prior reports have showed that the uptake of localized lung adenocarcinoma was higher in EGFR mutated than in wild type tumors [15,16]. Another study has suggested that early PET/CT assessment at a month after TKI treatment could predict tumor response, with sensitivity of 91.9% and specificity of 73.7% [17]. The prognostic nature of FDG PET/CT was therefore evaluated in this investigation. 2. Methods A.) Patient recruitment Patients who underwent FDG PET/CT in our centre from June 2013 to August 2015 for the staging of newly diagnosed lung cancer were retrospectively recruited. Subjects were included only if they fulfilled all criteria of having:
⁎ Corresponding author at: Nuclear Medicine Unit and Clinical PET Centre, LG Block, Queen Elizabeth Hospital, Gascoigne Rd 30, Yau Ma Tei, Hong Kong, PR China. E-mail address: [email protected] (K.S. Ng).
https://doi.org/10.1016/j.ejrad.2019.108780 Received 15 July 2019; Received in revised form 7 November 2019; Accepted 22 November 2019 0720-048X/ © 2019 Elsevier B.V. All rights reserved.
European Journal of Radiology 123 (2020) 108780
K.S. Ng, et al.
• histological diagnosis of EGFR mutated lung adenocarcinoma, • evidence of distant metastases in PET-CT and/or other imaging modalities (e.g. CT, MRI, bone scan), • staging PET-CT in our institution before oncological treatment, • 1 generation tyrosine kinase inhibitor (e.g. 150 mg Erlotinib daily or 250 mg Gefitinib daily) within 8 weeks after PET-CT, and • TKI treatment until disease progression.
Table 1 Characteristics of study population.
st
Subjects were excluded if they fulfilled any of the criteria:
• had blood glucose level greater than 11 mmol/L just before PET/CT acquisition (average 5.5 mmol/L, range: 3.2–10.3 mmol/L), • had oncological treatment before staging PET/CT, • were incompliant to TKI, • withdrew TKI treatment due to intolerance or side-effects (e.g. TKIinduced pneumonitis), • defaulted regular oncology follow-up, or • succumbed because of acute conditions unrelated to cancer (e.g. trauma). • PET-CT All FDG PET-CT examinations were performed with Discovery LS (General Electric Healthcare, the USA) and the administrated FDG activity was 10.7 ± 0.35 mCi . After mean uptake time of 58.7 ± 4.28 min, PET was acquired from skull vertex to mid thighs in 7–8 bed positions (2 min. per bed position) with mean axial bed coverage of 15.7 cm per bed and 9 slices bed overlap in two-dimensional acquisition mode. Reconstruction using Optimization of Ordered Subset Expectation Maximization (OSEM) was performed with 4.25 mm slice thickness in 128 × 128 matrix and processed through a standard filter. Non-contrast CT was acquired for anatomical correlation and attenuation correction with the following parameters: 120 mA tube current, 140 kV tube voltage, 0.5 s gantry rotation speed, 0.984pitch, 4.25 mm slice thickness and 512 × 512 matrix. Standardized uptake value is defined as the activity measured in volume of interest (VOI) divided by the injected FDG dose per body weight [18]:
SUV =
ActivityVOI (μmCi /mL) Doseinjected (μmCi )/Body Weight (kg )
(6)
A fixed sized 1.2cm-diameter spherical VOI was generated for SUV measurement, as recommended by the European Association of Nuclear Medicine (EANM) procedural guidelines [19]. Throughout this study, the maximum SUV (SUVmax) of primary tumor was exclusively investigated. For multiple intrapulmonary lesions, the one with the highest SUVmax was used. Body weight was routinely recorded in the same day of PET/CT acquisition, with mean weight of 56.5 ± 10.4 kg.
Variable
Number
%
Sex Male Female
20 21
48.8 51.2
Age ≤70 > 70 Average
25 16 68.1 (40–91)
61.0 39.0
Smoking history Never smoker Ever smoker
32 9
78.0 22.0
Performance status 0 1 2
6 30 5
14.6 73.2 12.2
No. of comorbidities 0 to 1 > 1
18 23
43.9 56.1
EGFR exon 18 - G719X mutation 19 deletion 21 - L858R mutation
2 17 22
4.9 41.5 53.6
Primary SUVmax ≤12 > 12 Average
25 16 12.1 (4.8–24.5)
61.0 39.0
CEA ≤40 ng/mL > 40 ng/mL
22 19
53.7 46.3
No of metastatic organ 1–2 >2
22 19
53.7 46.3
Presence of effusion Yes No
16 25
39.0 61.0
2 3 4 5 6 7 8
A.) Disease Monitoring Disease status was regularly monitored by oncologists based on physical examination, biochemical markers (e.g. serum carcinoembryonic antigen CEA level) and radiological imaging (e.g. chest X ray, CT, MRI, bone scan, PET-CT). Once disease progression was diagnosed, TKI would be terminated. Progression-free survival (PFS) was defined as the duration from the beginning to the end of TKI usage. All patients were evaluated for 730 days after the start of TKI. If a patient had no evidence of disease progression at the end of the evaluation period, the PFS was assigned to be 730 days.
9 10
sex (male vs. female) smoking history (ever smoker vs. never smoker) number of co-morbidities (e.g. hypertension, diabetes; ≤1 vs. > 1) baseline Eastern Cooperative Oncology Group (ECOG) performance status (PS) when TKI was started (0 vs. 2) EGFR mutation site (exon 19 deletion vs. exon 18 L828R mutation) primary SUVmax in staging PET-CT (≤12 vs. > 12) baseline serum CEA level before TKI was started (≤40 vs. > 40 ng/ mL) number of metastatic organs in staging PET-CT (e.g. intrapulomonary, liver, adrenal; ≤2 vs. > 2) presence of pericardial and/or pleural effusion in staging PET-CT (present vs. absent)
The effects of the ten parameters on the PFS were investigated with the Kaplan Meier plots. Statistical analyses were performed using Mantel-Cox method [20]. The results were defined as statistically significant if the corresponding p values < 0.05. All statistical evaluations were performed with SPSS Statistics 20 (IBM Corporation, the USA).
A.) Statistical analysis
A.) Approval by IRB
Ten potential prognostic factors were evaluated, with the corresponding cutoffs taken at approximately the mean or the median:
This study was approved by the ethic review board at our institution, and informed consent was waived.
1 age (≤70 vs. > 70 year old)
2
European Journal of Radiology 123 (2020) 108780
K.S. Ng, et al.
Fig. 1. Kaplan-Meier plots of progression-free survival against time according to (a) age, (b) sex, (c) smoking history, (d) number of co-morbidties, (e) ECOG performance status, (f) EGFR mutation site, (g) primary SUVmax, (h) serum CA level, (i) number of metastatic organs and (j) presence of pleural and/or pericardial effusion.
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European Journal of Radiology 123 (2020) 108780
K.S. Ng, et al.
red/black bars distribution pattern was not bimodal.
Table 2 Univariate analysis of study population. Parameter
Comparison
PFS: mean
PFS: standard deviation
p value
Sex
Male Female ≤ 70 > 70 Never smoker Ever smoker 0 2 0 to 1 > 1 19 deletion L858R mutation < 12 > 12 ≤ 40 ng/mL > 40 ng/mL 1–2 > 2 Yes No
367 488 448 340 459 321 592 449 406 447 437 430 517 291 507 342 471 380 368 469
48.3 47.0 44.9 55.1 39.1 70.5 82.0 109.0 52.7 46.5 55.1 48.5 41.3 43.8 48.1 46.1 44.3 53.2 53.5 44.3
0.034
Age Smoking history Performance status No. of comorbidities EGFR exon Primary SUVmax CEA No of metastatic organs Presence of effusion
4. Discussion The treatment efficacy in advanced cancer depends on the response rate and PFS. The response rate of TKI in advanced lung carcinoma was demonstrated to be much higher if EGFR was mutated [10,12,13,21]. In particular, the responding rates have been reported to be greater in female and never smoker [13]. For PFS, this study evaluated 10 parameters covering demographic, physical, social, medical, biochemical, immunohistochemistrical and radiological aspects. The current results showed that primary SUVmax ≤ 12, serum CEA ≤ 40 ng/mL, female and never smoker were good prognostic factors. Their corresponding p values in Mantel-Cox analyses were 0.001, 0.023, 0.034 and 0.041 respectively. SUVmax appears to be the most important prognostic factors among the four with the least p value. This is supported by Fig. 2 which shows the distribution of red and black bars were more bimodal in SUVmax group than in serum CEA, sex and smoking groups. SUVmax reflects glucose metabolic rate, proliferation and vascularisation, all of which are higher in tumor than in normal tissue [22]. Tumor with greater metabolism and proliferation (i.e. greater SUVmax) probably can undergo more cell divisions. Such tumor can have greater potential of developing secondary mutation, acquiring resistance to TKI and shortening PFS [23]. This may explain why SUVmax appeared more relevant to PFS than smoking history, sex and serum CEA level. The current finding could have important clinical implication: patients with greater SUVmax have higher risk of early disease progression. Thus they should have closer disease monitoring proactively and switch to second-line therapy early if indicated. This has clinical importance because Osimertinib has been demonstrated to be an effective treatment for T790M-positive lung cancer resistant to first generation TKI [24]. Recent study also suggested that T790M-positive lung tumor had lower SUVmax [25]. Interestingly, the current study showed that PFS depended on sex and smoking history. This observation was probably not coincidental: as female and never smoker had higher possibilities of EGFR mutation, they would respond to the anti-EGFR treatment better and have longer PFS [13]. Longer PFS was reported in patients with exon 19 deletion than with L858R mutation [26]. This finding was only observed in Erlotinib but not in Gefitinib [27]. However, other studies reported similar efficacy between Erlotinib and Gefitinib [28]. In the current study, patients under Erlotinib treatment showed no statistically significant difference in PFS between exon 19 deletion and L858R mutation (p = 0.754). One pitfall of the current report was the short evaluation period of 730 days from the start of TKI. For the patients with no evidence of disease progression by 730 days, their PFS were taken as 730 days. This applied to 24.4% of the patients and their PFS were undervalued. The underestimation was particularly important in never smoker, SUVmax ≤12, female and CEA ≤ 40 ng/dL, because their non-progression fractions were much higher than their counterparts (31.3% vs. 0%; 36% vs. 6.3%, 38.1% vs. 10% and 38.1% vs. 10% respectively). Another pitfall was the selection bias of performance status, which was 14.6% for PS 0, 73.2% for PS 1 and 12.2% for PS 2. Although Fig. 1(e) suggests that the PFS of PS 0 appears longer than that of PS 2, no statistical significance was observed (p = 0.337) because of the limited number of patients in the two groups.
0.426 0.041 0.337 0.512 0.849 0.001 0.023 0.324 0.133
3. Results Table 1 shows the characteristics of the study population. A total of 41 adult patients of age 40–91 year old were recruited. Male to female ratio was around 1: 1, majority were never smoker and had ECOG performance status of 1. For the pathology nature, 53.6% had exon 21 L858R mutation and 41.5% had exon 19 deletion. The primary SUVmax had an average magnitude of 12.1 (ranged from 4.8 to 24.5). 53.7% had metastases limited in 1–2 organs, whereas 46.3% had metastases in more than 2 organs. Pleural and/or pericardial effusion was present in 39% of patients. Fig. 1 shows the Kaplan-Meier plots of the ten parameters under investigation. Sex, smoking history, serum CEA level and primary SUVmax appeared to have significant effect on PFS. Disease progression was slower in female, never smoker, lower serum CEA level and lower primary SUVmax. On the other hand, the Kaplan-Meier graphs demonstrated that the prognosis was independent of patients’ age, number of comorbidities, EGFR mutation site, number of metastatic organs and presence/absence of effusion. Table 2 shows the statistical results based on Mantel-Cox tests. Among the ten parameters under investigation, primary SUVmax, serum CEA level, sex and smoking history had statistically significant effects on the PFS, with the corresponding p values of 0.001, 0.023, 0.034 and 0.041 respectively. In particular, the p value of primary SUVmax group was the lowest among the four important factors. To further investigate the effects of prognostic factors on PFS, waterfall plots were constructed in Fig. 2. Each bar represented an individual’s PFS and this was ranked in descending order along x-axis, with the longest time (730 day) in the leftmost to the shortest time in the rightmost. The x-axis was offset to the mean PFS of the whole studied population (430 days in the current study). Bars above x-axis represented the individuals with corresponding PFS greater than average. Bars below x-axis represented the individuals with PFS less than average. Red and black bars in Fig. 2a represented the individuals with primary SUVmax ≤ 12 and > 12 respectively. Majority of the bars above x-axis belonged to red, whereas majority of the bars below x-axis belong to black (i.e. bimodal distribution). This implied that patients with SUVmax ≤ 12 had longer PFS than those with SUVmax > 12. Similar findings, although less prominent, were noted in Fig. 2b to d for serum CEA, sex and smoking history respectively. No difference in PFS was noted for the remaining 6 parameters (age, number of co-morbidities, performance status, EGFR mutation site, number of metastatic organs, and presence of effusion). As an illustration, Fig. 2e shows the waterfall plot based on age analysis, of which the
5. Conclusion This study explores the potential parameters affecting disease control of EGFR mutated adenocarcinoma of lung under TKI treatment. Based on Kaplan-Meier plots with Mantel-Cox analyses, four prognostic factors are identified: SUVmax, serum CEA level, sex and smoking history. In particular, SUVmax appears to be the most crucial factor among the four. Closer disease monitoring is recommended for male, ever 4
European Journal of Radiology 123 (2020) 108780
K.S. Ng, et al.
Fig. 2. Waterfall plots of PFS according to (a) SUVmax (red: ≤12, black: > 12), (b) serum CEA (red: ≤40, black: > 40 ng/mL), (c) sex (red: female, black: male), (d) smoking history (red: never smoker, black: ever smoker) and (e) age (red: ≤70, black: > 70 year old). The y axis is offset to the average PFS of the studied population (430 days).
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European Journal of Radiology 123 (2020) 108780
K.S. Ng, et al.
smoker, with high serum CEA level and high primary SUVmax as they have higher risk of early disease progression.
1497–1500. [12] F.A. Shepherd, J. Rodrigues Pereira, T. Ciuleanu, E.H. Tan, V. Hirsh, S. Thongprasert, D. Campos, S. Maoleekoonpiroj, M. Smylie, R. Martins, M. van Kooten, Erlotinib in previously treated non–small-cell lung cancer, N. Engl. J. Med. 14 (2) (2005) 123–132. [13] A. Marchetti, C. Martella, L. Felicioni, F. Barassi, S. Salvatore, A. Chella, P.P. Camplese, T. Iarussi, F. Mucilli, Mezzetti A, F. Cuccurullo. EGFR mutations in non–small-cell lung cancer: analysis of a large series of cases and development of a rapid and sensitive method for diagnostic screening with potential implications on pharmacologic treatment, J. Clin. Oncol. 1 (4) (2005) 857–865. [14] S. Sheikhbahaei, E. Mena, A. Yanamadala, S. Reddy, L.B.L. Solnes, J. Wachsmann, R.M. Subramaniam, The value of FDG PET/CT in treatment response assessment, follow-up, and surveillance of lung cancer, AJR 208 (2) (2017) 420–433. [15] Y.I. Kim, J.C. Paeng, Y.S. Park, G.J. Cheon, D.S. Lee, J.K. Chung, K.W. Kang, 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, AJR 1 (2018) 1346–1351. [16] Z. Lv, J. Fan, J. Xu, F. Wu, Q. Huang, M. Guo, T. Liao, S. Liu, X. Lan, S. Liao, W. Geng, Value of 18 F–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 45 (5) (2018) 735–750 1. [17] L. Lyu, N. Wu, L. Ying, F. Yan, L. Xiaomeng, L. Ying, Early predictive and prognostic value of 18F-FDG PET/CT for response assessment in non-small cell lung cancer treated with EGFR-TKI, JNM 1 (58) (2017) 143. [18] H.A. Nabi, J.M. Zubeldia, Clinical applications of 18F-FDG in oncology, J. Nucl. Med. Technol. 30 (1) (2002) 3–9. [19] R. Boellaard, M.J. O’Doherty, W.A. Weber, F.M. Mottaghy, M.N. Lonsdale, S.G. Stroobants, W.J. Oyen, J. Kotzerke, O.S. Hoekstra, J. Pruim, P.K. Marsden, FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0, Eur. J. Nucl. Med. 37 (2010) 181–200. [20] N. Mantel, Evaluation of survival data and two new rank order statistics arising in its consideration, Cancer Chemother. Rep. 50 (1966) 163–170. [21] R.S. Herbst, A.M. Maddox, M.L. Rothenberg, E.J. Small, E.H. Rubin, J. Baselga, F. Rojo, W.K. Hong, H. Swaisland, S.D. Averbuch, J. Ochs, Selective oral epidermal growth factor receptor tyrosine kinase inhibitor ZD1839 is generally well-tolerated and has activity in non–small-cell lung cancer and other solid tumors: results of a phase I trial, J. Clin. Oncol. 15 (18) (2002) 3815–3825. [22] E.C. Lin, A. Abass, PET And PET/CT: a Clinical Guide, (2009) E142-E142. [23] W. Pao, J. Chmielecki, Rational, biologically based treatment of EGFR-mutant nonsmall-cell lung cancer, Nat. Rev. Cancer 10 (11) (2010) 760. [24] S.G. Wu, J.Y. Shih, Management of acquired resistance to EGFR TKI–targeted therapy in advanced non-small cell lung cancer, Mol. Cancer 38 (1) (2018) 1;17. [25] T. Yoshida, H. Tanaka, H. Kuroda, J. Shimizu, Y. Horio, Y. Saka, Y. Inaba, H. Iwata, T. Hida, Y. Yatabe, Standardized uptake value on 18F-FDG-PET/CT is a predictor of EGFR T790M mutation status in patients with acquired resistance to EGFR-TKIs, Lung Cancer 1 (100) (2016) 14–19. [26] R. Rosell, E. Carcereny, R. Gervais, A. Vergnenegre, B. Massuti, E. Felip, R. Palmero, R. Garcia-Gomez, C. Pallares, J.M. Sanchez, R. Porta, 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. 31 (3) (2012) 239–246. [27] M. Fukuoka, Y.L. Wu, S. Thongprasert, P. Sunpaweravong, S.S. Leong, V. Sriuranpong, T.Y. Chao, K. Nakagawa, D.T. Chu, N. Saijo, E.L. Duffield, Biomarker analyses and final overall survival results from a phase III, randomized, open-label, first-line study of gefitinib versus carboplatin/paclitaxel in clinically selected patients with advanced non–small-cell lung cancer in Asia (IPASS), J. Clin. Oncol. 13 (21) (2011) 2866–2874. [28] J.Y. Wu, S.G. Wu, C.H. Yang, Y.L. Chang, Y.C. Chang, Y.C. Hsu, J.Y. Shih, P.C. Yang, Comparison of gefitinib and erlotinib in advanced NSCLC and the effect of EGFR mutations, Lung Cancer. 31 (2) (2011) 205–212.
Financial support Nil. Funding No. Declaration of Competing Interest None for all authors. References [1] B.W. Stewart, P. Kleihues, World Cancer Report, IARC press, Lyon, 2003. [2] M.J. Horner, L.A. Ries, M. Krapcho, N. Neyman, R. Aminou, N. Howlader, S.F. Altekruse, E.J. Feuer, L. Huang, A. Mariotto, B.A. Miller, SEER Cancer Statistics Review, National Cancer Institute, 2006. [3] P. Goldstraw, J. Crowley, K. Chansky, D.J. Giroux, P.A. Groome, R. Rami-Porta, P.E. Postmus, V. Rusch, L. Sobin, International Association for the Study of Lung Cancer International Staging Committee. The IASLC Lung Cancer Staging Project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumours, J. Thorac. Oncol. 31 (8) (2007) 706–714. [4] R.J. Cerfolio, A.S. Bryant, B. Ohja, A.A. Bartolucci, The maximum standardized uptake values on positron emission tomography of a non-small cell lung cancer predict stage, recurrence, and survival, J. Thorac. Cardiovasc. Surg. 31 (1) (2005) 151–159. [5] H.J. Jeong, J.J. Min, J.M. Park, J.K. Chung, B.T. Kim, J.M. Jeong, D.S. Lee, M.C. Lee, S.K. Han, Y.S. Shim, Determination of the prognostic value of [18F] fluorodeoxyglucose uptake by using positron emission tomography in patients with non-small cell lung cancer, Nucl. Med. Commun. 1 (9) (2002) 865–870. [6] K. Higashi, Y. Ueda, Y. Arisaka, T. Sakuma, Y. Nambu, Oguchi M, H. Seki, S. Taki, H. Tonami, I. Yamamoto, 18F-FDG uptake as a biologic prognostic factor for recurrence in patients with surgically resected non–small cell lung cancer, J. Nucl. Med. 1 (1) (2002) 39–45. [7] R.J. Cerfolio, A.S. Bryant, T.S. Winokur, B. Ohja, A.A. Bartolucci, Repeat FDG-PET after neoadjuvant therapy is a predictor of pathologic response in patients with nonsmall cell lung cancer, Ann. Thorac. Surg. 31 (6) (2004) 1903–1909. [8] V. Ahuja, R.E. Coleman, J. Herndon, E.F. Patz, The prognostic significance of fluorodeoxyglucose positron emission tomography imaging for patients with nonsmall cell lung carcinoma, Cancer 1 (5) (1998) 918–924. [9] J.F. Vansteenkiste, S.G. Stroobants, P.J. Dupont, P.R. De Leyn, E.K. Verbeken, G.J. Deneffe, L.A. Mortelmans, M.G. Demedts, Prognostic importance of the standardized uptake value on 18F-fluoro-2-deoxy-glucose–positron emission tomography scan in non–small-cell lung cancer: an analysis of 125 cases, J. Clin. Oncol. 17 (10) (1999) 3201–3206. [10] M.S. Tsao, A. Sakurada, J.C. Cutz, C.Q. Zhu, S. Kamel-Reid, J. Squire, I. Lorimer, T. Zhang, N. Liu, M. Daneshmand, P. Marrano, Erlotinib in lung cancer—molecular and clinical predictors of outcome, N. Engl. J. Med. 14 (2) (2005) 133–144. [11] J.G. Paez, P.A. Jänne, J.C. Lee, S. Tracy, H. Greulich, S. Gabriel, P. Herman, F.J. Kaye, N. Lindeman, T.J. Boggon, K. Naoki, EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy, Science 4 (5676) (2004)
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Contents lists available at ScienceDirect
European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad
Prognostic factors of EGFR-mutated metastatic adenocarcinoma of lung
T
Ng Kwok Sing*, Chu King Sun, Kung Boom Ting, Au Yong Ting Kun Nuclear Medicine Unit and Clinical PET Centre, Queen Elizabeth Hospital, Hong Kong, PR China
A R T I C LE I N FO
A B S T R A C T
Keywords: Lung cancer FDG PET/CT Prognosis EGFR
Purpose: The first-line treatment of metastatic lung adenocarcinoma with epidermal growth factor receptor (EGFR) mutation is tyrosine kinase inhibitor (TKI). This study aimed to evaluate potential factors affecting the progression-free survival under TKI treatment. Methods: Forty one patients with EGFR-mutated metastatic lung adenocarcinoma under first-line TKI treatment were retrospectively evaluated. Ten factors potentially influencing the progression-free survival were studied: patients’ age, gender, smoking history, number of comorbidities, performance status, tumor mutation site, maximum of standardized uptake value (SUVmax) of primary tumor in FDG PET/CT, serum CEA level, number of metastatic organs and presence of pleural/pericardial effusion. Mantel-Cox tests and waterfall plots were performed for statistical analyses. Results: Statistical evaluation demonstrated that primary SUVmax, serum CEA level, gender and smoking history were important prognostic factors, with corresponding p values of 0.001, 0.023, 0.034 and 0.041 respectively in Mantel-Cox analyses. Conclusion: Low primary SUVmax, low serum CEA level, female and never smoker were four prognostic factors suggestive of good response to TKI in mutated EGFR metastatic lung adenocarcinoma. SUVmax is probably the most important among the four factors.
1. Introduction Among all malignancies in the world, lung cancer has the highest incidence and mortality [1]. It is also one of the most aggressive cancers with low 5-year survival rate: 50% for stage 1A and 2% for stage IV [2,3]. Prognostic systems have been previously developed to predict stage [4], post-operative mortality [5], disease recurrence [6,7] and survival [4–6,8,9]. Prior prognostic systems have focused mainly on early and operable stages. Unfortunately, 80% of the newly diagnosed lung cancer patients belong to advanced stage [2]. The prognosis of advanced lung carcinoma depends on the treatment modalities, which in turn depends on the presence of genetic mutation in the tumor. If the mutation is druggable, molecular targeted therapies can be offered specifically. The most common mutation is epidermal growth receptor (EGFR), with prevalence of 23% in lung cancer [10]. Tyrosine kinase inhibitors (TKI) have been demonstrated to prolong the survival of EGFR-mutated patients [10–12]. In particular, female and never smoker were noticed to have higher treatment response rates because they had higher frequencies of EGFR mutation (odd ratios of 2.90 and 3.63 respectively) [13]. On the other hand, potential parameters affecting the efficacy of TKI have not been well understood. The current study aimed
to evaluate the prognostic factors affecting the progression-free survival of EGFR-mutated lung adenocarcinoma under TKI treatment. 18 Ffluoro-2-deoxy-D-glucose (FDG) positron emission tomography and computed tomography (PET-CT) is a valuable tool for staging as well as surveillance of lung cancer [14]. Prior reports have showed that the uptake of localized lung adenocarcinoma was higher in EGFR mutated than in wild type tumors [15,16]. Another study has suggested that early PET/CT assessment at a month after TKI treatment could predict tumor response, with sensitivity of 91.9% and specificity of 73.7% [17]. The prognostic nature of FDG PET/CT was therefore evaluated in this investigation. 2. Methods A.) Patient recruitment Patients who underwent FDG PET/CT in our centre from June 2013 to August 2015 for the staging of newly diagnosed lung cancer were retrospectively recruited. Subjects were included only if they fulfilled all criteria of having:
⁎ Corresponding author at: Nuclear Medicine Unit and Clinical PET Centre, LG Block, Queen Elizabeth Hospital, Gascoigne Rd 30, Yau Ma Tei, Hong Kong, PR China. E-mail address: [email protected] (K.S. Ng).
https://doi.org/10.1016/j.ejrad.2019.108780 Received 15 July 2019; Received in revised form 7 November 2019; Accepted 22 November 2019 0720-048X/ © 2019 Elsevier B.V. All rights reserved.
European Journal of Radiology 123 (2020) 108780
K.S. Ng, et al.
• histological diagnosis of EGFR mutated lung adenocarcinoma, • evidence of distant metastases in PET-CT and/or other imaging modalities (e.g. CT, MRI, bone scan), • staging PET-CT in our institution before oncological treatment, • 1 generation tyrosine kinase inhibitor (e.g. 150 mg Erlotinib daily or 250 mg Gefitinib daily) within 8 weeks after PET-CT, and • TKI treatment until disease progression.
Table 1 Characteristics of study population.
st
Subjects were excluded if they fulfilled any of the criteria:
• had blood glucose level greater than 11 mmol/L just before PET/CT acquisition (average 5.5 mmol/L, range: 3.2–10.3 mmol/L), • had oncological treatment before staging PET/CT, • were incompliant to TKI, • withdrew TKI treatment due to intolerance or side-effects (e.g. TKIinduced pneumonitis), • defaulted regular oncology follow-up, or • succumbed because of acute conditions unrelated to cancer (e.g. trauma). • PET-CT All FDG PET-CT examinations were performed with Discovery LS (General Electric Healthcare, the USA) and the administrated FDG activity was 10.7 ± 0.35 mCi . After mean uptake time of 58.7 ± 4.28 min, PET was acquired from skull vertex to mid thighs in 7–8 bed positions (2 min. per bed position) with mean axial bed coverage of 15.7 cm per bed and 9 slices bed overlap in two-dimensional acquisition mode. Reconstruction using Optimization of Ordered Subset Expectation Maximization (OSEM) was performed with 4.25 mm slice thickness in 128 × 128 matrix and processed through a standard filter. Non-contrast CT was acquired for anatomical correlation and attenuation correction with the following parameters: 120 mA tube current, 140 kV tube voltage, 0.5 s gantry rotation speed, 0.984pitch, 4.25 mm slice thickness and 512 × 512 matrix. Standardized uptake value is defined as the activity measured in volume of interest (VOI) divided by the injected FDG dose per body weight [18]:
SUV =
ActivityVOI (μmCi /mL) Doseinjected (μmCi )/Body Weight (kg )
(6)
A fixed sized 1.2cm-diameter spherical VOI was generated for SUV measurement, as recommended by the European Association of Nuclear Medicine (EANM) procedural guidelines [19]. Throughout this study, the maximum SUV (SUVmax) of primary tumor was exclusively investigated. For multiple intrapulmonary lesions, the one with the highest SUVmax was used. Body weight was routinely recorded in the same day of PET/CT acquisition, with mean weight of 56.5 ± 10.4 kg.
Variable
Number
%
Sex Male Female
20 21
48.8 51.2
Age ≤70 > 70 Average
25 16 68.1 (40–91)
61.0 39.0
Smoking history Never smoker Ever smoker
32 9
78.0 22.0
Performance status 0 1 2
6 30 5
14.6 73.2 12.2
No. of comorbidities 0 to 1 > 1
18 23
43.9 56.1
EGFR exon 18 - G719X mutation 19 deletion 21 - L858R mutation
2 17 22
4.9 41.5 53.6
Primary SUVmax ≤12 > 12 Average
25 16 12.1 (4.8–24.5)
61.0 39.0
CEA ≤40 ng/mL > 40 ng/mL
22 19
53.7 46.3
No of metastatic organ 1–2 >2
22 19
53.7 46.3
Presence of effusion Yes No
16 25
39.0 61.0
2 3 4 5 6 7 8
A.) Disease Monitoring Disease status was regularly monitored by oncologists based on physical examination, biochemical markers (e.g. serum carcinoembryonic antigen CEA level) and radiological imaging (e.g. chest X ray, CT, MRI, bone scan, PET-CT). Once disease progression was diagnosed, TKI would be terminated. Progression-free survival (PFS) was defined as the duration from the beginning to the end of TKI usage. All patients were evaluated for 730 days after the start of TKI. If a patient had no evidence of disease progression at the end of the evaluation period, the PFS was assigned to be 730 days.
9 10
sex (male vs. female) smoking history (ever smoker vs. never smoker) number of co-morbidities (e.g. hypertension, diabetes; ≤1 vs. > 1) baseline Eastern Cooperative Oncology Group (ECOG) performance status (PS) when TKI was started (0 vs. 2) EGFR mutation site (exon 19 deletion vs. exon 18 L828R mutation) primary SUVmax in staging PET-CT (≤12 vs. > 12) baseline serum CEA level before TKI was started (≤40 vs. > 40 ng/ mL) number of metastatic organs in staging PET-CT (e.g. intrapulomonary, liver, adrenal; ≤2 vs. > 2) presence of pericardial and/or pleural effusion in staging PET-CT (present vs. absent)
The effects of the ten parameters on the PFS were investigated with the Kaplan Meier plots. Statistical analyses were performed using Mantel-Cox method [20]. The results were defined as statistically significant if the corresponding p values < 0.05. All statistical evaluations were performed with SPSS Statistics 20 (IBM Corporation, the USA).
A.) Statistical analysis
A.) Approval by IRB
Ten potential prognostic factors were evaluated, with the corresponding cutoffs taken at approximately the mean or the median:
This study was approved by the ethic review board at our institution, and informed consent was waived.
1 age (≤70 vs. > 70 year old)
2
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Fig. 1. Kaplan-Meier plots of progression-free survival against time according to (a) age, (b) sex, (c) smoking history, (d) number of co-morbidties, (e) ECOG performance status, (f) EGFR mutation site, (g) primary SUVmax, (h) serum CA level, (i) number of metastatic organs and (j) presence of pleural and/or pericardial effusion.
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red/black bars distribution pattern was not bimodal.
Table 2 Univariate analysis of study population. Parameter
Comparison
PFS: mean
PFS: standard deviation
p value
Sex
Male Female ≤ 70 > 70 Never smoker Ever smoker 0 2 0 to 1 > 1 19 deletion L858R mutation < 12 > 12 ≤ 40 ng/mL > 40 ng/mL 1–2 > 2 Yes No
367 488 448 340 459 321 592 449 406 447 437 430 517 291 507 342 471 380 368 469
48.3 47.0 44.9 55.1 39.1 70.5 82.0 109.0 52.7 46.5 55.1 48.5 41.3 43.8 48.1 46.1 44.3 53.2 53.5 44.3
0.034
Age Smoking history Performance status No. of comorbidities EGFR exon Primary SUVmax CEA No of metastatic organs Presence of effusion
4. Discussion The treatment efficacy in advanced cancer depends on the response rate and PFS. The response rate of TKI in advanced lung carcinoma was demonstrated to be much higher if EGFR was mutated [10,12,13,21]. In particular, the responding rates have been reported to be greater in female and never smoker [13]. For PFS, this study evaluated 10 parameters covering demographic, physical, social, medical, biochemical, immunohistochemistrical and radiological aspects. The current results showed that primary SUVmax ≤ 12, serum CEA ≤ 40 ng/mL, female and never smoker were good prognostic factors. Their corresponding p values in Mantel-Cox analyses were 0.001, 0.023, 0.034 and 0.041 respectively. SUVmax appears to be the most important prognostic factors among the four with the least p value. This is supported by Fig. 2 which shows the distribution of red and black bars were more bimodal in SUVmax group than in serum CEA, sex and smoking groups. SUVmax reflects glucose metabolic rate, proliferation and vascularisation, all of which are higher in tumor than in normal tissue [22]. Tumor with greater metabolism and proliferation (i.e. greater SUVmax) probably can undergo more cell divisions. Such tumor can have greater potential of developing secondary mutation, acquiring resistance to TKI and shortening PFS [23]. This may explain why SUVmax appeared more relevant to PFS than smoking history, sex and serum CEA level. The current finding could have important clinical implication: patients with greater SUVmax have higher risk of early disease progression. Thus they should have closer disease monitoring proactively and switch to second-line therapy early if indicated. This has clinical importance because Osimertinib has been demonstrated to be an effective treatment for T790M-positive lung cancer resistant to first generation TKI [24]. Recent study also suggested that T790M-positive lung tumor had lower SUVmax [25]. Interestingly, the current study showed that PFS depended on sex and smoking history. This observation was probably not coincidental: as female and never smoker had higher possibilities of EGFR mutation, they would respond to the anti-EGFR treatment better and have longer PFS [13]. Longer PFS was reported in patients with exon 19 deletion than with L858R mutation [26]. This finding was only observed in Erlotinib but not in Gefitinib [27]. However, other studies reported similar efficacy between Erlotinib and Gefitinib [28]. In the current study, patients under Erlotinib treatment showed no statistically significant difference in PFS between exon 19 deletion and L858R mutation (p = 0.754). One pitfall of the current report was the short evaluation period of 730 days from the start of TKI. For the patients with no evidence of disease progression by 730 days, their PFS were taken as 730 days. This applied to 24.4% of the patients and their PFS were undervalued. The underestimation was particularly important in never smoker, SUVmax ≤12, female and CEA ≤ 40 ng/dL, because their non-progression fractions were much higher than their counterparts (31.3% vs. 0%; 36% vs. 6.3%, 38.1% vs. 10% and 38.1% vs. 10% respectively). Another pitfall was the selection bias of performance status, which was 14.6% for PS 0, 73.2% for PS 1 and 12.2% for PS 2. Although Fig. 1(e) suggests that the PFS of PS 0 appears longer than that of PS 2, no statistical significance was observed (p = 0.337) because of the limited number of patients in the two groups.
0.426 0.041 0.337 0.512 0.849 0.001 0.023 0.324 0.133
3. Results Table 1 shows the characteristics of the study population. A total of 41 adult patients of age 40–91 year old were recruited. Male to female ratio was around 1: 1, majority were never smoker and had ECOG performance status of 1. For the pathology nature, 53.6% had exon 21 L858R mutation and 41.5% had exon 19 deletion. The primary SUVmax had an average magnitude of 12.1 (ranged from 4.8 to 24.5). 53.7% had metastases limited in 1–2 organs, whereas 46.3% had metastases in more than 2 organs. Pleural and/or pericardial effusion was present in 39% of patients. Fig. 1 shows the Kaplan-Meier plots of the ten parameters under investigation. Sex, smoking history, serum CEA level and primary SUVmax appeared to have significant effect on PFS. Disease progression was slower in female, never smoker, lower serum CEA level and lower primary SUVmax. On the other hand, the Kaplan-Meier graphs demonstrated that the prognosis was independent of patients’ age, number of comorbidities, EGFR mutation site, number of metastatic organs and presence/absence of effusion. Table 2 shows the statistical results based on Mantel-Cox tests. Among the ten parameters under investigation, primary SUVmax, serum CEA level, sex and smoking history had statistically significant effects on the PFS, with the corresponding p values of 0.001, 0.023, 0.034 and 0.041 respectively. In particular, the p value of primary SUVmax group was the lowest among the four important factors. To further investigate the effects of prognostic factors on PFS, waterfall plots were constructed in Fig. 2. Each bar represented an individual’s PFS and this was ranked in descending order along x-axis, with the longest time (730 day) in the leftmost to the shortest time in the rightmost. The x-axis was offset to the mean PFS of the whole studied population (430 days in the current study). Bars above x-axis represented the individuals with corresponding PFS greater than average. Bars below x-axis represented the individuals with PFS less than average. Red and black bars in Fig. 2a represented the individuals with primary SUVmax ≤ 12 and > 12 respectively. Majority of the bars above x-axis belonged to red, whereas majority of the bars below x-axis belong to black (i.e. bimodal distribution). This implied that patients with SUVmax ≤ 12 had longer PFS than those with SUVmax > 12. Similar findings, although less prominent, were noted in Fig. 2b to d for serum CEA, sex and smoking history respectively. No difference in PFS was noted for the remaining 6 parameters (age, number of co-morbidities, performance status, EGFR mutation site, number of metastatic organs, and presence of effusion). As an illustration, Fig. 2e shows the waterfall plot based on age analysis, of which the
5. Conclusion This study explores the potential parameters affecting disease control of EGFR mutated adenocarcinoma of lung under TKI treatment. Based on Kaplan-Meier plots with Mantel-Cox analyses, four prognostic factors are identified: SUVmax, serum CEA level, sex and smoking history. In particular, SUVmax appears to be the most crucial factor among the four. Closer disease monitoring is recommended for male, ever 4
European Journal of Radiology 123 (2020) 108780
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Fig. 2. Waterfall plots of PFS according to (a) SUVmax (red: ≤12, black: > 12), (b) serum CEA (red: ≤40, black: > 40 ng/mL), (c) sex (red: female, black: male), (d) smoking history (red: never smoker, black: ever smoker) and (e) age (red: ≤70, black: > 70 year old). The y axis is offset to the average PFS of the studied population (430 days).
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smoker, with high serum CEA level and high primary SUVmax as they have higher risk of early disease progression.
1497–1500. [12] F.A. Shepherd, J. Rodrigues Pereira, T. Ciuleanu, E.H. Tan, V. Hirsh, S. Thongprasert, D. Campos, S. Maoleekoonpiroj, M. Smylie, R. Martins, M. van Kooten, Erlotinib in previously treated non–small-cell lung cancer, N. Engl. J. Med. 14 (2) (2005) 123–132. [13] A. Marchetti, C. Martella, L. Felicioni, F. Barassi, S. Salvatore, A. Chella, P.P. Camplese, T. Iarussi, F. Mucilli, Mezzetti A, F. Cuccurullo. EGFR mutations in non–small-cell lung cancer: analysis of a large series of cases and development of a rapid and sensitive method for diagnostic screening with potential implications on pharmacologic treatment, J. Clin. Oncol. 1 (4) (2005) 857–865. [14] S. Sheikhbahaei, E. Mena, A. Yanamadala, S. Reddy, L.B.L. Solnes, J. Wachsmann, R.M. Subramaniam, The value of FDG PET/CT in treatment response assessment, follow-up, and surveillance of lung cancer, AJR 208 (2) (2017) 420–433. [15] Y.I. Kim, J.C. Paeng, Y.S. Park, G.J. Cheon, D.S. Lee, J.K. Chung, K.W. Kang, 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, AJR 1 (2018) 1346–1351. [16] Z. Lv, J. Fan, J. Xu, F. Wu, Q. Huang, M. Guo, T. Liao, S. Liu, X. Lan, S. Liao, W. Geng, Value of 18 F–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 45 (5) (2018) 735–750 1. [17] L. Lyu, N. Wu, L. Ying, F. Yan, L. Xiaomeng, L. Ying, Early predictive and prognostic value of 18F-FDG PET/CT for response assessment in non-small cell lung cancer treated with EGFR-TKI, JNM 1 (58) (2017) 143. [18] H.A. Nabi, J.M. Zubeldia, Clinical applications of 18F-FDG in oncology, J. Nucl. Med. Technol. 30 (1) (2002) 3–9. [19] R. Boellaard, M.J. O’Doherty, W.A. Weber, F.M. Mottaghy, M.N. Lonsdale, S.G. Stroobants, W.J. Oyen, J. Kotzerke, O.S. Hoekstra, J. Pruim, P.K. Marsden, FDG PET and PET/CT: EANM procedure guidelines for tumour PET imaging: version 1.0, Eur. J. Nucl. Med. 37 (2010) 181–200. [20] N. Mantel, Evaluation of survival data and two new rank order statistics arising in its consideration, Cancer Chemother. Rep. 50 (1966) 163–170. [21] R.S. Herbst, A.M. Maddox, M.L. Rothenberg, E.J. Small, E.H. Rubin, J. Baselga, F. Rojo, W.K. Hong, H. Swaisland, S.D. Averbuch, J. Ochs, Selective oral epidermal growth factor receptor tyrosine kinase inhibitor ZD1839 is generally well-tolerated and has activity in non–small-cell lung cancer and other solid tumors: results of a phase I trial, J. Clin. Oncol. 15 (18) (2002) 3815–3825. [22] E.C. Lin, A. Abass, PET And PET/CT: a Clinical Guide, (2009) E142-E142. [23] W. Pao, J. Chmielecki, Rational, biologically based treatment of EGFR-mutant nonsmall-cell lung cancer, Nat. Rev. Cancer 10 (11) (2010) 760. [24] S.G. Wu, J.Y. Shih, Management of acquired resistance to EGFR TKI–targeted therapy in advanced non-small cell lung cancer, Mol. Cancer 38 (1) (2018) 1;17. [25] T. Yoshida, H. Tanaka, H. Kuroda, J. Shimizu, Y. Horio, Y. Saka, Y. Inaba, H. Iwata, T. Hida, Y. Yatabe, Standardized uptake value on 18F-FDG-PET/CT is a predictor of EGFR T790M mutation status in patients with acquired resistance to EGFR-TKIs, Lung Cancer 1 (100) (2016) 14–19. [26] R. Rosell, E. Carcereny, R. Gervais, A. Vergnenegre, B. Massuti, E. Felip, R. Palmero, R. Garcia-Gomez, C. Pallares, J.M. Sanchez, R. Porta, 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. 31 (3) (2012) 239–246. [27] M. Fukuoka, Y.L. Wu, S. Thongprasert, P. Sunpaweravong, S.S. Leong, V. Sriuranpong, T.Y. Chao, K. Nakagawa, D.T. Chu, N. Saijo, E.L. Duffield, Biomarker analyses and final overall survival results from a phase III, randomized, open-label, first-line study of gefitinib versus carboplatin/paclitaxel in clinically selected patients with advanced non–small-cell lung cancer in Asia (IPASS), J. Clin. Oncol. 13 (21) (2011) 2866–2874. [28] J.Y. Wu, S.G. Wu, C.H. Yang, Y.L. Chang, Y.C. Chang, Y.C. Hsu, J.Y. Shih, P.C. Yang, Comparison of gefitinib and erlotinib in advanced NSCLC and the effect of EGFR mutations, Lung Cancer. 31 (2) (2011) 205–212.
Financial support Nil. Funding No. Declaration of Competing Interest None for all authors. References [1] B.W. Stewart, P. Kleihues, World Cancer Report, IARC press, Lyon, 2003. [2] M.J. Horner, L.A. Ries, M. Krapcho, N. Neyman, R. Aminou, N. Howlader, S.F. Altekruse, E.J. Feuer, L. Huang, A. Mariotto, B.A. Miller, SEER Cancer Statistics Review, National Cancer Institute, 2006. [3] P. Goldstraw, J. Crowley, K. Chansky, D.J. Giroux, P.A. Groome, R. Rami-Porta, P.E. Postmus, V. Rusch, L. Sobin, International Association for the Study of Lung Cancer International Staging Committee. The IASLC Lung Cancer Staging Project: proposals for the revision of the TNM stage groupings in the forthcoming (seventh) edition of the TNM Classification of malignant tumours, J. Thorac. Oncol. 31 (8) (2007) 706–714. [4] R.J. Cerfolio, A.S. Bryant, B. Ohja, A.A. Bartolucci, The maximum standardized uptake values on positron emission tomography of a non-small cell lung cancer predict stage, recurrence, and survival, J. Thorac. Cardiovasc. Surg. 31 (1) (2005) 151–159. [5] H.J. Jeong, J.J. Min, J.M. Park, J.K. Chung, B.T. Kim, J.M. Jeong, D.S. Lee, M.C. Lee, S.K. Han, Y.S. Shim, Determination of the prognostic value of [18F] fluorodeoxyglucose uptake by using positron emission tomography in patients with non-small cell lung cancer, Nucl. Med. Commun. 1 (9) (2002) 865–870. [6] K. Higashi, Y. Ueda, Y. Arisaka, T. Sakuma, Y. Nambu, Oguchi M, H. Seki, S. Taki, H. Tonami, I. Yamamoto, 18F-FDG uptake as a biologic prognostic factor for recurrence in patients with surgically resected non–small cell lung cancer, J. Nucl. Med. 1 (1) (2002) 39–45. [7] R.J. Cerfolio, A.S. Bryant, T.S. Winokur, B. Ohja, A.A. Bartolucci, Repeat FDG-PET after neoadjuvant therapy is a predictor of pathologic response in patients with nonsmall cell lung cancer, Ann. Thorac. Surg. 31 (6) (2004) 1903–1909. [8] V. Ahuja, R.E. Coleman, J. Herndon, E.F. Patz, The prognostic significance of fluorodeoxyglucose positron emission tomography imaging for patients with nonsmall cell lung carcinoma, Cancer 1 (5) (1998) 918–924. [9] J.F. Vansteenkiste, S.G. Stroobants, P.J. Dupont, P.R. De Leyn, E.K. Verbeken, G.J. Deneffe, L.A. Mortelmans, M.G. Demedts, Prognostic importance of the standardized uptake value on 18F-fluoro-2-deoxy-glucose–positron emission tomography scan in non–small-cell lung cancer: an analysis of 125 cases, J. Clin. Oncol. 17 (10) (1999) 3201–3206. [10] M.S. Tsao, A. Sakurada, J.C. Cutz, C.Q. Zhu, S. Kamel-Reid, J. Squire, I. Lorimer, T. Zhang, N. Liu, M. Daneshmand, P. Marrano, Erlotinib in lung cancer—molecular and clinical predictors of outcome, N. Engl. J. Med. 14 (2) (2005) 133–144. [11] J.G. Paez, P.A. Jänne, J.C. Lee, S. Tracy, H. Greulich, S. Gabriel, P. Herman, F.J. Kaye, N. Lindeman, T.J. Boggon, K. Naoki, EGFR mutations in lung cancer: correlation with clinical response to gefitinib therapy, Science 4 (5676) (2004)
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