Lymph Node Maximum Uptake of 18F-ALF-NOTA-PRGD2 II PET/CT Predicts Lung Cancer Survival

Lymph Node Maximum Uptake of 18F-ALF-NOTA-PRGD2 II PET/CT Predicts Lung Cancer Survival | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Original research Lymph Node Maximum Uptake of 18F-ALF-NOTA-PRGD2 II PET/CT Predicts Lung Cancer Survival Yuchun Wei, Li Ma, Jinsong Zheng, Yanqing Pei, Xueting Qin, Xiaohui Luan, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-125389/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Tumor angiogenesis plays a key role in tumor growth, development, and metastasis, so the exploratory study of tumor neovascularization imaging is one of the potential methods to predict survival. This study aims to examine the predictive capacity of 18 F-ALF-NOTA-PRGD2 II (denoted 18 F-Alfatide II) positron emission tomography (PET)/computed tomography (CT) before antitumor therapy (ATR) in patients with lung cancer. Results The median follow-up was 31 (1.3~57.0) months. Among the patients, 6 were lost to follow-up. The overall survival (OS) and progression-free survival (PFS) were 40.0 (3.50~57.0) months and 21.30 (2.0~56.0) months, respectively. The maximum uptake values (SUV max ) of the metastatic lymph nodes (SUV LN ) and tumor node metastasis (TNM) staging were significant predictors of PFS and OS (all P<0.05) in a multivariate Cox regression analysis. Statistical significance was not reached by any other variable in the multivariate analysis. Receiver operating curve (ROC) analysis for survival revealed an area under the curve of 0.93 (P<0.001) for SUV LN and 0.96 for the TNM stage (P<0.001). The SUV LN and TNM stage cutoff values were 2.50 and II, and their sensitivity, specificity and positive and negative prediction were 77.42%, 80.0% and 82.76% and 74.07%; and 87.10%, 60.0% and 72.97% and 78.95%, respectively. Patients with a lower SUV LN and early stage had a longer PFS and OS (all P<0.05). Conclusions For lung cancer, low SUV LN and an early TNM stage (≤stage II) as assessed before ATR by 18 F-alfatide II PET/CT represents a favorable subgroup with increased PFS and OS. Health Economics & Outcomes Research 18F-ALF-NOTA-PRGD2 II PET/CT Lung cancer Survival Figures Figure 1 Figure 1 Figure 2 Figure 2 1. Background Lung cancer remains the leading cause of cancer incidence and mortality worldwide 1 , with 2.1 million new lung cancer cases and 1.8 million deaths predicted in 2018, representing close to 1 in 5 (18.4%) cancer deaths 2 . The treatment methods are surgery, radiotherapy, chemotherapy, targeting and so on; the therapeutic effect is poor, and there is a significant difference in outcomes 3 , the main reason for which is the widespread heterogeneity of tumors. The functional molecular imaging of positron emission tomography (PET) can be used to detect the internal characteristics of the whole tumor, such as glucose metabolism, angiogenesis, and hypoxia. With the development of individualized functional metabolic imaging, molecular imaging techniques are promising to predict the prognosis of lung cancer. Arginine-glycine-aspartic acid peptide (Arg-Gly-Asp, RGD) enables a new kind of positron drug, which is approved for clinical trials and can safely 4 and effectively image the angiogenesis of non-small-cell lung cancer (NSCLC) 5 , 6 with clarity and desirable image contrast. Tumor angiogenesis plays an important role in regulating growth, local invasiveness, and metastatic potential 7 . Previously, we performed a pilot clinical study that demonstrated the feasibility of using 18 F-ALF-NOTA-PRGD2 II (denoted 18 F-alfatide II) PET/computed tomography (CT) to predict the short-term outcome of concurrent chemoradiotherapy in patients with advanced NSCLC 6 . However, there are few reports on whether 18 F-alfatide II can predict the long-term survival of lung cancer. In the present study, we analyze standard uptake values (SUVs) of 18 F-alfatide II on PET/CT before antitumor therapy (ATR) and explore its predictive value in overall survival (OS) and progression-free survival (PFS) of patients with lung cancer. 2. Material And Methods 2.1 Patients Between June 10, 2015, and Dec 28, 2016, a total of sixty-two patients with pathologically confirmed lung cancer were enrolled in the study. This prospective study was approved by the local ethics committee of Shandong Cancer Hospital and Institute, and each patient gave written informed consent before the study. All patients were treated in Shandong Cancer Hospital and satisfied the following criteria: ① diagnosed by histological and imaging examination such as CT or 18 F-fluorodeoxyglucose (FDG) PET/CT; ② an Eastern Cooperative Oncology Group (ECOG) score of ≤ 1; ③ clearly measurable metastatic lymph nodes and primary tumors; and ④ no ATR before the 18 F-alfatide II PET/CT scan. 2.2 Radiotracer preparation A simple lyophilization kit for labeling PRGD2 peptide was purchased from the Jiangsu Institute of Nuclear Medicine, and the synthesis process was carried out by reference to the previous study 8 . The radiochemical purity of the 18 F-alfatide exceeded 99%, and its specific radioactivity exceeded 37 GBq (1,000 mCi)/µmol. 2.3 PET/CT scanning Patients were given an intravenous injection of 4.81 MBq/kg (0.12 mCi/kg) 18 F-alfatide II and allowed to rest for approximately 60 minutes. Patients were not requested to fast but were requested to specify their recent diet to allow estimation of blood glucose levels. Scanning was performed with an integrated inline PET/CT system (GEMINI TF Big Bore; Philips Healthcare). PET images were acquired from the head to the thigh, and the spiral CT component was obtained with an X-ray tube voltage peak of 120 kV, 300 mAs. A full-ring dedicated PET scan of the same axial range followed. The patients exhibited normal shallow respiration during image acquisition. The images were attenuation-corrected with the transmission data from CT. The attenuation-corrected PET images, CT images, and fused PET/CT images, displayed as coronal, sagittal, and transaxial slices, were viewed on a MEDEX workstation (Beijing, China). 2.4 Image analysis Two experienced nuclear medicine physicians assessed the 18 F-alfatide II PET/CT images visually, referring to the PET fusion and CT images, until consensus was reached. The acquired 18 F-alfatide II PET/CT data were transferred into a workstation in the DICOM format. The radiotracer concentration in the region of interest (ROI) was normalized to the injected dose per kilogram of the patients’ body weight to derive the standardized uptake values (SUVs). PET/CT parameters such as the maximum uptake values for the primary tumor (SUV P ) or metastatic lymph node (SUV LN ) and the mean SUVs for the mediastinal blood pool (SUV blood ) were generated using a vendor-provided automated contouring program. In addition, tumor-to-background ratios (TBRs) were calculated. Then, the SUV ratios of the primary tumor to blood pool, metastatic lymph node to blood pool, and primary tumor to metastatic lymph node were calculated and are denoted TBR P , TBR LN , and TBR P−LN , respectively. 2.5 Antitumor therapy Surgery is the first choice for patients who can be surgically resected. Patients without surgical indications or who are unable to tolerate surgery should choose comprehensive treatment based on radiotherapy and chemotherapy. The chemotherapy scheme is a platinum-based dual-drug. An intensity-modulated radiotherapy technique (IMRT) or a three-dimensional conformal radiotherapy technique (3D-CRT) was delivered to patients with megavoltage equipment (6 MV). Radiotherapy was given as the conventionally fractionated regimen, 180 cGy to 200 cGy for five days per week, and the total dose administered to patients ranged from 5040 cGy to 6600 cGy (median dose, 6000 cGy). The pathological type of adenocarcinoma is routine gene detection, and patients with targeted treatment can choose epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) drug therapy. 2.6 End points and assessments The two primary end points were PFS (as assessed by investigators according to RECIST criteria) and OS in all patients with lung cancer. Patients were followed up by enhanced CT every 6 weeks during treatment, every 2 months in the first year after treatment, and every six months from the second year after treatment. The OS time was from the date of diagnosis to the date of follow-up or death, and the date of PFS was from the date of diagnosis to the date of tumor recurrence or progression. General case data that might have affected the prognosis of the patients were recorded, including the sex, age, pathological type, and TNM stage (clinical stage or postoperative stage). 2.7 Statistical analysis Statistical analysis was performed using IBM SPSS Statistics for Windows version 20.0 (IBM, Armonk, USA). The Pearson test was used for continuous variables in correlation analysis, and the Spearman test was used for classified variables. Data derived from SUV measurements were analyzed for correlation with survival using receiver operating characteristic (ROC) curve analysis and the Youden index for independencies using the χ 2 test. The PFS and OS were assessed by Kaplan-Meier analysis. Cox regression proportional hazards models were used to obtain hazard ratio estimates of significant parameters derived from univariate analysis using P ≤ 0.1 for the parameters to qualify for multivariate analysis. All tests were 2-sided, and P < 0.05 was considered statistically significant. 3. Results Of the sixty-two patients (Table 1 ), 6 cases were lost to follow-up, including 2 cases (1 of stage III and 1 of stage IV) of adenocarcinoma and 4 cases (2 of stage II and 2 of stage III) of squamous cell carcinoma. As of Dec 31, 2019, the median follow-up was 31 months (range 1.30 ~ 57 months), of which 55.36% (31/56) of the patients had died. The median PFS and OS were 21.30 (range 2.0 ~ 56.0) months and 40.0 (range 3.50 ~ 57.0) months, respectively. Table 1 Baseline Characteristics Characteristics Total (n = 62) Median age, years (range) 59.5 (24–84) Sex No. (%) Male 46 (74.19) Female 16 (25.81) Histology No. (%) Small-cell lung cancer 7 (11.29) Adenocarcinoma 24 (38.71) Squamous cell carcinoma 25 (40.32) NSCLC not otherwise specified 6 (9.68) Stage No. (%) Stage I 8 (12.90) Stage II 13 (20.97) Stage III 33 (53.23) Stage IV 8 (12.90) Pretreatment SUVs on PET/CT Mean ± SD SUV P 5.18 ± 2.53 SUV LN 2.98 ± 1.68 TBR P−LN 2.15 ± 1.54 TBR LN 3.92 ± 2.29 TBR P 6.67 ± 3.32 NSCLC , non-small-cell lung cancer; SUV P , maximum standardized uptake values for primary tumor; SUV LN , maximum standardized uptake values for metastatic lymph node; TBR P , primary tumor to blood pool; TBR LN , metastatic lymph node to blood pool; TBR P−LN , metastatic lymph node to primary tumor. Table 2 shows the results of univariable and multivariable linear regression analyses performed to determine which tracked parameters are potential predictors of PFS and OS. Following multivariable analysis, two parameters remained significantly associated with PFS: SUV LN ( P = 0.001, HR 1.44, 95% CI 1.17 ~ 1.77) and stage ( P = 0.026, HR 1.77, 95% CI 1.07 ~ 2.93); the same applied to OS: SUV LN ( P = 0.001, HR 1.43, 95% CI 1.15 ~ 1.78) and stage ( P = 0.048, HR 1.66, 95% CI 1.0 ~ 2.76). Of note, differences in sex, histology, SUV P , TBR LN and TBR P were significant by univariable assessment but did not retain significance following multivariable analysis. Table 3 shows the correlation between different factors with PFS and OS. SUV LN and stage were negatively correlated with OS and PFS, all P < 0.05. Table 2 Univariate and multivariate COX regression associating baseline variables and SUVs with PFS and OS Variable PFS OS Univariate analysis Multivariate analysis Univariate analysis Multivariate analysis HR (95% CI) P Value HR (95% CI) P Value HR (95% CI) P Value HR (95% CI) P Value Sex 3.534 (1.232 ~ 10.141) 0.019 ND 0.231 3.594 (1.250 ~ 10.335) 0.018 ND 0.108 Age 1.015 (0.983 ~ 1.048) 0.361§ - - 1.012 (0.978 ~ 1.047) 0.494§ - - Histology 0.597 (0.368 ~ 0.968) 0.036 ND 0.85 0.504 (0.301 ~ 0.844) 0.009 ND 0.08 Stage 2.105 (1.359 ~ 3.259) 0.001 1.770 (1.071 ~ 2.926) 0.026* 2.099 (1.343 ~ 3.280) 0.001 1.664 (1.004 ~ 2.760) 0.048* SUV P 1.168 (1.040 ~ 1.313) 0.009 ND 0.166 1.174 (1.042 ~ 1.322) 0.008 ND 0.58 SUV LN 1.551 (1.281 ~ 1.877) < 0.001 1.441 (1.171 ~ 1.772) 0.001* 1.562 (1.282 ~ 1.902) < 0.001 1.431 (1.152 ~ 1.777) 0.001* TBR P−LN 0.750 (0.542 ~ 1.038 0.082 ND 0.907 0.748 (0.529 ~ 1.056) 0.099 ND 0.776 TBR LN 1.333 (1.150 ~ 1.546) < 0.001 ND 0.403 1.318 (1.138 ~ 1.527) < 0.001 ND 0.519 TBR P 1.134 (1.031 ~ 1.248) 0.01 ND 0.2 1.140 (1.034 ~ 1.257) 0.009 ND 0.154 PFS , progression-free survival; OS , overall survival; HR , hazard ratio; CI , confidence interval. Statistical method: Forward, LR, Cox proportional-hazards model. §Findings with P > 0.10 are not included in the multivariate Cox regression analysis. ND, not displayed; *Significant result. Table 3 Analysis of correlation between general parameters with OS/PFS and survival risk PFS OS r P r P Sex -0.273 0.032 -0.302 0.017 Age 0.011 0.933 0.108 0.402 Histology 0.349 0.005 0.453 < 0.001 Stage -0.328 0.009 -0.34 0.007 SUV P -0.29 0.022 -0.287 0.024 SUV LN -0.509 < 0.001 -0.501 < 0.001 TBR P−LN 0.263 0.039 0.206 0.109 TBR LN -0.513 < 0.001 -0.511 < 0.001 TBR P -0.357 0.004 -0.385 0.002 The ROC curve analysis of the respective parameters, applying survival as the dichotomous characteristic, revealed a significant area under the curve of 0.93 ( P < 0.001) for SUV LN (Fig. 1 ). At a cutoff value of 2.50, derived by the Youden index, the sensitivity, specificity, and positive and negative prediction were 77.42%, 80.0%, and 82.76% and 74.07%, respectively. A significant area under the curve of 0.96 ( P < 0.001) was found for stage (Fig. 1 ). At a cutoff value of II, derived by the Youden index, the sensitivity, specificity, and positive and negative prediction were 87.10%, 60.0%, and 72.97% and 78.95%, respectively. The corresponding Kaplan-Meier curves are given in Fig. 2 . Patents with a higher SUV LN (> 2.50) had a PFS of 12.35 ± 12.90 months, whereas that for patients with a lower SUV LN was 34.41 ± 17.02 months ( P < 0.001). Patients with a higher SUV LN had an OS of 22.88 ± 15.71 months, whereas patients with a lower SUV LN survived 41.91 ± 11.10 months ( P < 0.001). Patents with a higher stage (≥ stage III) had a PFS of 16.30 ± 15.03 months, whereas that for patients with a lower stage (≤ stage II) was 36.02 ± 18.24 months ( P < 0.001). Patients with a higher stage (≥ stage III) had an OS of 27.33 ± 15.99 months, whereas patients with a lower stage (≤ stage II) survived 41.26 ± 14.03 months ( P = 0.002). 4. Discussion Due to the existence of heterogeneity, the prognosis of lung cancer varies greatly, so it is very important to screen relevant prognostic indicators. With the advancement of image analysis tools, tumor metabolic characteristics can now be assessed rapidly and consistently with no interobserver variability, with the potential for routine assessment in clinical practice. Various molecular imaging techniques have been developed to predict the tumor response to therapy, such as FDG PET 9 , 18 F-fluorothymidine (FLT) PET 10 , 18 F-fluroerythronitroimidazole (FETNIM) PET 11 and 18 F-fluoromisonidazole (FMISO) PET 12 . Studies on the capabilities of 18 F-alfatide II PET/CT have increased in recent years and have shown the advantages of this imaging technique for evaluating chest tumors due to the high in vivo TBR identified in PET imaging 5 , 8 , 13 . In this study, sex, histology, stage, SUV P , SUV LN , TBR P−LN , TBR LN and TBR P were significantly associated with PFS and OS in the correlation analysis, and SUV LN before treatment in 18 F-alfatide II PET/CT and TNM staging were revealed to independently predict PFS and OS of lung cancer through multivariate Cox regression analysis. Why is 18 F-alfatide II PET/CT useful in predicting survival in patients with lung cancer? 18 F-alfatide II can bind to integrin αvβ3, which is upregulated in the activated endothelial cells with tumor angiogenesis, with high affinity and specificity. Li et al. reported that 18 F-alfatide II uptake on PET/CT can predict the response to antiangiogenic therapy, with higher 18 F-alfatide II uptake in tumors predicting a better response to apatinib therapy in a variety of tumors 14 . Luan X et al. found that SUV P and tumor-to-blood ratios can predict the short-term outcome of concurrent chemoradiotherapy (CCRT) in patients with advanced NSCLC. Patients with lower SUV P and tumor-to-blood ratios responded to CCRT (all P < 0.05) 6 . Lymph metastasis is a well-characterized negative factor affecting survival in cancer patients and reducing tumor staging. Wu C et al. suggested that tumors often drive inflammation both in primary tumor tissue and in tumor-draining lymph nodes, and the inflamed tissue can also show high uptake of 18 F-FDG and 18 F-alfatide II 4 . Chen et al. found that RGD PET provides better imaging of mediastinal lymph nodes and contralateral metastases than 18 F-FDG by providing better imaging 15 , 16 . Studies have also indicated that SUV LN both in 18 F-FDG PET/CT and in 18 F-alfatide II PET/CT is influenced by the pathological stage, lymph node states, and tumor differentiation and that it may serve as a useful new parameter for risk stratification with esophageal squamous cell carcinoma 17 . In this study, we found that SUV LN not only is significantly negative associated with PFS and OS but also may be an independent predictor for PFS and OS in patients with lung cancer. The SUVs from 18 F-alfatide PET/CT imaging represent the expression of integrin αvβ3: the higher the expression is, the higher the malignant degree of the tumor and the worse the prognosis. In this study, it was found that the PFS and OS of patients with lung cancer in stages I-II were better than those in stages III-IV. TNM staging is recognized as one of the useful factors for predicting tumor survival 13 , 18 . Clinical studies have confirmed that 18 F-alfatide II PET/CT offers good differentiation and imaging of lung cancer 5 , 13 , breast cancer 19 , esophageal cancer 17 , glioblastoma 20 , brain metastases 21 , and other diseases. The sensitivity, specificity, and accuracy of 18 F-alfatide PET/CT in the diagnosis of lymph node metastasis of NSCLC were 92.7%, 95.7% and 95.4%, respectively 16 . 18 F-alfatide II PET/CT is superior to 18 F-FDG PET/CT in the detection of skeletal and bone marrow metastases, with nearly 100% sensitivity for osteolytic, mixed and bone marrow lesions 22 . The above studies show that 18 F-alfatide II PET/CT is capable of accurately measuring TNM of lung cancer. This study has several limitations in addition to the relatively small subgroup sample sizes. First, it was a single-center study. In addition, 18 F-alfatide II PET/CT imaging was performed only once in patients with lung cancer before treatment, but not during or after treatment. The idea that changes of SUVs in 18 F-alfatide II PET/CT are related to prognosis is a proposition worth exploring. Nevertheless, these shortcomings diminish neither the potential of our findings nor the importance of dedicated prospective investigations to corroborate these findings. Conclusion In this prospectively study, it was confirmed that the high uptake of SUV LN in 18 F-alfatide II PET/CT predicted poor PFS and OS in patients with lung cancer. This threshold could serve as a selection criterion for a new subgroup of lung cancer patients with poor prognosis. Abbreviations 18 F-Alfatide: 18 F-ALF-NOTA-PRGD2; PET: positron emission tomography; CT:computed tomography; ATR: antitumor therapy; NSCLC: non-small-cell lung cancer; SUV P : maximum standardized uptake values for primary tumor; SUV LN : maximum standardized uptake values for metastatic lymph node; TBR P : primary tumor to blood pool; TBR LN : metastatic lymph node to blood pool; TBR P-LN : metastatic lymph node to primary tumor; SUV blood : mean uptake values of the blood pool; OS: overall survival; PFS: progression-free survival; TNM: tumor node metastasis; ROC: Receiver operating curve; FDG: fluorodeoxyglucose; IMRT: intensity-modulated radiotherapy technique; 3D-CRT: three-dimensional conformal radiotherapy technique; EGFR-TKI: epidermal growth factor receptor-tyrosine kinase inhibitor. Declarations Ethics approval and consent to participate This prospective study was approved by the local ethics committee of Shandong Cancer Hospital and Institute, and each patient gave written informed consent before the study. Consent for publication All the personal data involved in this article have been signed with informed consent. Availability of data and material The datasets used and/or analyzed during the current study are available form the corresponding author on reasonable request. Competing interests: None. Funding This study was partially funded by Natural Science Foundation of China (NSFC81872475, NSFC81372413), Shandong Key Research and Development Plan (2017CXGC1209 and 2017GSF18164) and the Outstanding Youth Natural Science Foundation of Shandong Province (JQ201423), Jinan Clinical Medicine Science and Technology Innovation Plan (201704095), National Key Research and Development Program of China (2016YFC0904700). Author contributions Shuanghu Yuan and Yongzheng Wang: Conceptualization, Methodology. Yuchun Wei: Data curation, Writing-Original draft preparation. Li Ma and Jinsong Zheng: Visualization, Investigation, Software. Yanqing Pei: Statistical analysis, Xueting Qin: Follow-up care. Xiaohui Luan and Yue Zhou: Case collection and supervision. Acknowledgments The authors thank AiMi Academic Services (www.aimieditor.com) for English language editing and review services. References Siegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68:7–30. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018;68:394–424. Travis WD, Brambilla E, Nicholson AG, Yatabe Y, JHM A, Beasley MB, et al. 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Relationship Between Clinicopathological Characteristics and PET/CT Uptake in Esophageal Squamous Cell Carcinoma: [18F]Alfatide versus [18F]FDG. Mol Imaging Biol. 2019;21:175–82. Chansky K, Sculier JP, Crowley JJ, Giroux D, Van Meerbeeck J, Goldstraw P. The International Association for the Study of Lung Cancer Staging Project: prognostic factors and pathologic TNM stage in surgically managed non-small cell lung cancer. J Thorac Oncol. 2009;4:792–801. Wu J, Wang S, Zhang X, Teng Z, Wang J, Yung BC, et al. 18F-Alfatide II PET/CT for Identification of Breast Cancer: A Preliminary Clinical Study. J Nucl Med. 2018;59:1809–16. Zhang H, Liu N, Gao S, Hu X, Zhao W, Tao R, et al. Can an ¹â࿽¸F-ALF-NOTA-PRGD2 PET/CT Scan Predict Treatment Sensitivity to Concurrent Chemoradiotherapy in Patients with Newly Diagnosed Glioblastoma. J Nucl Med. 2016;57:524–9. Yu C, Pan D, Mi B, Xu Y, Lang L, Niu G, et al. (18)F-Alfatide II PET/CT in healthy human volunteers and patients with brain metastases. Eur J Nucl Med Mol Imaging. 2015;42:2021–8. Mi B, Yu C, Pan D, Yang M, Wan W, Niu G, et al. Pilot Prospective Evaluation of (18)F-Alfatide II for Detection of Skeletal Metastases. Theranostics. 2015;5:1115–21. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-125389","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Original research","associatedPublications":[],"authors":[{"id":6172337,"identity":"6a84211b-c166-4e9d-9075-4629bf3223b3","order_by":0,"name":"Yuchun Wei","email":"","orcid":"","institution":"Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuchun","middleName":"","lastName":"Wei","suffix":""},{"id":6172338,"identity":"c2e38ffc-7cca-4094-961c-676decc79103","order_by":1,"name":"Li Ma","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Ma","suffix":""},{"id":6172339,"identity":"d26905b4-f26d-41ee-aeca-f6d4517f5a22","order_by":2,"name":"Jinsong Zheng","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jinsong","middleName":"","lastName":"Zheng","suffix":""},{"id":6172340,"identity":"e2c6bc7b-8682-4e9f-aa0e-7d4945978c83","order_by":3,"name":"Yanqing Pei","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yanqing","middleName":"","lastName":"Pei","suffix":""},{"id":6172341,"identity":"7cd86215-061b-4860-a9a8-710d170faa70","order_by":4,"name":"Xueting Qin","email":"","orcid":"","institution":"Shandong Cancer Hospital and Institute","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xueting","middleName":"","lastName":"Qin","suffix":""},{"id":6172342,"identity":"b6e04740-9f23-4004-bd82-01b9a0840eae","order_by":5,"name":"Xiaohui Luan","email":"","orcid":"","institution":"Dezhou People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Luan","suffix":""},{"id":6172343,"identity":"6fe1312f-7382-4288-83dc-542a6b6c27c1","order_by":6,"name":"Yue Zhou","email":"","orcid":"","institution":"Shanghe People's Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yue","middleName":"","lastName":"Zhou","suffix":""},{"id":6172344,"identity":"f4eaae65-86fc-498d-8ca4-4a64761df697","order_by":7,"name":"Yongzheng Wang","email":"","orcid":"","institution":"The Second Hospital of Shandong University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yongzheng","middleName":"","lastName":"Wang","suffix":""},{"id":6172345,"identity":"8dbfaf5c-04c8-4a1c-96de-f6768e2d636d","order_by":8,"name":"Shuanghu Yuan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYLACxgYQycPA8IFBgkQtjDNI1sLMQ4xqgxvJzx7+3HFY3px/7TFpmz8WefwNzA8f3cCrJc3cmPfMYcOdM96lSee2SRRLHGAzNs7BqyXBTJqx7TDjhhtnzKRzGyQSGw7wsEnj15L+TfJn22F7sBaLPxKJ8wlryTGT4G07nLjhfI+ZNAObROIGQlokz7wpk+ZtS0/ecIPH2LK3TSJx42ECfuE7nr4N6DBr2w3nzxje+PGnLnHe8eaHj/FpUTgAY0kkQBnMeJSDgHwDjMV/ALeqUTAKRsEoGNkAAJEbUNb7GLhrAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0002-3857-2800","institution":"Shandong Cancer Hospital affiliated to Shandong University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Shuanghu","middleName":"","lastName":"Yuan","suffix":""}],"badges":[],"createdAt":"2020-12-09 21:44:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-125389/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-125389/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4206356,"identity":"a3fbf52f-dd1d-4abb-8573-dc973138d7c0","added_by":"auto","created_at":"2020-12-11 20:19:26","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242166,"visible":true,"origin":"","legend":"ROC curve analysis of the TNM stage and SUVLN, applying survival as the dichotomous characteristic, revealing a significant area under the curve of 0.93 (P\u003c0.001) for SUVLN and 0.96 (P\u003c0.001) for stage. ","description":"","filename":"Figure1ROCcurve.jpg","url":"https://assets-eu.researchsquare.com/files/rs-125389/v1/0ce8bb2373f469b92226edba.jpg"},{"id":4206352,"identity":"6c523b19-39cf-4a1f-bdc2-268ba9267c96","added_by":"auto","created_at":"2020-12-11 20:18:56","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":242166,"visible":true,"origin":"","legend":"ROC curve analysis of the TNM stage and SUVLN, applying survival as the dichotomous characteristic, revealing a significant area under the curve of 0.93 (P\u003c0.001) for SUVLN and 0.96 (P\u003c0.001) for stage. ","description":"","filename":"Figure1ROCcurve.jpg","url":"https://assets-eu.researchsquare.com/files/rs-125389/v1/501d6c7e8b3a9f3b9b19b2c5.jpg"},{"id":4206357,"identity":"00c99d08-c138-49de-8569-7351602d2bc7","added_by":"auto","created_at":"2020-12-11 20:19:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56139,"visible":true,"origin":"","legend":"A shows that patents with a higher SUVLN (\u003e2.50) had a worse PFS of 12.35±12.90 months, whereas that for patients with a lower SUVLN was 34.41±17.02 months (P\u003c0.001). \nB shows that patents at a higher stage (≥stage III) had a PFS of 16.30±15.03 months, whereas patients at a lower stage (≤stage II) was 36.02±18.24 months (P\u003c0.001). \nC shows that patients with a higher SUVLN had a worse OS of 22.88±15.71 months, whereas patients with a lower SUVLN survived 41.91±11.10 months (P\u003c0.001).\nD shows that patients at a higher stage (≥stage III) had an OS of 27.33±15.99 months, whereas patients at a lower stage (≤stage II) survived 41.26±14.03 months (P=0.002).\n","description":"","filename":"renamed0bfd7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-125389/v1/d846dba5b176c79f826df6b3.jpg"},{"id":4206353,"identity":"f164a6e1-d6ee-4193-becf-cdb2ae50b226","added_by":"auto","created_at":"2020-12-11 20:18:56","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":56139,"visible":true,"origin":"","legend":"A shows that patents with a higher SUVLN (\u003e2.50) had a worse PFS of 12.35±12.90 months, whereas that for patients with a lower SUVLN was 34.41±17.02 months (P\u003c0.001). \nB shows that patents at a higher stage (≥stage III) had a PFS of 16.30±15.03 months, whereas patients at a lower stage (≤stage II) was 36.02±18.24 months (P\u003c0.001). \nC shows that patients with a higher SUVLN had a worse OS of 22.88±15.71 months, whereas patients with a lower SUVLN survived 41.91±11.10 months (P\u003c0.001).\nD shows that patients at a higher stage (≥stage III) had an OS of 27.33±15.99 months, whereas patients at a lower stage (≤stage II) survived 41.26±14.03 months (P=0.002).\n","description":"","filename":"renamed0bfd7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-125389/v1/4b187868951e6c166708f93c.jpg"},{"id":13633132,"identity":"43a88512-5861-411b-9051-c2c3aaf9b8c7","added_by":"auto","created_at":"2021-09-17 08:27:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":596663,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-125389/v1/c0c8a58d-051d-45fc-bd1a-cb74b8fb6486.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eLymph Node Maximum Uptake of 18F-ALF-NOTA-PRGD2 II PET/CT Predicts Lung Cancer Survival\u003c/p\u003e","fulltext":[{"header":"1. Background","content":" \u003cp\u003eLung cancer remains the leading cause of cancer incidence and mortality worldwide \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, with 2.1\u0026nbsp;million new lung cancer cases and 1.8\u0026nbsp;million deaths predicted in 2018, representing close to 1 in 5 (18.4%) cancer deaths \u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The treatment methods are surgery, radiotherapy, chemotherapy, targeting and so on; the therapeutic effect is poor, and there is a significant difference in outcomes \u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, the main reason for which is the widespread heterogeneity of tumors. The functional molecular imaging of positron emission tomography (PET) can be used to detect the internal characteristics of the whole tumor, such as glucose metabolism, angiogenesis, and hypoxia. With the development of individualized functional metabolic imaging, molecular imaging techniques are promising to predict the prognosis of lung cancer.\u003c/p\u003e \u003cp\u003eArginine-glycine-aspartic acid peptide (Arg-Gly-Asp, RGD) enables a new kind of positron drug, which is approved for clinical trials and can safely \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e and effectively image the angiogenesis of non-small-cell lung cancer (NSCLC) \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e with clarity and desirable image contrast. Tumor angiogenesis plays an important role in regulating growth, local invasiveness, and metastatic potential \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Previously, we performed a pilot clinical study that demonstrated the feasibility of using \u003csup\u003e18\u003c/sup\u003eF-ALF-NOTA-PRGD2 II (denoted \u003csup\u003e18\u003c/sup\u003eF-alfatide II) PET/computed tomography (CT) to predict the short-term outcome of concurrent chemoradiotherapy in patients with advanced NSCLC \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, there are few reports on whether \u003csup\u003e18\u003c/sup\u003eF-alfatide II can predict the long-term survival of lung cancer.\u003c/p\u003e \u003cp\u003eIn the present study, we analyze standard uptake values (SUVs) of \u003csup\u003e18\u003c/sup\u003eF-alfatide II on PET/CT before antitumor therapy (ATR) and explore its predictive value in overall survival (OS) and progression-free survival (PFS) of patients with lung cancer.\u003c/p\u003e "},{"header":"2. Material And Methods","content":" \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Patients\u003c/h2\u003e \u003cp\u003eBetween June 10, 2015, and Dec 28, 2016, a total of sixty-two patients with pathologically confirmed lung cancer were enrolled in the study. This prospective study was approved by the local ethics committee of Shandong Cancer Hospital and Institute, and each patient gave written informed consent before the study. All patients were treated in Shandong Cancer Hospital and satisfied the following criteria: ① diagnosed by histological and imaging examination such as CT or \u003csup\u003e18\u003c/sup\u003eF-fluorodeoxyglucose (FDG) PET/CT; ② an Eastern Cooperative Oncology Group (ECOG) score of \u0026le;\u0026thinsp;1; ③ clearly measurable metastatic lymph nodes and primary tumors; and ④ no ATR before the \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT scan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Radiotracer preparation\u003c/h2\u003e \u003cp\u003eA simple lyophilization kit for labeling PRGD2 peptide was purchased from the Jiangsu Institute of Nuclear Medicine, and the synthesis process was carried out by reference to the previous study \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. The radiochemical purity of the \u003csup\u003e18\u003c/sup\u003eF-alfatide exceeded 99%, and its specific radioactivity exceeded 37\u0026nbsp;GBq (1,000\u0026nbsp;mCi)/\u0026micro;mol.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 PET/CT scanning\u003c/h2\u003e \u003cp\u003ePatients were given an intravenous injection of 4.81\u0026nbsp;MBq/kg (0.12\u0026nbsp;mCi/kg) \u003csup\u003e18\u003c/sup\u003eF-alfatide II and allowed to rest for approximately 60 minutes. Patients were not requested to fast but were requested to specify their recent diet to allow estimation of blood glucose levels. Scanning was performed with an integrated inline PET/CT system (GEMINI TF Big Bore; Philips Healthcare). PET images were acquired from the head to the thigh, and the spiral CT component was obtained with an X-ray tube voltage peak of 120\u0026nbsp;kV, 300 mAs. A full-ring dedicated PET scan of the same axial range followed. The patients exhibited normal shallow respiration during image acquisition. The images were attenuation-corrected with the transmission data from CT. The attenuation-corrected PET images, CT images, and fused PET/CT images, displayed as coronal, sagittal, and transaxial slices, were viewed on a MEDEX workstation (Beijing, China).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Image analysis\u003c/h2\u003e \u003cp\u003eTwo experienced nuclear medicine physicians assessed the \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT images visually, referring to the PET fusion and CT images, until consensus was reached. The acquired \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT data were transferred into a workstation in the DICOM format. The radiotracer concentration in the region of interest (ROI) was normalized to the injected dose per kilogram of the patients\u0026rsquo; body weight to derive the standardized uptake values (SUVs). PET/CT parameters such as the maximum uptake values for the primary tumor (SUV\u003csub\u003eP\u003c/sub\u003e) or metastatic lymph node (SUV\u003csub\u003eLN\u003c/sub\u003e) and the mean SUVs for the mediastinal blood pool (SUV\u003csub\u003eblood\u003c/sub\u003e) were generated using a vendor-provided automated contouring program.\u003c/p\u003e \u003cp\u003eIn addition, tumor-to-background ratios (TBRs) were calculated. Then, the SUV ratios of the primary tumor to blood pool, metastatic lymph node to blood pool, and primary tumor to metastatic lymph node were calculated and are denoted TBR\u003csub\u003eP\u003c/sub\u003e, TBR\u003csub\u003eLN\u003c/sub\u003e, and TBR\u003csub\u003eP\u0026minus;LN\u003c/sub\u003e, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Antitumor therapy\u003c/h2\u003e \u003cp\u003eSurgery is the first choice for patients who can be surgically resected. Patients without surgical indications or who are unable to tolerate surgery should choose comprehensive treatment based on radiotherapy and chemotherapy. The chemotherapy scheme is a platinum-based dual-drug.\u003c/p\u003e \u003cp\u003eAn intensity-modulated radiotherapy technique (IMRT) or a three-dimensional conformal radiotherapy technique (3D-CRT) was delivered to patients with megavoltage equipment (6 MV). Radiotherapy was given as the conventionally fractionated regimen, 180\u0026nbsp;cGy to 200\u0026nbsp;cGy for five days per week, and the total dose administered to patients ranged from 5040\u0026nbsp;cGy to 6600\u0026nbsp;cGy (median dose, 6000\u0026nbsp;cGy).\u003c/p\u003e \u003cp\u003eThe pathological type of adenocarcinoma is routine gene detection, and patients with targeted treatment can choose epidermal growth factor receptor-tyrosine kinase inhibitor (EGFR-TKI) drug therapy.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 End points and assessments\u003c/h2\u003e \u003cp\u003eThe two primary end points were PFS (as assessed by investigators according to RECIST criteria) and OS in all patients with lung cancer. Patients were followed up by enhanced CT every 6 weeks during treatment, every 2\u0026nbsp;months in the first year after treatment, and every six months from the second year after treatment. The OS time was from the date of diagnosis to the date of follow-up or death, and the date of PFS was from the date of diagnosis to the date of tumor recurrence or progression.\u003c/p\u003e \u003cp\u003eGeneral case data that might have affected the prognosis of the patients were recorded, including the sex, age, pathological type, and TNM stage (clinical stage or postoperative stage).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis was performed using IBM SPSS Statistics for Windows version 20.0 (IBM, Armonk, USA). The Pearson test was used for continuous variables in correlation analysis, and the Spearman test was used for classified variables. Data derived from SUV measurements were analyzed for correlation with survival using receiver operating characteristic (ROC) curve analysis and the Youden index for independencies using the χ\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e test. The PFS and OS were assessed by Kaplan-Meier analysis. Cox regression proportional hazards models were used to obtain hazard ratio estimates of significant parameters derived from univariate analysis using \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026le;\u0026thinsp;0.1 for the parameters to qualify for multivariate analysis. All tests were 2-sided, and \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e "},{"header":"3. Results","content":" \u003cp\u003eOf the sixty-two patients (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), 6 cases were lost to follow-up, including 2 cases (1 of stage III and 1 of stage IV) of adenocarcinoma and 4 cases (2 of stage II and 2 of stage III) of squamous cell carcinoma. As of Dec 31, 2019, the median follow-up was 31 months (range 1.30\u0026thinsp;~\u0026thinsp;57 months), of which 55.36% (31/56) of the patients had died. The median PFS and OS were 21.30 (range 2.0\u0026thinsp;~\u0026thinsp;56.0) months and 40.0 (range 3.50\u0026thinsp;~\u0026thinsp;57.0) months, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian age, years (range)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e59.5 (24\u0026ndash;84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46 (74.19)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16 (25.81)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmall-cell lung cancer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7 (11.29)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdenocarcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (38.71)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSquamous cell carcinoma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (40.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNSCLC not otherwise specified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (9.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage I\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage II\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13 (20.97)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage III\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33 (53.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage IV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8 (12.90)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePretreatment SUVs on PET/CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.18\u0026thinsp;\u0026plusmn;\u0026thinsp;2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003eLN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.98\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eP\u0026minus;LN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.15\u0026thinsp;\u0026plusmn;\u0026thinsp;1.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eLN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.92\u0026thinsp;\u0026plusmn;\u0026thinsp;2.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.67\u0026thinsp;\u0026plusmn;\u0026thinsp;3.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eNSCLC\u003c/em\u003e, non-small-cell lung cancer; \u003cem\u003eSUV\u003c/em\u003e\u003csub\u003e\u003cem\u003eP\u003c/em\u003e\u003c/sub\u003e, maximum standardized uptake values for primary tumor; \u003cem\u003eSUV\u003c/em\u003e\u003csub\u003e\u003cem\u003eLN\u003c/em\u003e\u003c/sub\u003e, maximum standardized uptake values for metastatic lymph node; \u003cem\u003eTBR\u003c/em\u003e\u003csub\u003e\u003cem\u003eP\u003c/em\u003e\u003c/sub\u003e, primary tumor to blood pool; \u003cem\u003eTBR\u003c/em\u003e\u003csub\u003e\u003cem\u003eLN\u003c/em\u003e\u003c/sub\u003e, metastatic lymph node to blood pool; \u003cem\u003eTBR\u003c/em\u003e\u003csub\u003e\u003cem\u003eP\u0026minus;LN\u003c/em\u003e\u003c/sub\u003e, metastatic lymph node to primary tumor.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the results of univariable and multivariable linear regression analyses performed to determine which tracked parameters are potential predictors of PFS and OS. Following multivariable analysis, two parameters remained significantly associated with PFS: SUV\u003csub\u003eLN\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, HR 1.44, 95% CI 1.17\u0026thinsp;~\u0026thinsp;1.77) and stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026, HR 1.77, 95% CI 1.07\u0026thinsp;~\u0026thinsp;2.93); the same applied to OS: SUV\u003csub\u003eLN\u003c/sub\u003e (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, HR 1.43, 95% CI 1.15\u0026thinsp;~\u0026thinsp;1.78) and stage (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048, HR 1.66, 95% CI 1.0\u0026thinsp;~\u0026thinsp;2.76). Of note, differences in sex, histology, SUV\u003csub\u003eP\u003c/sub\u003e, TBR\u003csub\u003eLN\u003c/sub\u003e and TBR\u003csub\u003eP\u003c/sub\u003e were significant by univariable assessment but did not retain significance following multivariable analysis. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the correlation between different factors with PFS and OS. SUV\u003csub\u003eLN\u003c/sub\u003e and stage were negatively correlated with OS and PFS, all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate and multivariate COX regression associating baseline variables and SUVs with PFS and OS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c12\" namest=\"c8\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003eP Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.534 (1.232\u0026thinsp;~\u0026thinsp;10.141)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3.594 (1.250\u0026thinsp;~\u0026thinsp;10.335)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.015 (0.983\u0026thinsp;~\u0026thinsp;1.048)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.361\u0026sect;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.012 (0.978\u0026thinsp;~\u0026thinsp;1.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.494\u0026sect;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.597 (0.368\u0026thinsp;~\u0026thinsp;0.968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.504 (0.301\u0026thinsp;~\u0026thinsp;0.844)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.105 (1.359\u0026thinsp;~\u0026thinsp;3.259)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.770 (1.071\u0026thinsp;~\u0026thinsp;2.926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.026*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.099 (1.343\u0026thinsp;~\u0026thinsp;3.280)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.664 (1.004\u0026thinsp;~\u0026thinsp;2.760)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.048*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.168 (1.040\u0026thinsp;~\u0026thinsp;1.313)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.174 (1.042\u0026thinsp;~\u0026thinsp;1.322)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003eLN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.551 (1.281\u0026thinsp;~\u0026thinsp;1.877)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.441 (1.171\u0026thinsp;~\u0026thinsp;1.772)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.562 (1.282\u0026thinsp;~\u0026thinsp;1.902)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.431 (1.152\u0026thinsp;~\u0026thinsp;1.777)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.001*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eP\u0026minus;LN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.750 (0.542\u0026thinsp;~\u0026thinsp;1.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.748 (0.529\u0026thinsp;~\u0026thinsp;1.056)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eLN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.333 (1.150\u0026thinsp;~\u0026thinsp;1.546)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.318 (1.138\u0026thinsp;~\u0026thinsp;1.527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.519\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.134 (1.031\u0026thinsp;~\u0026thinsp;1.248)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.140 (1.034\u0026thinsp;~\u0026thinsp;1.257)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eND\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"12\" nameend=\"c12\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePFS\u003c/em\u003e, progression-free survival; \u003cem\u003eOS\u003c/em\u003e, overall survival; \u003cem\u003eHR\u003c/em\u003e, hazard ratio; \u003cem\u003eCI\u003c/em\u003e, confidence interval.\u003c/p\u003e \u003cp\u003eStatistical method: Forward, LR, Cox proportional-hazards model. \u0026sect;Findings with P\u0026thinsp;\u0026gt;\u0026thinsp;0.10 are not included in the multivariate Cox regression analysis. ND, not displayed; *Significant result.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of correlation between general parameters with OS/PFS and survival risk\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003ePFS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eOS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003er\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.402\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHistology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSUV\u003csub\u003eLN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eP\u0026minus;LN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eLN\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTBR\u003csub\u003eP\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.357\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe ROC curve analysis of the respective parameters, applying survival as the dichotomous characteristic, revealed a significant area under the curve of 0.93 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) for SUV\u003csub\u003eLN\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At a cutoff value of 2.50, derived by the Youden index, the sensitivity, specificity, and positive and negative prediction were 77.42%, 80.0%, and 82.76% and 74.07%, respectively. A significant area under the curve of 0.96 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) was found for stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). At a cutoff value of II, derived by the Youden index, the sensitivity, specificity, and positive and negative prediction were 87.10%, 60.0%, and 72.97% and 78.95%, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe corresponding Kaplan-Meier curves are given in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Patents with a higher SUV\u003csub\u003eLN\u003c/sub\u003e (\u0026gt;\u0026thinsp;2.50) had a PFS of 12.35\u0026thinsp;\u0026plusmn;\u0026thinsp;12.90 months, whereas that for patients with a lower SUV\u003csub\u003eLN\u003c/sub\u003e was 34.41\u0026thinsp;\u0026plusmn;\u0026thinsp;17.02 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with a higher SUV\u003csub\u003eLN\u003c/sub\u003e had an OS of 22.88\u0026thinsp;\u0026plusmn;\u0026thinsp;15.71 months, whereas patients with a lower SUV\u003csub\u003eLN\u003c/sub\u003e survived 41.91\u0026thinsp;\u0026plusmn;\u0026thinsp;11.10 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patents with a higher stage (\u0026ge;\u0026thinsp;stage III) had a PFS of 16.30\u0026thinsp;\u0026plusmn;\u0026thinsp;15.03 months, whereas that for patients with a lower stage (\u0026le;\u0026thinsp;stage II) was 36.02\u0026thinsp;\u0026plusmn;\u0026thinsp;18.24 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Patients with a higher stage (\u0026ge;\u0026thinsp;stage III) had an OS of 27.33\u0026thinsp;\u0026plusmn;\u0026thinsp;15.99 months, whereas patients with a lower stage (\u0026le;\u0026thinsp;stage II) survived 41.26\u0026thinsp;\u0026plusmn;\u0026thinsp;14.03 months (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e "},{"header":"4. Discussion","content":" \u003cp\u003eDue to the existence of heterogeneity, the prognosis of lung cancer varies greatly, so it is very important to screen relevant prognostic indicators. With the advancement of image analysis tools, tumor metabolic characteristics can now be assessed rapidly and consistently with no interobserver variability, with the potential for routine assessment in clinical practice. Various molecular imaging techniques have been developed to predict the tumor response to therapy, such as FDG PET \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, \u003csup\u003e18\u003c/sup\u003eF-fluorothymidine (FLT) PET \u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, \u003csup\u003e18\u003c/sup\u003eF-fluroerythronitroimidazole (FETNIM) PET \u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e and \u003csup\u003e18\u003c/sup\u003eF-fluoromisonidazole (FMISO) PET \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eStudies on the capabilities of \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT have increased in recent years and have shown the advantages of this imaging technique for evaluating chest tumors due to the high in vivo TBR identified in PET imaging \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. In this study, sex, histology, stage, SUV\u003csub\u003eP\u003c/sub\u003e, SUV\u003csub\u003eLN\u003c/sub\u003e, TBR\u003csub\u003eP\u0026minus;LN\u003c/sub\u003e, TBR\u003csub\u003eLN\u003c/sub\u003e and TBR\u003csub\u003eP\u003c/sub\u003e were significantly associated with PFS and OS in the correlation analysis, and SUV\u003csub\u003eLN\u003c/sub\u003e before treatment in \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT and TNM staging were revealed to independently predict PFS and OS of lung cancer through multivariate Cox regression analysis.\u003c/p\u003e \u003cp\u003eWhy is \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT useful in predicting survival in patients with lung cancer? \u003csup\u003e18\u003c/sup\u003eF-alfatide II can bind to integrin αvβ3, which is upregulated in the activated endothelial cells with tumor angiogenesis, with high affinity and specificity. Li et al. reported that \u003csup\u003e18\u003c/sup\u003eF-alfatide II uptake on PET/CT can predict the response to antiangiogenic therapy, with higher \u003csup\u003e18\u003c/sup\u003eF-alfatide II uptake in tumors predicting a better response to apatinib therapy in a variety of tumors \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Luan X et al. found that SUV\u003csub\u003eP\u003c/sub\u003e and tumor-to-blood ratios can predict the short-term outcome of concurrent chemoradiotherapy (CCRT) in patients with advanced NSCLC. Patients with lower SUV\u003csub\u003eP\u003c/sub\u003e and tumor-to-blood ratios responded to CCRT (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLymph metastasis is a well-characterized negative factor affecting survival in cancer patients and reducing tumor staging. Wu C et al. suggested that tumors often drive inflammation both in primary tumor tissue and in tumor-draining lymph nodes, and the inflamed tissue can also show high uptake of \u003csup\u003e18\u003c/sup\u003eF-FDG and \u003csup\u003e18\u003c/sup\u003eF-alfatide II \u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Chen et al. found that RGD PET provides better imaging of mediastinal lymph nodes and contralateral metastases than \u003csup\u003e18\u003c/sup\u003eF-FDG by providing better imaging \u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Studies have also indicated that SUV\u003csub\u003eLN\u003c/sub\u003e both in \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT and in \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT is influenced by the pathological stage, lymph node states, and tumor differentiation and that it may serve as a useful new parameter for risk stratification with esophageal squamous cell carcinoma \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. In this study, we found that SUV\u003csub\u003eLN\u003c/sub\u003e not only is significantly negative associated with PFS and OS but also may be an independent predictor for PFS and OS in patients with lung cancer. The SUVs from \u003csup\u003e18\u003c/sup\u003eF-alfatide PET/CT imaging represent the expression of integrin αvβ3: the higher the expression is, the higher the malignant degree of the tumor and the worse the prognosis.\u003c/p\u003e \u003cp\u003eIn this study, it was found that the PFS and OS of patients with lung cancer in stages I-II were better than those in stages III-IV. TNM staging is recognized as one of the useful factors for predicting tumor survival \u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Clinical studies have confirmed that \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT offers good differentiation and imaging of lung cancer \u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, breast cancer \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, esophageal cancer \u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, glioblastoma \u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, brain metastases \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, and other diseases. The sensitivity, specificity, and accuracy of \u003csup\u003e18\u003c/sup\u003eF-alfatide PET/CT in the diagnosis of lymph node metastasis of NSCLC were 92.7%, 95.7% and 95.4%, respectively \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT is superior to \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in the detection of skeletal and bone marrow metastases, with nearly 100% sensitivity for osteolytic, mixed and bone marrow lesions \u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. The above studies show that \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT is capable of accurately measuring TNM of lung cancer.\u003c/p\u003e \u003cp\u003eThis study has several limitations in addition to the relatively small subgroup sample sizes. First, it was a single-center study. In addition, \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT imaging was performed only once in patients with lung cancer before treatment, but not during or after treatment. The idea that changes of SUVs in \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT are related to prognosis is a proposition worth exploring. Nevertheless, these shortcomings diminish neither the potential of our findings nor the importance of dedicated prospective investigations to corroborate these findings.\u003c/p\u003e "},{"header":"Conclusion","content":" \u003c/p\u003e \u003cp\u003eIn this prospectively study, it was confirmed that the high uptake of SUV\u003csub\u003eLN\u003c/sub\u003e in \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT predicted poor PFS and OS in patients with lung cancer. This threshold could serve as a selection criterion for a new subgroup of lung cancer patients with poor prognosis.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003e\u003csup\u003e18\u003c/sup\u003eF-Alfatide: \u003csup\u003e18\u003c/sup\u003eF-ALF-NOTA-PRGD2; PET: positron emission tomography; CT:computed tomography; ATR: antitumor therapy; NSCLC: non-small-cell lung cancer; SUV\u003csub\u003eP\u003c/sub\u003e: maximum standardized uptake values for primary tumor; SUV\u003csub\u003eLN\u003c/sub\u003e: maximum standardized uptake values for metastatic lymph node; TBR\u003csub\u003eP\u003c/sub\u003e: primary tumor to blood pool; TBR\u003csub\u003eLN\u003c/sub\u003e: metastatic lymph node to blood pool; TBR\u003csub\u003eP-LN\u003c/sub\u003e: metastatic lymph node to primary tumor; SUV\u003csub\u003eblood\u003c/sub\u003e: mean uptake values of the blood pool; OS: overall survival; PFS: progression-free survival; TNM: tumor node metastasis; ROC: Receiver operating curve; FDG: fluorodeoxyglucose; IMRT: intensity-modulated radiotherapy technique; 3D-CRT: three-dimensional conformal radiotherapy technique; EGFR-TKI: epidermal growth factor receptor-tyrosine kinase inhibitor.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis prospective study was approved by the local ethics committee of Shandong Cancer Hospital and Institute, and each patient gave written informed consent before the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the personal data involved in this article have been signed with informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available form the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was partially funded by Natural Science Foundation of China (NSFC81872475, NSFC81372413), Shandong Key Research and Development Plan (2017CXGC1209 and 2017GSF18164) and the Outstanding Youth Natural Science Foundation of Shandong Province (JQ201423), Jinan Clinical Medicine Science and Technology Innovation Plan (201704095), National Key Research and Development Program of China (2016YFC0904700).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eShuanghu Yuan and Yongzheng Wang: Conceptualization, Methodology. Yuchun Wei: Data curation, Writing-Original draft preparation. Li Ma and Jinsong Zheng: Visualization, Investigation, Software. Yanqing Pei: Statistical analysis, Xueting Qin: Follow-up care. Xiaohui Luan and Yue Zhou: Case collection and supervision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank AiMi Academic Services (www.aimieditor.com) for English language editing and review services.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2018. CA Cancer J Clin. 2018;68:7\u0026ndash;30.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. 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Int J Radiat Oncol Biol Phys. 2004;59:971\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAskoxylakis V, Dinkel J, Eichinger M, Stieltjes B, Sommer G, Strauss LG, et al. Multimodal hypoxia imaging and intensity modulated radiation therapy for unresectable non-small-cell lung cancer: the HIL trial. Radiat Oncol. 2012;7:157.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDu X, Zhang Y, Chen L, Mi B, You Q, Xu Y, et al. Comparing the Differential Diagnostic Values of 18F-Alfatide II PET/CT between Tuberculosis and Lung Cancer Patients. Contrast Media Mol Imaging. 2018; 2018: 8194678.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi L, Ma L, Shang D, Liu Z, Yu Q, Wang S, et al. Pretreatment PET/CT imaging of angiogenesis based on 18F-RGD tracer uptake may predict antiangiogenic response. Eur J Nucl Med Mol Imaging. 2019;46:940\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, Sievers E, Hou Y, Park R, Tohme M, Bart R, et al. Integrin alpha v beta 3-targeted imaging of lung cancer. Neoplasia. 2005;7:271\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Gao S, Huang Y, Zheng J, Dong Y, Zhang B, et al. A Pilot Study of 18F-Alfatide PET/CT Imaging for Detecting Lymph Node Metastases in Patients with Non-Small Cell Lung Cancer. Sci Rep. 2017;7:2877.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong Y, Wei Y, Chen G, Huang Y, Song P, Liu S, et al. Relationship Between Clinicopathological Characteristics and PET/CT Uptake in Esophageal Squamous Cell Carcinoma: [18F]Alfatide versus [18F]FDG. Mol Imaging Biol. 2019;21:175\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChansky K, Sculier JP, Crowley JJ, Giroux D, Van Meerbeeck J, Goldstraw P. The International Association for the Study of Lung Cancer Staging Project: prognostic factors and pathologic TNM stage in surgically managed non-small cell lung cancer. J Thorac Oncol. 2009;4:792\u0026ndash;801.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu J, Wang S, Zhang X, Teng Z, Wang J, Yung BC, et al. 18F-Alfatide II PET/CT for Identification of Breast Cancer: A Preliminary Clinical Study. J Nucl Med. 2018;59:1809\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Liu N, Gao S, Hu X, Zhao W, Tao R, et al. Can an \u0026sup1;\u0026acirc;࿽\u0026cedil;F-ALF-NOTA-PRGD2 PET/CT Scan Predict Treatment Sensitivity to Concurrent Chemoradiotherapy in Patients with Newly Diagnosed Glioblastoma. J Nucl Med. 2016;57:524\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu C, Pan D, Mi B, Xu Y, Lang L, Niu G, et al. (18)F-Alfatide II PET/CT in healthy human volunteers and patients with brain metastases. Eur J Nucl Med Mol Imaging. 2015;42:2021\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMi B, Yu C, Pan D, Yang M, Wan W, Niu G, et al. Pilot Prospective Evaluation of (18)F-Alfatide II for Detection of Skeletal Metastases. Theranostics. 2015;5:1115\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"18F-ALF-NOTA-PRGD2 II, PET/CT, Lung cancer, Survival","lastPublishedDoi":"10.21203/rs.3.rs-125389/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-125389/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e\u003c/p\u003e\u003cp\u003eTumor angiogenesis plays a key role in tumor growth, development, and metastasis, so the exploratory study of tumor neovascularization imaging is one of the potential methods to predict survival. This study aims to examine the predictive capacity of \u003csup\u003e18\u003c/sup\u003eF-ALF-NOTA-PRGD2 II (denoted \u003csup\u003e18\u003c/sup\u003eF-Alfatide II) positron emission tomography (PET)/computed tomography (CT) before antitumor therapy (ATR) in patients with lung cancer.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eThe median follow-up was 31 (1.3~57.0) months. Among the patients, 6 were lost to follow-up. The overall survival (OS) and progression-free survival (PFS) were 40.0 (3.50~57.0) months and 21.30 (2.0~56.0) months, respectively. The maximum uptake values (SUV\u003csub\u003emax\u003c/sub\u003e) of the metastatic lymph nodes (SUV\u003csub\u003eLN\u003c/sub\u003e) and tumor node metastasis (TNM) staging were significant predictors of PFS and OS (all P\u0026lt;0.05) in a multivariate Cox regression analysis. Statistical significance was not reached by any other variable in the multivariate analysis. Receiver operating curve (ROC) analysis for survival revealed an area under the curve of 0.93 (P\u0026lt;0.001) for SUV\u003csub\u003eLN\u003c/sub\u003e and 0.96 for the TNM stage (P\u0026lt;0.001). The SUV\u003csub\u003eLN\u003c/sub\u003e and TNM stage cutoff values were 2.50 and II, and their sensitivity, specificity and positive and negative prediction were 77.42%, 80.0% and 82.76% and 74.07%; and 87.10%, 60.0% and 72.97% and 78.95%, respectively. Patients with a lower SUV\u003csub\u003eLN\u003c/sub\u003e and early stage had a longer PFS and OS (all P\u0026lt;0.05).\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003e\u003c/p\u003e\u003cp\u003eFor lung cancer, low SUV\u003csub\u003eLN\u003c/sub\u003e and an early TNM stage (≤stage II) as assessed before ATR by \u003csup\u003e18\u003c/sup\u003eF-alfatide II PET/CT represents a favorable subgroup with increased PFS and OS.\u003c/p\u003e","manuscriptTitle":"Lymph Node Maximum Uptake of 18F-ALF-NOTA-PRGD2 II PET/CT Predicts Lung Cancer Survival","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-11 20:18:55","doi":"10.21203/rs.3.rs-125389/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"068fef2f-23de-4187-9a1f-886a564ecc12","owner":[],"postedDate":"December 11th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1434122,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2021-01-06T21:43:03+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-11 20:18:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-125389","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-125389","identity":"rs-125389","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}