Quantification of Malignant Lymph Nodes and Benign Lymph Nodes in Patients of Esophageal Squamous Cell Carcinoma With Dynamic 18F-FDG PET/CT

Quantification of Malignant Lymph Nodes and Benign Lymph Nodes in Patients of Esophageal Squamous Cell Carcinoma With Dynamic 18F-FDG PET/CT | 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 Quantification of Malignant Lymph Nodes and Benign Lymph Nodes in Patients of Esophageal Squamous Cell Carcinoma With Dynamic 18F-FDG PET/CT Xiaohui Wang, Qingdong Cao, Xiaojing Wang, Ying Wang, Dan Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-127268/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 Most esophageal squamous cell carcinoma (ESCC) imaging diagnoses can be performed by routine CT and ultrasound, but it is difficult to detect metastatic lymph nodes or minor lesions. Functional imaging diagnosis based on 18 F-FDG PET/CT has potential advantages for detection of metastatic lymph nodes, or differentiation of benign from malignant lymph nodes, and for typing and staging of ESCC. The purpose of this study is to provide 18 F-FDG PET/CT imaging for ESCC patient to quantify the difference between malignant lymph nodes (MLN) and benign lymph nodes (BLN) for ESCC. Methods Dynamic 18 F-FDG PET/CT was performed in 46 patients (26 patients without MLN (N0 stage) and 20 with MLN (non-N0 stage) who were pathologically confirmed for ESCC. Visual and quantitative differences were measured in primary tumor (PT), MLN and BLN regions of interest (ROIs). Finally, 52 MLN and 133 BLN (83 from N0 stage and 50 from non-N0 stage) were included for analysis. Pharmacokinetic analysis was performed by a Patlak model using Matlab program to obtain the influx constant (K i ). Maximum standardized uptake value (SUV max ) was also determined from the static and dynamic PET/CT scans. Based on the receiver operator characteristic (ROC) curve, the sensitivity and specificity for each parameter in differentiation diagnosis were evaluated. Results K i and SUV max in PT non-N0 group was slightly higher than in N0 groups (0.04 ± 0.02 vs 0.03 ± 0.03, 8.01 ± 3.90 vs 7.08 ± 5.39, respectively), but with no significant difference (p > 0.05). And K i and SUV max in MLN were higher than BLN with statistically significant difference (K i MLN vs K i BLN ( 0.021 ± 0.014 vs 0.006 ± 0.004, p < 0.0001); (SUV max MLN vs SUV max BLN (4.35 ± 2.27 vs 1.89 ± 0.85, p < 0.0001); The sensitivity both Ki and SUV max were 80.77 %, the specificity for Ki was 89.47%, and SUV max 87.22% respectively. And the diagnostic accuracy Ki (90.61%) was slightly better than SUV max (88.16%). Conclusions  Quantitative parameters (both K i and SUV max ) of 18 F-FDG in ESCC patients are sensitive diagnostic measurements capable to identify MLNs from BLNs . Cardiothoracic Surgery ESCC MLN BLN 18F-FDG-PET/CT quantitative analysis glucose metabolic rate Figures Figure 1 Figure 1 Figure 2 Figure 2 Figure 3 Figure 3 Figure 4 Figure 4 Figure 5 Figure 5 Introduction Esophageal cancer is one of the most aggressive malignancies in the world, which accounted for an estimated 572,034 new cases and 508,585 deaths in 2018 worldwide 1 . The incidence and mortality of esophageal cancer is ranked first in China and esophageal squamous cell carcinoma (ESCC) is the main histological subtype of esophageal cancers in China 2 . Correct preoperative evaluation of whether the tumor has reached any lymph nodes is important for management. Various methods have been used to detect primary and lymph node metastases in esophageal cancer patients, including computed tomography (CT), endoscopic examinations, and endoscopic ultrasonography (EUS). However, even such advanced imaging modalities do not always reliably identify lymph node metastasis prior to surgical resection and pathological examination. The appearance of lymph nodes with morphological imaging procedures is classified by their shape, size, density and, if applied, contrast enhancement. Benign lymph nodes (BLN) usually tend to have a fatty hilum, an oval shape and frequently do not measure more than 1 cm in the short axis diameter. However, the use of size as the most important criterion for differentiation of benign and malignant lymph nodes has limitations: small metastases without an increase in lymph node size are frequently missed 3 . Positron emission tomography (PET)/computed tomography (CT) is increasingly used as a promising method, which the combination of morphological and functional imaging represents the optimal approach for lymph node staging and general staging 4 . A radioactive tracer，2-[ 18 F]fluoro-2-deoxy-dglucose ( 18 F-FDG) currently used is based on the increased glucose metabolism, which may be reported with semiquantitative standard uptake value (SUV). It was reported that PET/CT sensitivity and specificity for the detection of loco-regional metastases were moderate, but sensitivity and specificity were reasonable for distant metastases 5 . In malignancy, the uptake of 18 F-FDG continues to increase for several hours after FDG injection whereas such prolonged period of 18 F-FDG uptake is rare in inflammatory/infectious or normal tissues 6-8 . Shum et al ever assessed clinical usefulness of dual-time 18 F-FDG PET/CT in ESCC, which turned out the sensitivity of 18 F-FDG PET-CT in detecting the primary ESCC with combination of early SUV max ≥ 2.5 or retention index (RI) ≥ 10% was 96.2% 9 . However, for loco-regional lymph node detection, there was no significant difference 9 . Dynamic 18 F-FDG PET/CT allows quantitative assessment of lesion in vivo by using a Patlak model to obtain the influx constant (K i ) 10-13 . The purpose of this study is to quantify MLN and BLN in patients of ESCC with 18 F-FDG PET/CT by applying both routing scan (SUV max ) and dynamic scans (K i ). Patients And Methods Patients population Forty-six patients (36 men, 10 women; age range, 45-85 years old; mean age, 64-year-old) who were pathologically confirmed ESCC were included in this study ( Table 1 ). Exclusion criteria were diabetes mellitus, fasted glucose level ≥11.0 mmol·L -1 , breast feeding, pregnancy and claustrophobia. Conventional medical imaging for these patients were carried out with routing CT, gastroduodenoscopy. Surgical pathology results were used to provide the final diagnosis with which the 18 F-FDG PET/CT results were compared. The study was approved by the institutional review board of the Fifth Affiliated Hospital of Sun Yat-sen University (IRB protocol # ZDWY.FZYX.006). All included patients provided signed informed consent. The clinical trial registration number is NCT04514822 (http://www.clinicaltrials.gov/). Baseline clinical characteristics, including sex, age, height, weight, and tumor characteristics were obtained from electronic medical records. Imaging protocol All patients were fasted for at least 4 h and the fasted glucose level is ≤ 11.0 mmoL·L -1 . Imaging was performed with the 112-ring digital light guide PET/CT scanner (United Imaging, UMI780, Shanghai, China). The patients were fasted for at least 6 h before scans. The scan covered the region between the thoracic inlet and the lower liver margin. Each PET/CT scan began with a transmission CT scan for 5 seconds that was used for attenuation correction. After transmission CT scan (160 mA, 100 kV, pitch 0.9875, rotation time 0.5 s) for subsequent PET data attenuation correction, continual dynamic clinical PET scans were performed in a single bed position immediately after 18 F-FDG intravenously injection (210 ± 30 MBq) in list mode for 60 minutes in supine position, dynamic 48-timeframe PET/CT imaging was obtained (18 × 5 s, 6 × 10 s, 5 × 30 s, 5 × 60 s, 8 × 150 s, 6 × 300 s). Related parameters calculation Volumes of interest (VOIs) Volumes of interest (VOIs) were placed over the primary esophageal squamous cell carcinoma, metastatic, benign lymph nodes and the aorta, and 0-60 min time-activity curves (TACs) were generated for further data evaluation. A VOI consists of at least 3 regions of interest (ROIs) over the target area. Irregular ROIs were drawn manually using Carimas 2.10 software (turkupetcentre.fi/carimas/) with PET and the corresponding CT slices. To compensate for patient motion during the acquisition time, the original images were visually repositioned. The arterial blood input in this study was obtained from the left ventricle or aorta using image-derived input function as an input function as we previously reported 14 . Patlak analysis The dynamic quantitative data were analyzed with the Patlak graphical model using Matlab program (Version 2014a) followed the methods from our group and other as published previously 14,15 . To simplify the quantification protocol, the influx constant (K i ) was calculated using Patlak linear regression analysis based on linear range selected in the time period of 40 to 60 min p.i.. SUV max Standardised uptake value (SUV) of lesions on the frames at 55–60 min and 25-30 min were calculated. The SUV max was produced. PET/CT images were analyzed by two experienced nuclear medicine physicians, and the disagreement was discussed with the third expert. In 46 ESCC patients, 26 patients without MLN (N0 stage) and 20 with MLN (non-N0 stage). Finally, 52 MLN and 133 BLN (83 from N0 stage and 50 from non-N0 stage) were included for analysis. All parameters (K i and SUV max ) of each lesion were calculated. The difference of the primary tumor between N0 stage and non-N0 stage, MLN verse BLN, BLN from N0 stage and non-N0 stage were calculated respectively. The sensitivity, specificity and accuracy for each parameter in differentiating malignant and benign lymph nodes were evaluated based on the receiver operator characteristic (ROC) curve. Statistical analysis To test for differences between MLNs, BLNs and the primary tumor between N0 stage and non-N0 stage, the Mann-Whitney U test was used. Correlations were examined using the Spearman’s rank correlation test. Cutoff values for differentiation were determined using receiver-operating-characteristic curve analysis, and the area under the curve was calculated. Statistical analysis was conducted using GraphPad Prism 6 software. A two-sided p value of less than 0.05 was considered to be statistically significant. Results Parameters comparison from PTs between N0 and non-N0 stage In 46 patients, 26 patients were in N0 stage and 20 in non-N0 stage. According to the PT sites, we divided the PTs in four parts (which were cervical, upper thoracic, middle thoracic and lower thoracic & abdominal), and found middle thoracic ESCC accounted for the most, 16/26 (61.5%) in N0 verse 11/20 (55%) in non-N0 stage. All parameters were compared in two groups at different locations. Generally speaking, K i , SUV max in PT non-N0 group was slightly higher than in N0 groups (0.04 ± 0.02 vs 0.03 ± 0.03, 8.01 ± 3.90 vs 7.08 ± 5.39, respectively), but with no significant difference (p > 0.05) ( Table S1 , Fig. 2 ). Parameters comparison between MLNs and BLNs Although the primary tumors were quantified from our PET/CT analysis with no significant difference, we further analyzed the MLN and BLN. Based on our analysis, both K i and SUV max in MLNs were higher than BLNs with statistically significant difference (K i MLNs vs K i BLNs (0.021 ± 0.014 vs 0.006 ± 0.004, p < 0.0001); (SUV max MLNs vs SUV max BLNs (4.35 ± 2.27 vs 1.89 ± 0.85, p < 0.0001) ( Fig. 1, Table 2 ). It suggested both SUVmax amd Ki were capable quantification parameters to differentiate the BLN from MLN. Parameters comparison of BLNs from N0 and non-N0 stage In 133 BLNs, 83 were from N0 stage and 50 from non-N0 stage. And the parameter values (K i , SUV max ) in two groups were similarly (0.006 ± 0.004 vs 0.006 ± 0.004, 1.99 ± 0.927 vs 1.73 ± 0.69) and without significant difference (p > 0.05) ( Table 2 , Fig. S1 ). Parameters correlations To further quantify the parameters of SUV max and K i , the correlation between SUV max and K i were analyzed for the Pear’s correlations. Based on our results, the Pear’s correlation factor r = 0.858, p < 0.0001 for lymph nodes. As for the correlation between N0 stage and non-N0 stage in the primary tumors, SUV max and K i were r = 0.952 verse 0.911, p < 0.0001) ( Table 3, Fig. 3 ). Sensitivity, specificity and accuracy The sensitivity order for MLN differentiation diagnosis for K i and SUV max were 80.77 %, the specificity for K i was 89.47% ) and SUV max was 87.22% ). And the diagnostic accuracy K i (90.61%) > SUV max (88.16%) ( Table 4 , Fig. 4 ). Discussion Developing non-invasively methods to evaluate and differentiate MLN from BLN is clinically important. Our study is the first time to focus on the issue with dynamic 18 F-FDG PET/CT in patients with ESCC and the main results indicated that: 1) diagnostic parameters, including K i and SUV max in MLN were higher than in BLN, and the difference with statistically significant ( p < 0.00001); 2) SUV max and K i was best correlated in both lymph nodes and primary tumors ( r = 0.858 verse r = 0.952); 3) quantitative dynamic 18 F-FDG PET/CT protocol may suggest higher accuracy for distinguishing MLN from BLN in ESCC patients; 4) All parameters of the primary tumors in N0 stage was slightly higher than non-N0 stage, but without significant difference ( p > 0.05). Maximal standard uptake value (SUV max ) is usually used as the parameter for PET semi-quantitative analysis in clinic to evaluate glucose metabolism of static imaging 3,16,17 . Pharmacokinetic analysis of dynamic PET/CT allows quantitative assessment of FDG influx constant (K i ) using Patlak model. Time-activity curves (TACs) provide kinetic parameters that allow the assessment of physiological processes in space and time. For most malignant lesions the TACs were persistent ascending, whereas the majority of the benign lesions showed a low slope and the FDG uptake were lower 12,19,20 . We compared of SUV max , K i , in MLN and BLN. In general, we found each parameter had great significant difference between MLN and BLN (p < 0.0001, Fig.1 ). Further more, to investigate the impact of different locations of primary ESCC, BLN and MLN parameters at different ESCC location were compared in detail ( Table 2 ), upper and middle thoracic locations seemed to show more significant difference (all p < 0.001), but the cervical and the lower thoracic & abdominal showed less significance (p < 0.01), this was possibly associated with the clinical primary ESCC of upper and middle thoracic ESCC accounted for the most ( Table S2 ). We compared the BLN from N0 stage and non-No stage, and no difference was found, which was reasonable in clinic. We also compared the above mentioned parameters in PT (N0 stage verse non-No stage). Interestingly, we found both SUV max and K i in non-N0 group was slightly higher than in N0 groups ( Table S1 , Fig. 2 ), however, there was no significant difference in two groups (p > 0.05). This may be caused by limited patient population in the study. It has been reported that SUV max was associated with the histopathological malignancy grade and differentiation. The correlation between SUV max and K i was great in both lymph nodes ( r = 0.858) and primary tumors ( r = 0.952) ( Fig. 3 ), but it was clear that the less correlation in lymph notes arributed to the difference between BLN and MLN. ROC curve showed the diagnostic accuracy that K i (90.61%) was greater than SUV max (88.16%) ( Table 2, Fig. 4 ). Which may indicate K i is a more sensitive diagnostic parameter in dynamic imaging than static modality (SUV max ). Yuan S et al. assessed locoregional lymph nodes in 32 ESCC patients, and they reported the sensitivity, specificity and accuracy of PET/CT for malignant lymph nodes diagnosis were 93.90%, 92.06% and 92.44%, respectively. 17 And later, Hu Q et al reported that the static FDG PET/CT for differentiating malignancy from benign were 76.06%, 85.16%, 83.33% 21 . Our results were moderate compared with others. This study is the first time to evaluate MLN with dynamic 18 F-FDG PET/CT in patients with ESCC. Previous dynamic 18 F-PET FDG study mainly focus on differentiation of benign from malignant primary lesions 12,13,22,23 , but rarely was related to MLN diagnosis. Yang M et al discussed dynamic 18 F-FDG PET scans in 62 non-small cell lung cancer (NSCLC), and concluded that the dynamic modeling for MLN (K i MLN ) was more sensitive than the SUV max to detect metastatic lymph nodes 14 . Lockau H et al underwent dynamic 18 F-FDG PET lymphography for identification of lymph node metastases in murine melanoma, and indicated the MLN showed significantly longer retention of the radiotracer than in nonmetastatic lymph nodes 20 . This is consisting with our findings. Lockau H et al performed multiple time points dynamic PET/CT in 74 patients with oral/head and neck cancer, and their results indicated that the 18 F-FDG-PET did not predictably identify metastatic cervical lymph nodes 24 . Since not all of lymph nodes were operated and pathologically confirmed, and the enrolled patients were limited in this study, further investigations are needed to confirm its potential in MLN differentiation. Studies have demonstrated the value of dynamic PET/CT scan in lesions differential diagnosis 12,18-20,22,23,25-27 , but it has not been translated to the clinic, mainly because its complexity and only allows the assessment of one field of view (typically 15–25 cm), limited the coverage extent of scanner 18,28 . Dynamic whole-body PET/CT, with iterative image reconstruction, it is possible to acquire eyes-to-thighs imaging in a shorter time, which may overcome the drawback of routine PET/CT scan 29 . Conclusions Both dynamic and static PET/CT are capable to identify all primary ESCC lesions. Quantitative dynamic parameters of 18 F-FDG in metastatic lymph nodes are higher than in benign lymph nodes, K i may be an more important and sensitive diagnostic parameter in dynamic imaging than static SUV max. Declarations Acknowledgments This work was funded by the National Key R&D Program of China (2018YFC0910600), the National Natural Science Foundation of China (No.81871382), Key Realm R&D Program of Guangdong Province (2018B030337001), and Starting Fund from Sun Yat-sen University Fifth Affiliated Hospital. The authors would like to thank Ye Liu in Pathology Department, Fanwei Zhang in Nuclear Medicine Department, Min Yang in Guangdong Provincial Key Laboratory of Biomedical Imaging and Xiaojian Li in Department of Cardiothoracic Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, for their kind support in this work. References 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: a cancer journal for clinicians. Nov 2018;68(6):394-424. Liao HY, Wang GP, Gu LJ, et al. 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Tables Table 1 Patient characteristics Category PTs (N0-group) PTs (non-N0 group) p value Gender Male 16 20 Female 4 6 0.76 Age (years) 67 ± 9 60 ± 10 0.08 Weight (Kg) 57.99 ± 8.44 54.33 ± 7.22 0.28 PT location Cervical 3 2 0.71 Upper thoracic 4 3 Middle thoracic 16 11 Lower thoracic & abdominal 3 4 ESCC: Esophageal squamous cell carcinoma PT: Primary tumors N0: Primary tumor with no metastatic lymph nodes non-N0 group: Primary tumor with metastatic lymph nodes Table 2 BLN and MLN parameters comparison at different ESCC location ESCC location Parameters BLN (N0 stage) (n = 83) BLN (non-N0 stage) (n = 50) MLN ( n = 52) p value Cervical K i SUV max 0.006 ± 0.003 1.68 ± 0.72 (n = 8) 0.007 ± 0.003 2.00 ± 0.60 (n = 7) 0.03 ± 0.024 6.58 ± 4.14 (n = 6) 0.0078 0.0023 Upper thoracic K i SUV max 0.004 ± 0.001 1.74 ± 0.49 (n = 12) 0.008 ± 0.002 2.10 ± 0.59 (n = 11) 0.02 ± 0.01 4.21 ± 2.04 (n = 4) < 0.0001 0.0002 Middle thoracic K i SUV max 0.006 ± 0.004 1.80 ± 0.80 （n = 50） 0.004 ± 0.002 1.42 ± 0.41 (n = 22) 0.02 ± 0.01 3.73 ± 1.71 (n = 20) < 0.0001 < 0.0001 Lower thoracic & abdominal K i SUV max 0.006 ± 0.006 2.43 ± 1.21 (n = 13) 0.009 ± 0.007 0.049 ± 0.042 (n = 10) 0.02 ± 0.01 4.32 ± 1.85 (n = 22) < 0.0001 0.0019 Table 3 Correlation coefficient (Spearman r ) between SUV max and K i in different groups SUV max verse K i r 95% confidence interval p value LNs 0.858 0.815 to 0.892 p < 0.0001 PTs 0.952 0.916 to 0.973 p < 0.0001 LNs :l ymph nodes , including BLNs and MLNs, PTs:Primary tumors Table 4 Quantitative analysis of malignant lymph nodes (MLN) and benign lymph nodes (BLN) K i (min -1 ) SUV max MLNs (52) 0.021 ± 0.014 4.35 ± 2.27 BLNs (133) 0.006 ± 0.004 1.89 ± 0.85 p value < 0.0001 < 0.0001 Sensitivity (%) 80.77 80.77 Specificity (%) 89.47 87.22 Accuracy (%) 90.61 88.16 Supplementary Files Supplementalmaterials.docx Supplementalmaterials.docx 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. 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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-127268","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Original research","associatedPublications":[],"authors":[{"id":6341940,"identity":"a23afb37-ec7c-4ea7-933b-f66a8f87952f","order_by":0,"name":"Xiaohui Wang","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaohui","middleName":"","lastName":"Wang","suffix":""},{"id":6341941,"identity":"57562a9a-d77a-4656-87be-0a94701157d9","order_by":1,"name":"Qingdong Cao","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Qingdong","middleName":"","lastName":"Cao","suffix":""},{"id":6341942,"identity":"2027a1a5-8b11-4e67-b298-7541df2a57ef","order_by":2,"name":"Xiaojing Wang","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaojing","middleName":"","lastName":"Wang","suffix":""},{"id":6341943,"identity":"30f21c40-990b-4cad-8b74-5e6864a5ee45","order_by":3,"name":"Ying Wang","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ying","middleName":"","lastName":"Wang","suffix":""},{"id":6341944,"identity":"2c47dc1f-e1bf-4222-930b-271e9cdcf750","order_by":4,"name":"Dan Li","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Li","suffix":""},{"id":6341945,"identity":"54f6c04a-f235-45ac-8cc2-005b5799a93d","order_by":5,"name":"Lei Bi","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Bi","suffix":""},{"id":6341946,"identity":"e5fd2ddf-dca0-433a-8115-4400fd9ee058","order_by":6,"name":"Shuai Yang","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shuai","middleName":"","lastName":"Yang","suffix":""},{"id":6341947,"identity":"4b0c7f86-3868-4e86-9a1e-9639814fd658","order_by":7,"name":"Hong Shan","email":"","orcid":"","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hong","middleName":"","lastName":"Shan","suffix":""},{"id":6341948,"identity":"3ff5aef6-fb68-4e98-bb0e-2ecd1581b9a8","order_by":8,"name":"Hongjun Jin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA90lEQVRIiWNgGAWjYBACxmYGNgaJHzYMDMzMDTBBA8JaJHvSgFoYidQCBGxAdBikm0gtzO28xx5Y8JyP5m9nbGD82VaX2MDevE2CoeYOHofxpRtIWNzOnXGYsYGZt+1wYgPPsTIJhmPP8GjhMZOQ4Lmd2wDSwth2ILFBIsdMgrHhMAEtbOdy5x+GOUz+DVFaDuRuAGph4G1jBtrCQ4QWyZ7k3I1ALYd5zh02buNJK7ZIOIZbi2H/GTNpiR92ufPOHz748EdZnWw/++GNNz7U4NHSAAxoCSjnACMbOJoYGBJwamBgkAc57gOc+weP0lEwCkbBKBixAADrZFCpzIL+BwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1522-1098","institution":"Fifth Affiliated Hospital of Sun Yat-sen University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Hongjun","middleName":"","lastName":"Jin","suffix":""}],"badges":[],"createdAt":"2020-12-12 13:26:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-127268/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-127268/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":4306282,"identity":"5b70fc48-d1ea-46ee-9c33-55c531938c9c","added_by":"auto","created_at":"2020-12-16 16:43:58","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49783,"visible":true,"origin":"","legend":"The 18F-FDG uptake of metastatic lymph nodes (MLN) were significantly higher than benign lymph nodes (BLN) Comparison of Ki, SUVmax between MLN and BLN; Mann-Whitney U test test, **** indicate p \u003c 0.0001.","description":"","filename":"1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/f44a9080a738046da4621dbe.JPG"},{"id":4306274,"identity":"565dd27b-9dda-485a-83f7-20ae210fe475","added_by":"auto","created_at":"2020-12-16 16:43:52","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":49783,"visible":true,"origin":"","legend":"The 18F-FDG uptake of metastatic lymph nodes (MLN) were significantly higher than benign lymph nodes (BLN) Comparison of Ki, SUVmax between MLN and BLN; Mann-Whitney U test test, **** indicate p \u003c 0.0001.","description":"","filename":"1.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/2712792c1bc89ac7c830a3f8.JPG"},{"id":4306284,"identity":"ab779a4e-a701-4de7-8797-531f63675f2c","added_by":"auto","created_at":"2020-12-16 16:43:59","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45224,"visible":true,"origin":"","legend":"There was no 18F-FDG uptake difference in primary tumor (PT) between N0 and non-N0 stage Comparison of Ki, SUVmax in PT between N0 stage and non-N0 stage patients; p \u003e 0.05 (non-significant difference, NS).","description":"","filename":"2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/71470bbe8aff513b944e2e0a.JPG"},{"id":4306276,"identity":"3e8052dc-b7f2-444a-bf71-59ad78fc64aa","added_by":"auto","created_at":"2020-12-16 16:43:52","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":45224,"visible":true,"origin":"","legend":"There was no 18F-FDG uptake difference in primary tumor (PT) between N0 and non-N0 stage Comparison of Ki, SUVmax in PT between N0 stage and non-N0 stage patients; p \u003e 0.05 (non-significant difference, NS).","description":"","filename":"2.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/45a228507689b7e9a173c7a0.JPG"},{"id":4306285,"identity":"1f87ade6-d8a4-4dfc-a88f-313d22ab92ab","added_by":"auto","created_at":"2020-12-16 16:43:59","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49701,"visible":true,"origin":"","legend":"Correlations between four parameters A. Correlation between SUVmax and Ki of lymph nodes (r = 0.832, p \u003c 0.0001); B. Correlation between SUVmax and Ki of primary tumors (r = 0.911, p \u003c 0.0001).","description":"","filename":"3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/b91fe1fe9489f13b304bedba.JPG"},{"id":4306277,"identity":"133d16c2-ca0f-4b17-ba1a-4095343bab30","added_by":"auto","created_at":"2020-12-16 16:43:53","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":49701,"visible":true,"origin":"","legend":"Correlations between four parameters A. Correlation between SUVmax and Ki of lymph nodes (r = 0.832, p \u003c 0.0001); B. Correlation between SUVmax and Ki of primary tumors (r = 0.911, p \u003c 0.0001).","description":"","filename":"3.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/2f549d68ee7560e49ca595cd.JPG"},{"id":4306286,"identity":"208dcb91-74ff-4eb7-9206-6e479427794f","added_by":"auto","created_at":"2020-12-16 16:43:59","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66786,"visible":true,"origin":"","legend":"ROC curves analysis For a cut-off value of 2.711 SUVmax, the sensitivity is 80.77%, the specificity 87.22% and accuracy 88.16%; For a cut-off value of 0.0099 Ki the sensitivity is 80.77%, the specificity 89.47% and accuracy 90.61%. The diagnostic accuracy order is Ki \u003e SUVmax.","description":"","filename":"4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/3f4115f23593fac49c4eb96b.JPG"},{"id":4306278,"identity":"b1a9fc0f-c3be-4dd5-9125-4c9d80d17569","added_by":"auto","created_at":"2020-12-16 16:43:53","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":66786,"visible":true,"origin":"","legend":"ROC curves analysis For a cut-off value of 2.711 SUVmax, the sensitivity is 80.77%, the specificity 87.22% and accuracy 88.16%; For a cut-off value of 0.0099 Ki the sensitivity is 80.77%, the specificity 89.47% and accuracy 90.61%. The diagnostic accuracy order is Ki \u003e SUVmax.","description":"","filename":"4.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/8690b23cd830696f98181431.JPG"},{"id":4306287,"identity":"bf7c0e85-71e5-4243-ab88-5e5f13761279","added_by":"auto","created_at":"2020-12-16 16:43:59","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91597,"visible":true,"origin":"","legend":"Representative images to demonstrate MLN vs BLN. A 65-year-old male patient with poor differentiated ESCC, pathological stage was T3N2M0. A. Maximum intensity projection (MIP) PET image shows an 18F-FDG-avid primary tumor at the lower thoracic \u0026 abdominal (red long arrow) and an 18F-FDG-avid lymph node in the cervical (black short arrow). B and C were trans-axial images, and small benign lymph nodes (BLN) was found surround the primary tumor (B), C showed a metastatic lymph node (MLN). The pathology confirmed the lymph nodes (D, primary tumor; E, BLN; F, MLN). G. Patlak plot of slope for Ki calculation for MLN (red) and BLN (black): the slope of the Patlak plot equals influx constant Ki. And the KiMLN was much higher than KiBLN in this patient although the images showed similar uptake.","description":"","filename":"5.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/a366d7c572b11d94bf7949e8.JPG"},{"id":4306279,"identity":"35fca2ef-3900-4856-abe0-1101fd3d2188","added_by":"auto","created_at":"2020-12-16 16:43:53","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":91597,"visible":true,"origin":"","legend":"Representative images to demonstrate MLN vs BLN. A 65-year-old male patient with poor differentiated ESCC, pathological stage was T3N2M0. A. Maximum intensity projection (MIP) PET image shows an 18F-FDG-avid primary tumor at the lower thoracic \u0026 abdominal (red long arrow) and an 18F-FDG-avid lymph node in the cervical (black short arrow). B and C were trans-axial images, and small benign lymph nodes (BLN) was found surround the primary tumor (B), C showed a metastatic lymph node (MLN). The pathology confirmed the lymph nodes (D, primary tumor; E, BLN; F, MLN). G. Patlak plot of slope for Ki calculation for MLN (red) and BLN (black): the slope of the Patlak plot equals influx constant Ki. And the KiMLN was much higher than KiBLN in this patient although the images showed similar uptake.","description":"","filename":"5.JPG","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/dc67a3037b47f3e96eaae027.JPG"},{"id":13634333,"identity":"86426d21-5b27-46b8-b80f-20d21a0663fc","added_by":"auto","created_at":"2021-09-17 08:31:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":889345,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/4200d778-dfe8-4b29-8ff3-7a09cde178d9.pdf"},{"id":4306283,"identity":"a78c4d51-d398-4f04-9e1c-aa4e84096c09","added_by":"auto","created_at":"2020-12-16 16:43:58","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":114899,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/9e7bba32aec84fabed1bf89b.docx"},{"id":4306275,"identity":"e40c8b86-6618-4088-8e06-786d3ac6e849","added_by":"auto","created_at":"2020-12-16 16:43:52","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":114899,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementalmaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-127268/v1/ffed9c6a0ab346b662752862.docx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eQuantification of Malignant Lymph Nodes and Benign Lymph Nodes in Patients of Esophageal Squamous Cell Carcinoma With Dynamic 18F-FDG PET/CT\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEsophageal cancer is one of the most aggressive malignancies in the world, which accounted for an estimated 572,034 new cases and 508,585 deaths in 2018 worldwide\u003csup\u003e1\u003c/sup\u003e. The incidence and mortality of esophageal cancer is ranked first in China and esophageal squamous cell carcinoma (ESCC) is the main histological subtype of esophageal cancers in China\u003csup\u003e2\u003c/sup\u003e. Correct preoperative evaluation of whether the tumor has reached any lymph nodes is important for management. Various methods have been used to detect primary and lymph node metastases in esophageal cancer patients, including computed tomography (CT), endoscopic examinations, and endoscopic ultrasonography (EUS). However, even such advanced imaging modalities do not always reliably identify lymph node metastasis prior to surgical resection and pathological examination.\u003c/p\u003e\n\u003cp\u003eThe appearance of lymph nodes with morphological imaging procedures is classified by their shape, size, density and, if applied, contrast enhancement. Benign lymph nodes (BLN) usually tend to have a fatty hilum, an oval shape and frequently do not measure more than 1 cm in the short axis diameter. However, the use of size as the most important criterion for differentiation of benign and malignant lymph nodes has limitations: small metastases without an increase in lymph node size are frequently missed\u003csup\u003e3\u003c/sup\u003e. Positron emission tomography (PET)/computed tomography (CT) is increasingly used as a promising method, which the combination of morphological and functional imaging represents the optimal approach for lymph node staging and general staging\u003csup\u003e4\u003c/sup\u003e. A radioactive tracer，2-[\u003csup\u003e18\u003c/sup\u003eF]fluoro-2-deoxy-dglucose (\u003csup\u003e18\u003c/sup\u003eF-FDG) currently used is based on the increased glucose metabolism, which may be reported with semiquantitative standard uptake value (SUV). It was reported that PET/CT sensitivity and specificity for the detection of loco-regional metastases were moderate, but sensitivity and specificity were reasonable for distant metastases\u003csup\u003e5\u003c/sup\u003e. In malignancy, the uptake of \u003csup\u003e18\u003c/sup\u003eF-FDG continues to increase for several hours after FDG injection whereas such prolonged period of \u003csup\u003e18\u003c/sup\u003eF-FDG uptake is rare in inflammatory/infectious or normal tissues\u003csup\u003e6-8\u003c/sup\u003e. Shum et al ever assessed clinical usefulness of dual-time \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in ESCC, which turned out the sensitivity of \u003csup\u003e18\u003c/sup\u003eF-FDG PET-CT in detecting the primary ESCC with combination of early SUV\u003csub\u003emax\u003c/sub\u003e \u0026ge; 2.5 or retention index (RI) \u0026ge; 10% was 96.2%\u003csup\u003e9\u003c/sup\u003e. However, for loco-regional lymph node detection, there was no significant difference\u003csup\u003e9\u003c/sup\u003e. Dynamic \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT allows quantitative assessment of lesion \u003cem\u003ein vivo\u003c/em\u003e by using a Patlak model to obtain the influx constant (K\u003csub\u003ei\u003c/sub\u003e)\u003csup\u003e10-13\u003c/sup\u003e. The purpose of this study is to quantify MLN and BLN in patients of ESCC with \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT by applying both routing scan (SUV\u003csub\u003emax\u003c/sub\u003e) and dynamic scans (K\u003csub\u003ei\u003c/sub\u003e).\u003c/p\u003e"},{"header":"Patients And Methods","content":"\u003cp\u003e\u003cstrong\u003ePatients population\u003c/strong\u003e Forty-six patients (36 men, 10 women; age range, 45-85 years old; mean age, 64-year-old) who were pathologically confirmed ESCC were included in this study (\u003cstrong\u003eTable 1\u003c/strong\u003e). Exclusion criteria were diabetes mellitus, fasted glucose level \u0026ge;11.0 mmol\u0026middot;L\u003csup\u003e-1\u003c/sup\u003e, breast feeding, pregnancy and claustrophobia. Conventional medical imaging for these patients were carried out with routing CT, gastroduodenoscopy. Surgical pathology results were used to provide the final diagnosis with which the \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT results were compared. The study was approved by the institutional review board of the Fifth Affiliated Hospital of Sun Yat-sen University (IRB protocol # ZDWY.FZYX.006). All included patients provided signed informed consent. The clinical trial registration number is NCT04514822 (http://www.clinicaltrials.gov/). Baseline clinical characteristics, including sex, age, height, weight, and tumor characteristics were obtained from electronic medical records.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImaging protocol\u003c/strong\u003e All patients were fasted for at least 4 h and the fasted glucose level is \u0026le; 11.0 mmoL\u0026middot;L\u003csup\u003e-1\u003c/sup\u003e. Imaging was performed with the 112-ring digital light guide PET/CT scanner (United Imaging, UMI780, Shanghai, China). The patients were fasted for at least 6 h before scans. The scan covered the region between the thoracic inlet and the lower liver margin. Each PET/CT scan began with a transmission CT scan for 5 seconds that was used for attenuation correction. After transmission CT scan (160 mA, 100 kV, pitch 0.9875, rotation time 0.5 s) for subsequent PET data attenuation correction, continual dynamic clinical PET scans were performed in a single bed position immediately after \u003csup\u003e18\u003c/sup\u003eF-FDG intravenously injection (210 \u0026plusmn; 30 MBq) in list mode for 60 minutes in supine position, dynamic 48-timeframe PET/CT imaging was obtained (18 \u0026times; 5 s, 6 \u0026times; 10 s, 5 \u0026times; 30 s, 5 \u0026times; 60 s, 8 \u0026times; 150 s, 6 \u0026times; 300 s).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRelated parameters calculation\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eVolumes of interest (VOIs) \u003c/em\u003eVolumes of interest (VOIs) were placed over the primary esophageal squamous cell carcinoma, metastatic, benign lymph nodes and the aorta, and 0-60 min time-activity curves (TACs) were generated for further data evaluation. A VOI consists of at least 3 regions of interest (ROIs) over the target area. Irregular ROIs were drawn manually using Carimas 2.10 software (turkupetcentre.fi/carimas/) with PET and the corresponding CT slices. To compensate for patient motion during the acquisition time, the original images were visually repositioned. The arterial blood input in this study was obtained from the left ventricle or aorta using image-derived input function as an input function as we previously reported \u003csup\u003e14\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePatlak analysis \u003c/em\u003eThe dynamic quantitative data were analyzed with the Patlak graphical model using Matlab program (Version 2014a) followed the methods from our group and other as published previously\u003csup\u003e14,15\u003c/sup\u003e. To simplify the quantification protocol, the influx constant (K\u003csub\u003ei\u003c/sub\u003e) was calculated using Patlak linear regression analysis based on linear range selected in the time period of 40 to 60 min p.i..\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/em\u003e Standardised uptake value (SUV) of lesions on the frames at 55\u0026ndash;60 min and 25-30 min were calculated. The SUV\u003csub\u003emax \u003c/sub\u003ewas produced. PET/CT images were analyzed by two experienced nuclear medicine physicians, and the disagreement was discussed with the third expert. In 46 ESCC patients, 26 patients without MLN (N0 stage) and 20 with MLN (non-N0 stage). Finally, 52 MLN and 133 BLN (83 from N0 stage and 50 from non-N0 stage) were included for analysis. All parameters (K\u003csub\u003ei\u003c/sub\u003e and SUV\u003csub\u003emax\u003c/sub\u003e) of each lesion were calculated. The difference of the primary tumor between N0 stage and non-N0 stage, MLN verse BLN, BLN from N0 stage and non-N0 stage were calculated respectively. The sensitivity, specificity and accuracy for each parameter in differentiating malignant and benign lymph nodes were evaluated based on the receiver operator characteristic (ROC) curve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test for differences between MLNs, BLNs and the primary tumor between N0 stage and non-N0 stage, the Mann-Whitney U test was used. Correlations were examined using the Spearman\u0026rsquo;s rank correlation test. Cutoff values for differentiation were determined using receiver-operating-characteristic curve analysis, and the area under the curve was calculated. Statistical analysis was conducted using GraphPad Prism 6 software. A two-sided\u003cem\u003e p\u003c/em\u003e value of less than 0.05 was considered to be statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParameters \u003c/strong\u003e\u003cstrong\u003ecomparison from PTs between N0 and non-N0 stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 46 patients, 26 patients were in N0 stage and 20 in non-N0 stage. According to the PT sites, we divided the PTs in four parts (which were cervical, upper thoracic, middle thoracic and lower thoracic \u0026amp; abdominal), and found middle thoracic ESCC accounted for the most, 16/26 (61.5%) in N0 verse 11/20 (55%) in non-N0 stage. All parameters were compared in two groups at different locations. Generally speaking, K\u003csub\u003ei\u003c/sub\u003e, SUV\u003csub\u003emax\u003c/sub\u003e in PT non-N0 group was slightly higher than in N0 groups (0.04\u0026nbsp;\u0026plusmn;\u0026nbsp;0.02 vs 0.03 \u0026plusmn; 0.03, 8.01\u0026nbsp;\u0026plusmn;\u0026nbsp;3.90 vs 7.08\u0026nbsp;\u0026plusmn;\u0026nbsp;5.39, respectively), but with no significant difference (p \u0026gt; 0.05) (\u003cstrong\u003eTable S1\u003c/strong\u003e\u003cstrong\u003e, Fig. 2\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParameters \u003c/strong\u003e\u003cstrong\u003ecomparison between MLNs and BLNs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAlthough the primary tumors were quantified from our PET/CT analysis with no significant difference, we further analyzed the MLN and BLN. Based on our analysis, both K\u003csub\u003ei\u003c/sub\u003e and SUV\u003csub\u003emax\u003c/sub\u003e in MLNs were higher than BLNs with statistically significant difference (K\u003csub\u003ei\u003c/sub\u003e\u003csup\u003eMLNs\u003c/sup\u003e vs K\u003csub\u003ei\u003c/sub\u003e\u003csup\u003eBLNs\u003c/sup\u003e (0.021 \u0026plusmn; 0.014 vs 0.006 \u0026plusmn; 0.004, p \u0026lt; 0.0001); (SUV\u003csub\u003emax\u003c/sub\u003e\u003csup\u003eMLNs\u003c/sup\u003e vs SUV\u003csub\u003emax\u003c/sub\u003e\u003csup\u003eBLNs\u003c/sup\u003e (4.35 \u0026plusmn; 2.27 vs 1.89 \u0026plusmn; 0.85, p \u0026lt; 0.0001) (\u003cstrong\u003eFig. 1, Table 2\u003c/strong\u003e). It suggested both SUVmax amd Ki were capable quantification parameters to differentiate the BLN from MLN.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParameters \u003c/strong\u003e\u003cstrong\u003ecomparison of BLNs from N0 and non-N0 stage\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn 133 BLNs, 83 were from N0 stage and 50 from non-N0 stage. And the parameter values (K\u003csub\u003ei\u003c/sub\u003e, SUV\u003csub\u003emax\u003c/sub\u003e) in two groups were similarly (0.006 \u0026plusmn; 0.004 vs 0.006 \u0026plusmn; 0.004, 1.99 \u0026plusmn; 0.927 vs 1.73 \u0026plusmn; 0.69) and without significant difference (p \u0026gt; 0.05) (\u003cstrong\u003eTable 2\u003c/strong\u003e\u003cstrong\u003e, \u003c/strong\u003e\u003cstrong\u003eFig. S1\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParameters \u003c/strong\u003e\u003cstrong\u003ecorrelations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further quantify the parameters of SUV\u003csub\u003emax \u003c/sub\u003eand K\u003csub\u003ei\u003c/sub\u003e, the correlation between SUV\u003csub\u003emax \u003c/sub\u003eand K\u003csub\u003ei\u003c/sub\u003e were analyzed for the Pear\u0026rsquo;s correlations. Based on our results, the Pear\u0026rsquo;s correlation factor \u003cem\u003er\u003c/em\u003e = 0.858, \u003cem\u003ep \u003c/em\u003e\u0026lt; 0.0001 for lymph nodes. As for the correlation between N0 stage and non-N0 stage in the primary tumors, SUV\u003csub\u003emax \u003c/sub\u003eand K\u003csub\u003ei\u003c/sub\u003e were \u003cem\u003er\u003c/em\u003e = 0.952 verse 0.911,\u003cem\u003e p \u003c/em\u003e\u0026lt; 0.0001) (\u003cstrong\u003eTable 3, \u003c/strong\u003e\u003cstrong\u003eFig. 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity, specificity and accuracy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe sensitivity order for MLN differentiation diagnosis for K\u003csub\u003ei\u003c/sub\u003e and SUV\u003csub\u003emax\u003c/sub\u003e were 80.77 %, the specificity for K\u003csub\u003ei \u003c/sub\u003ewas 89.47% ) and SUV\u003csub\u003emax\u003c/sub\u003e was 87.22% ). And the diagnostic accuracy K\u003csub\u003ei\u003c/sub\u003e (90.61%) \u0026gt; SUV\u003csub\u003emax\u003c/sub\u003e (88.16%) (\u003cstrong\u003eTable 4\u003c/strong\u003e, \u003cstrong\u003eFig. 4 \u003c/strong\u003e).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eDeveloping non-invasively methods to evaluate and differentiate MLN from BLN is clinically important. Our study is the first time to focus on the issue with dynamic \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in patients with ESCC and the main results indicated that: 1) diagnostic parameters, including K\u003csub\u003ei \u003c/sub\u003eand SUV\u003csub\u003emax\u003c/sub\u003e in MLN were higher than in BLN, and the difference with statistically significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.00001); 2) SUV\u003csub\u003emax\u003c/sub\u003e and K\u003csub\u003ei\u003c/sub\u003e was best correlated in both lymph nodes and primary tumors (\u003cem\u003er = \u003c/em\u003e0.858 verse \u003cem\u003er = \u003c/em\u003e0.952); 3) quantitative dynamic \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT protocol may suggest higher accuracy for distinguishing MLN from BLN in ESCC patients; 4) All parameters of the primary tumors in N0 stage was slightly higher than non-N0 stage, but without significant difference (\u003cem\u003ep \u003c/em\u003e\u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003eMaximal standard uptake value (SUV\u003csub\u003emax\u003c/sub\u003e) is usually used as the parameter for PET semi-quantitative analysis in clinic to evaluate glucose metabolism of static imaging\u003csup\u003e3,16,17\u003c/sup\u003e. Pharmacokinetic analysis of dynamic PET/CT allows quantitative assessment of FDG influx constant (K\u003csub\u003ei\u003c/sub\u003e) using Patlak model. Time-activity curves (TACs) provide kinetic parameters that allow the assessment of physiological processes in space and time. For most malignant lesions the TACs were persistent ascending, whereas the majority of the benign lesions showed a low slope and the FDG uptake were lower \u003csup\u003e12,19,20\u003c/sup\u003e. We compared of SUV\u003csub\u003emax\u003c/sub\u003e, K\u003csub\u003ei\u003c/sub\u003e, in MLN and BLN. In general, we found each parameter had great significant difference between MLN and BLN (p \u0026lt; 0.0001, \u003cstrong\u003eFig.1\u003c/strong\u003e). Further more, to investigate the impact of different locations of primary ESCC, BLN and MLN parameters at different ESCC location were compared in detail (\u003cstrong\u003eTable 2\u003c/strong\u003e), upper and middle thoracic locations seemed to show more significant difference (all p \u0026lt; 0.001), but the cervical and the lower thoracic \u0026amp; abdominal showed less significance (p \u0026lt; 0.01), this was possibly associated with the clinical primary ESCC of upper and middle thoracic ESCC accounted for the most (\u003cstrong\u003eTable S2\u003c/strong\u003e). We compared the BLN from N0 stage and non-No stage, and no difference was found, which was reasonable in clinic.\u003c/p\u003e\n\u003cp\u003eWe also compared the above mentioned parameters in PT (N0 stage verse non-No stage). Interestingly, we found both SUV\u003csub\u003emax\u003c/sub\u003e and K\u003csub\u003ei \u003c/sub\u003ein non-N0 group was slightly higher than in N0 groups (\u003cstrong\u003eTable S1\u003c/strong\u003e\u003cstrong\u003e, Fig. 2\u003c/strong\u003e), however, there was no significant difference in two groups (p \u0026gt; 0.05). This may be caused by limited patient population in the study. It has been reported that SUV\u003csub\u003emax \u003c/sub\u003ewas associated with the histopathological malignancy grade and differentiation.\u003c/p\u003e\n\u003cp\u003eThe correlation between SUV\u003csub\u003emax\u003c/sub\u003e and K\u003csub\u003ei\u003c/sub\u003e was great in both lymph nodes (\u003cem\u003er = \u003c/em\u003e0.858) and primary tumors (\u003cem\u003er\u003c/em\u003e = 0.952) (\u003cstrong\u003eFig. 3\u003c/strong\u003e), but it was clear that the less correlation in lymph notes arributed to the difference between BLN and MLN. ROC curve showed the diagnostic accuracy that K\u003csub\u003ei\u003c/sub\u003e (90.61%) was greater than SUV\u003csub\u003emax\u003c/sub\u003e (88.16%) (\u003cstrong\u003eTable 2, Fig. 4\u003c/strong\u003e). Which may indicate K\u003csub\u003ei\u003c/sub\u003e is a more sensitive diagnostic parameter in dynamic imaging than static modality (SUV\u003csub\u003emax\u003c/sub\u003e). Yuan S et al. assessed locoregional lymph nodes in 32 ESCC patients, and they reported the sensitivity, specificity and accuracy of PET/CT for malignant lymph nodes diagnosis were 93.90%, 92.06% and 92.44%, respectively.\u003csup\u003e17\u003c/sup\u003e And later, Hu Q et al reported that the static FDG PET/CT for differentiating malignancy from benign were 76.06%, 85.16%, 83.33%\u003csup\u003e21\u003c/sup\u003e. Our results were moderate compared with others.\u003c/p\u003e\n\u003cp\u003eThis study is the first time to evaluate MLN with dynamic \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT in patients with ESCC. Previous dynamic \u003csup\u003e18\u003c/sup\u003eF-PET FDG study mainly focus on differentiation of benign from malignant primary lesions\u003csup\u003e12,13,22,23\u003c/sup\u003e, but rarely was related to MLN diagnosis. Yang M et al discussed dynamic \u003csup\u003e18\u003c/sup\u003eF-FDG PET scans in 62 non-small cell lung cancer (NSCLC), and concluded that the dynamic modeling for MLN (K\u003csub\u003ei\u003c/sub\u003e\u003csup\u003e MLN\u003c/sup\u003e) was more sensitive than the SUV\u003csub\u003emax\u003c/sub\u003e to detect metastatic lymph nodes\u003csup\u003e14\u003c/sup\u003e. \u003ca href=\"https://www.ncbi.nlm.nih.gov/pubmed/?term=Lockau%20H%5BAuthor%5D\u0026amp;cauthor=true\u0026amp;cauthor_uid=28912145\"\u003eLockau H\u003c/a\u003e et al underwent dynamic \u003csup\u003e18\u003c/sup\u003eF-FDG PET lymphography for identification of lymph node metastases in murine melanoma, and indicated the MLN showed significantly longer retention of the radiotracer than in nonmetastatic lymph nodes\u003csup\u003e20\u003c/sup\u003e. This is consisting with our findings. Lockau H et al performed multiple time points dynamic PET/CT in 74 patients with oral/head and neck cancer, and their results indicated that the \u003csup\u003e18\u003c/sup\u003eF-FDG-PET did not predictably identify metastatic cervical lymph nodes\u003csup\u003e24\u003c/sup\u003e. Since not all of lymph nodes were operated and pathologically confirmed, and the enrolled patients were limited in this study, further investigations are needed to confirm its potential in MLN differentiation. Studies have demonstrated the value of dynamic PET/CT scan in lesions differential diagnosis\u003csup\u003e12,18-20,22,23,25-27\u003c/sup\u003e, but it has not been translated to the clinic, mainly because its complexity and only allows the assessment of one field of view (typically 15\u0026ndash;25 cm), limited the coverage extent of scanner\u003csup\u003e18,28\u003c/sup\u003e. Dynamic whole-body PET/CT, with iterative image reconstruction, it is possible to acquire eyes-to-thighs imaging in a shorter time, which may overcome the drawback of routine PET/CT scan\u003csup\u003e29\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBoth dynamic and static PET/CT are capable to identify all primary ESCC lesions. Quantitative dynamic parameters of \u003csup\u003e18\u003c/sup\u003eF-FDG in metastatic lymph nodes are higher than in benign lymph nodes, K\u003csub\u003ei\u003c/sub\u003e may be an more important and sensitive diagnostic parameter in dynamic imaging than static SUV\u003csub\u003emax.\u003c/sub\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the National Key R\u0026amp;D Program of China (2018YFC0910600), the National Natural Science Foundation of China (No.81871382), Key Realm R\u0026amp;D Program of Guangdong Province (2018B030337001), and Starting Fund from Sun Yat-sen University Fifth Affiliated Hospital. The authors would like to thank Ye Liu in Pathology Department, Fanwei Zhang in Nuclear Medicine Department, Min Yang in Guangdong Provincial Key Laboratory of Biomedical Imaging and Xiaojian Li in Department of Cardiothoracic Surgery, The Fifth Affiliated Hospital, Sun Yat-sen University, for their kind support in this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003e 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: a cancer journal for clinicians. Nov 2018;68(6):394-424.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e Liao HY, Wang GP, Gu LJ, et al. HIF-1alpha siRNA and cisplatin in combination suppress tumor growth in a nude mice model of esophageal squamous cell carcinoma. 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The use of multiple time point dynamic positron emission tomography/computed tomography in patients with oral/head and neck cancer does not predictably identify metastatic cervical lymph nodes. Journal of oral and maxillofacial surgery : official journal of the American Association of Oral and Maxillofacial Surgeons. Jan 2013;71(1):162-177.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e Karakatsanis NA, Lodge MA, Tahari AK, Zhou Y, Wahl RL, Rahmim A. Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application. Physics in medicine and biology. Oct 21 2013;58(20):7391-7418.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e Karakatsanis NA, Zhou Y, Lodge MA, et al. Generalized whole-body Patlak parametric imaging for enhanced quantification in clinical PET. Physics in medicine and biology. Nov 21 2015;60(22):8643-8673.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e Fahrni G, Karakatsanis NA, Di Domenicantonio G, Garibotto V, Zaidi H. Does whole-body Patlak (18)F-FDG PET imaging improve lesion detectability in clinical oncology? European radiology. Sep 2019;29(9):4812-4821.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003e Rahmim A, Lodge MA, Karakatsanis NA, et al. Dynamic whole-body PET imaging: principles, potentials and applications. European journal of nuclear medicine and molecular imaging. Feb 2019;46(2):501-518.\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003e\u003cem\u003eHutton BF. Recent advances in iterative reconstruction for clinical SPECT/PET and CT. Acta oncologica (Stockholm, Sweden). Aug 2011;50(6):851-858.\u003c/em\u003e\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e Patient characteristics\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eCategory\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003ePTs\u003c/p\u003e\n\u003cp\u003e(N0-group)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"166\"\u003e\n\u003cp\u003ePTs\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;(non-N0 group)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eGender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"4\" width=\"391\"\u003e\n\u003cp\u003eMale\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;16\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;20\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFemale\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;4\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;6\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"97\"\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp; 0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eAge (years)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e67 \u0026plusmn; 9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"166\"\u003e\n\u003cp\u003e60 \u0026plusmn; 10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"92\"\u003e\n\u003cp\u003eWeight (Kg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"96\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"37\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e57.99 \u0026plusmn; 8.44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"166\"\u003e\n\u003cp\u003e54.33 \u0026plusmn; 7.22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"57\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"4\" width=\"92\"\u003e\n\u003cp\u003ePT location\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003eCervical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"166\"\u003e\n\u003cp\u003e2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd rowspan=\"4\" width=\"57\"\u003e\n\u003cp\u003e0.71\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003eUpper thoracic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"166\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003eMiddle thoracic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"166\"\u003e\n\u003cp\u003e11\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" width=\"132\"\u003e\n\u003cp\u003eLower\u0026nbsp;thoracic\u003c/p\u003e\n\u003cp\u003e\u0026amp; abdominal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"166\"\u003e\n\u003cp\u003e4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eESCC: Esophageal squamous cell carcinoma \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePT: Primary tumors \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eN0: Primary tumor with no metastatic lymph nodes \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003enon-N0 group: Primary tumor with metastatic lymph nodes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e BLN and MLN parameters comparison at different ESCC location\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003e\u003cstrong\u003eESCC location\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003e\u003cstrong\u003eParameters\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003eBLN (N0 stage)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 83)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e\u003cstrong\u003eBLN\u003c/strong\u003e \u003cstrong\u003e(non-N0 stage)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(n = 50)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e\u003cstrong\u003eMLN\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e( n = 52)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e\u003cstrong\u003ep value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003eCervical\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.006 \u0026plusmn; 0.003\u003c/p\u003e\n\u003cp\u003e1.68 \u0026plusmn; 0.72\u003c/p\u003e\n\u003cp\u003e(n = 8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e0.007 \u0026plusmn; 0.003\u003c/p\u003e\n\u003cp\u003e2.00 \u0026plusmn; 0.60\u003c/p\u003e\n\u003cp\u003e(n = 7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.03 \u0026plusmn; 0.024\u003c/p\u003e\n\u003cp\u003e6.58 \u0026plusmn; 4.14\u003c/p\u003e\n\u003cp\u003e(n = 6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e0.0078\u003c/p\u003e\n\u003cp\u003e0.0023\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003eUpper thoracic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.004 \u0026plusmn; 0.001\u003c/p\u003e\n\u003cp\u003e1.74 \u0026plusmn; 0.49\u003c/p\u003e\n\u003cp\u003e(n = 12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e0.008 \u0026plusmn; 0.002\u003c/p\u003e\n\u003cp\u003e2.10 \u0026plusmn; 0.59\u003c/p\u003e\n\u003cp\u003e(n = 11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.02 \u0026plusmn; 0.01\u003c/p\u003e\n\u003cp\u003e4.21 \u0026plusmn; 2.04\u003c/p\u003e\n\u003cp\u003e(n = 4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003cp\u003e0.0002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003eMiddle thoracic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.006 \u0026plusmn; 0.004\u003c/p\u003e\n\u003cp\u003e1.80 \u0026plusmn; 0.80\u003c/p\u003e\n\u003cp\u003e（n = 50）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e0.004 \u0026plusmn; 0.002\u003c/p\u003e\n\u003cp\u003e1.42 \u0026plusmn; 0.41\u003c/p\u003e\n\u003cp\u003e(n = 22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.02 \u0026plusmn; 0.01\u003c/p\u003e\n\u003cp\u003e3.73 \u0026plusmn; 1.71\u003c/p\u003e\n\u003cp\u003e(n = 20)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"108\"\u003e\n\u003cp\u003eLower thoracic\u003c/p\u003e\n\u003cp\u003e\u0026amp; abdominal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"89\"\u003e\n\u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e0.006 \u0026plusmn; 0.006\u003c/p\u003e\n\u003cp\u003e2.43 \u0026plusmn; 1.21\u003c/p\u003e\n\u003cp\u003e(n = 13)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"142\"\u003e\n\u003cp\u003e0.009 \u0026plusmn; 0.007\u003c/p\u003e\n\u003cp\u003e0.049 \u0026plusmn; 0.042\u003c/p\u003e\n\u003cp\u003e(n = 10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003e0.02 \u0026plusmn; 0.01\u003c/p\u003e\n\u003cp\u003e4.32 \u0026plusmn; 1.85\u003c/p\u003e\n\u003cp\u003e(n = 22)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"82\"\u003e\n\u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003cp\u003e0.0019\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Correlation coefficient (Spearman \u003cem\u003er\u003c/em\u003e) between SUV\u003csub\u003emax\u003c/sub\u003e and K\u003csub\u003ei\u003c/sub\u003e in different groups\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u003cstrong\u003eSUV\u003csub\u003emax \u003c/sub\u003everse K\u003csub\u003ei\u003c/sub\u003e \u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003er\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"133\"\u003e\n\u003cp\u003e\u003cstrong\u003e95% confidence \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003einterval\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLNs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.858\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"133\"\u003e\n\u003cp\u003e0.815 to 0.892\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"151\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePTs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"125\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e0.952\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"133\"\u003e\n\u003cp\u003e0.916 to 0.973\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"114\"\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026nbsp;p\u003c/em\u003e \u0026lt; 0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eLNs\u003c/em\u003e\u003cem\u003e:l\u003c/em\u003e\u003cem\u003eymph nodes\u003c/em\u003e\u003cem\u003e, including BLNs and MLNs, \u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003ePTs:Primary tumors\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Quantitative analysis of malignant lymph nodes (MLN) and benign lymph nodes (BLN)\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e\u003cstrong\u003eK\u003csub\u003ei \u003c/sub\u003e(min\u003csup\u003e-1\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u003cstrong\u003eSUV\u003csub\u003emax\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003eMLNs (52)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e0.021 \u0026plusmn; 0.014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e4.35 \u0026plusmn; 2.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003eBLNs (133)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e0.006 \u0026plusmn; 0.004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e1.89 \u0026plusmn; 0.85\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ep\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e value\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e80.77\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e80.77\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003eSpecificity (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e89.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e87.22\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"132\"\u003e\n\u003cp\u003e\u003cstrong\u003eAccuracy (%)\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"127\"\u003e\n\u003cp\u003e90.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"113\"\u003e\n\u003cp\u003e88.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\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":"ESCC, MLN, BLN, 18F-FDG-PET/CT, quantitative analysis, glucose metabolic rate","lastPublishedDoi":"10.21203/rs.3.rs-127268/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-127268/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground\u003c/p\u003e\u003cp\u003eMost esophageal squamous cell carcinoma (ESCC) imaging diagnoses can be performed by routine CT and ultrasound, but it is difficult to detect metastatic lymph nodes or minor lesions. Functional imaging diagnosis based on \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT has potential advantages for detection of metastatic lymph nodes, or differentiation of benign from malignant lymph nodes, and for typing and staging of ESCC. The purpose of this study is to provide \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT imaging for ESCC patient to quantify the difference between malignant lymph nodes (MLN) and benign lymph nodes (BLN) for ESCC. \u003c/p\u003e\u003cp\u003eMethods\u003c/p\u003e\u003cp\u003eDynamic \u003csup\u003e18\u003c/sup\u003eF-FDG PET/CT was performed in 46 patients (26 patients without MLN (N0 stage) and 20 with MLN (non-N0 stage) who were pathologically confirmed for ESCC. Visual and quantitative differences were measured in primary tumor (PT), MLN and BLN regions of interest (ROIs). Finally, 52 MLN and 133 BLN (83 from N0 stage and 50 from non-N0 stage) were included for analysis. Pharmacokinetic analysis was performed by a Patlak model using Matlab program to obtain the influx constant (K\u003csub\u003ei\u003c/sub\u003e). Maximum standardized uptake value (SUV\u003csub\u003emax\u003c/sub\u003e) was also determined from the static and dynamic PET/CT scans. Based on the receiver operator characteristic (ROC) curve, the sensitivity and specificity for each parameter in differentiation diagnosis were evaluated. \u003c/p\u003e\u003cp\u003eResults\u003c/p\u003e\u003cp\u003eK\u003csub\u003ei\u003c/sub\u003e and SUV\u003csub\u003emax \u003c/sub\u003ein PT non-N0 group was slightly higher than in N0 groups (0.04\u0026nbsp;±\u0026nbsp;0.02 vs 0.03 ± 0.03, 8.01\u0026nbsp;±\u0026nbsp;3.90 vs 7.08\u0026nbsp;±\u0026nbsp;5.39, respectively), but with no significant difference (p \u0026gt; 0.05). And K\u003csub\u003ei \u003c/sub\u003eand SUV\u003csub\u003emax\u003c/sub\u003e in MLN were higher than BLN with statistically significant difference (K\u003csub\u003ei\u003c/sub\u003e\u003csup\u003eMLN\u003c/sup\u003e vs K\u003csub\u003ei\u003c/sub\u003e\u003csup\u003eBLN\u003c/sup\u003e\u0026nbsp;( 0.021 ± 0.014 vs 0.006 ± 0.004, p \u0026lt; 0.0001); (SUV\u003csub\u003emax\u003c/sub\u003e\u003csup\u003eMLN\u003c/sup\u003e vs SUV\u003csub\u003emax\u003c/sub\u003e\u003csup\u003eBLN\u003c/sup\u003e (4.35 ± 2.27 vs 1.89 ± 0.85, p \u0026lt; 0.0001); The sensitivity both Ki and SUV\u003csub\u003emax\u003c/sub\u003e were 80.77 %, the specificity for Ki was 89.47%, and SUV\u003csub\u003emax\u003c/sub\u003e 87.22% respectively. And the diagnostic accuracy Ki (90.61%) was slightly better than SUV\u003csub\u003emax\u003c/sub\u003e (88.16%). \u003c/p\u003e\u003cp\u003eConclusions\u003c/p\u003e\u003cp\u003e\u003cspan class=\"ql-cursor\"\u003e\u003c/span\u003eQuantitative parameters (both K\u003csub\u003ei\u003c/sub\u003e and SUV\u003csub\u003emax\u003c/sub\u003e)\u003csub\u003e \u003c/sub\u003eof \u003csup\u003e18\u003c/sup\u003eF-FDG in ESCC patients are sensitive diagnostic measurements capable to identify MLNs from BLNs\u003csub\u003e.\u003c/sub\u003e\u003c/p\u003e","manuscriptTitle":"Quantification of Malignant Lymph Nodes and Benign Lymph Nodes in Patients of Esophageal Squamous Cell Carcinoma With Dynamic 18F-FDG PET/CT","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2020-12-16 16:43:51","doi":"10.21203/rs.3.rs-127268/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":"d5e2f7aa-165c-4126-99ad-9aec67b5b396","owner":[],"postedDate":"December 16th, 2020","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":1503222,"name":"Cardiothoracic Surgery"}],"tags":[],"updatedAt":"2020-12-16T16:43:52+00:00","versionOfRecord":[],"versionCreatedAt":"2020-12-16 16:43:51","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-127268","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-127268","identity":"rs-127268","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}