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

DOI: https://doi.org/10.21203/rs.3.rs-127268/v1

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 18F-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 18F-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 18F-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 (Ki). Maximum standardized uptake value (SUVmax) 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

Ki and SUVmax 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 Ki and SUVmax in MLN were higher than BLN with statistically significant difference (KiMLN vs KiBLN ( 0.021 ± 0.014 vs 0.006 ± 0.004, p < 0.0001); (SUVmaxMLN vs SUVmaxBLN (4.35 ± 2.27 vs 1.89 ± 0.85, p < 0.0001); The sensitivity both Ki and SUVmax were 80.77 %, the specificity for Ki was 89.47%, and SUVmax 87.22% respectively. And the diagnostic accuracy Ki (90.61%) was slightly better than SUVmax (88.16%).

Conclusions

Quantitative parameters (both Ki and SUVmax) of 18F-FDG in ESCC patients are sensitive diagnostic measurements capable to identify MLNs from BLNs.

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 worldwide1. 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 China2. 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 missed3. 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 staging4. A radioactive tracer,2-[18F]fluoro-2-deoxy-dglucose (18F-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 metastases5. In malignancy, the uptake of 18F-FDG continues to increase for several hours after FDG injection whereas such prolonged period of 18F-FDG uptake is rare in inflammatory/infectious or normal tissues6-8. Shum et al ever assessed clinical usefulness of dual-time 18F-FDG PET/CT in ESCC, which turned out the sensitivity of 18F-FDG PET-CT in detecting the primary ESCC with combination of early SUVmax ≥ 2.5 or retention index (RI) ≥ 10% was 96.2%9. However, for loco-regional lymph node detection, there was no significant difference9. Dynamic 18F-FDG PET/CT allows quantitative assessment of lesion in vivo by using a Patlak model to obtain the influx constant (Ki)10-13. The purpose of this study is to quantify MLN and BLN in patients of ESCC with 18F-FDG PET/CT by applying both routing scan (SUVmax) and dynamic scans (Ki).

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 18F-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 18F-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 previously14,15. To simplify the quantification protocol, the influx constant (Ki) was calculated using Patlak linear regression analysis based on linear range selected in the time period of 40 to 60 min p.i..

SUVmax Standardised uptake value (SUV) of lesions on the frames at 55–60 min and 25-30 min were calculated. The SUVmax 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 (Ki and SUVmax) 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, Ki, SUVmax 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 Ki and SUVmax in MLNs were higher than BLNs with statistically significant difference (KiMLNs vs KiBLNs (0.021 ± 0.014 vs 0.006 ± 0.004, p < 0.0001); (SUVmaxMLNs vs SUVmaxBLNs (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 (Ki, SUVmax) 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 SUVmax and Ki, the correlation between SUVmax and Ki 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, SUVmax and Ki 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 Ki and SUVmax were 80.77 %, the specificity for Ki was 89.47% ) and SUVmax was 87.22% ). And the diagnostic accuracy Ki (90.61%) > SUVmax (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 18F-FDG PET/CT in patients with ESCC and the main results indicated that: 1) diagnostic parameters, including Ki and SUVmax in MLN were higher than in BLN, and the difference with statistically significant (p < 0.00001); 2) SUVmax and Ki was best correlated in both lymph nodes and primary tumors (r = 0.858 verse r = 0.952); 3) quantitative dynamic 18F-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 (SUVmax) is usually used as the parameter for PET semi-quantitative analysis in clinic to evaluate glucose metabolism of static imaging3,16,17. Pharmacokinetic analysis of dynamic PET/CT allows quantitative assessment of FDG influx constant (Ki) 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 SUVmax, Ki, 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 SUVmax and Ki 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 SUVmax was associated with the histopathological malignancy grade and differentiation.

The correlation between SUVmax and Ki 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 Ki (90.61%) was greater than SUVmax (88.16%) (Table 2, Fig. 4). Which may indicate Ki is a more sensitive diagnostic parameter in dynamic imaging than static modality (SUVmax). 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 18F-FDG PET/CT in patients with ESCC. Previous dynamic 18F-PET FDG study mainly focus on differentiation of benign from malignant primary lesions12,13,22,23, but rarely was related to MLN diagnosis. Yang M et al discussed dynamic 18F-FDG PET scans in 62 non-small cell lung cancer (NSCLC), and concluded that the dynamic modeling for MLN (Ki MLN) was more sensitive than the SUVmax to detect metastatic lymph nodes14. Lockau H et al underwent dynamic 18F-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 nodes20. 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 18F-FDG-PET did not predictably identify metastatic cervical lymph nodes24. 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 diagnosis12,18-20,22,23,25-27, 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 scanner18,28. 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 scan29.

Conclusions

Both dynamic and static PET/CT are capable to identify all primary ESCC lesions. Quantitative dynamic parameters of 18F-FDG in metastatic lymph nodes are higher than in benign lymph nodes, Ki may be an more important and sensitive diagnostic parameter in dynamic imaging than static SUVmax.

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.

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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

Ki

SUVmax

 

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

Ki

SUVmax

 

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

Ki

SUVmax

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

Ki

SUVmax

 

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 SUVmax and Ki in different groups

SUVmax verse Ki

 

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:lymph nodes, including BLNs and MLNs,

PTs:Primary tumors

Table 4 Quantitative analysis of malignant lymph nodes (MLN) and benign lymph nodes (BLN)

 

Ki (min-1)

SUVmax

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