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fmed-09-996127 November 29, 2022 Time: 7:40 # 1
TYPE Original Research
PUBLISHED 01 December 2022
DOI 10.3389/fmed.2022.996127
OPEN ACCESS
EDITED BY
Weiren Luo,
The Second Affiliated Hospital
of Southern University of Science
and Technology, China
REVIEWED BY
Norhafiza Mat Lazim,
Universiti Sains Malaysia (USM),
Malaysia
Ana Banko,
University of Belgrade, Serbia
*CORRESPONDENCE
Wen Liu
liuwen@sysucc.org.cn
Wencheng Tan
tanwch@sysucc.org.cn
†These authors share first authorship
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This article was submitted to
Translational Medicine,
a section of the journal
Frontiers in Medicine
RECEIVED 17 July 2022
ACCEPTED 13 October 2022
PUBLISHED 01 December 2022
CITATION
Mao M, Wang X, Seeruttun SR, Chi P,
Huang K, Liu W and Tan W (2022)
Recurrence risk stratification based on
Epstein–Barr virus DNA to identify
enlarged retropharyngeal lymph
nodes of nasopharyngeal carcinoma:
A model-histopathologic correlation
study.
Front. Med. 9:996127.
doi: 10.3389/fmed.2022.996127
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does not comply with these terms.
Recurrence risk stratification
based on Epstein–Barr virus
DNA to identify enlarged
retropharyngeal lymph nodes of
nasopharyngeal carcinoma: A
model-histopathologic
correlation study
Minjie Mao1,2†, Xueping Wang1,2†, Sharvesh Raj Seeruttun2,3†,
Peidong Chi1,2 , Kewei Huang1,2, Wen Liu1,2*and
Wencheng Tan2,4*
1Department of Laboratory Medicine, Sun Yat-sen University Cancer Center, Guangzhou,
Guangdong, China, 2State Key Laboratory of Oncology in South China, Collaborative Innovation
Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis
and Therapy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China, 3Department
of Gastric Cancer, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China,
4Department of Endoscopy, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, China
Background: Accurate assessment of the nature of enlarged retropharyngeal
lymph nodes (RLN) of nasopharyngeal carcinoma (NPC) patients after
radiotherapy is related to selecting appropriate treatments and avoiding
unnecessary therapy. This study aimed to develop a non-invasive and effective
model for predicting the recurrence of RLN (RRLN) in NPC.
Materials and methods: The data of post-radiotherapy NPC patients (N= 76)
with abnormal enlargement of RLN who underwent endonasopharyngeal
ultrasound-guided fine-needle aspirations (EPUS-FNA) were examined. They
were randomly divided into a discovery (n= 53) and validation (n= 23) cohort.
Univariate logistic regression was used to assess the association between
variables (magnetic resonance imaging characteristics, EBV DNA) and RRLN.
Multiple logistic regression was used to construct a prediction model. The
accuracy of the model was assessed by discrimination and calibration, and
decision curves were used to assess the clinical reliability of the model for the
identification of high risk RLNs for possible recurrence.
Results: Abnormal enhancement, minimum axis diameter (MAD) and EBV-
DNA were identified as independent risk factors for RRLN and could stratify
NPC patients into three risk groups. The probability of RRLN in the low-,
medium-, and high-risk groups were 37.5, 82.4, and 100%, respectively.
The AUC of the final predictive model was 0.882 (95% CI: 0.782–0.982)
in the discovery cohort and 0.926 (95% CI, 0.827–1.000) in the validation
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cohort, demonstrating good clinical accuracy for predicting the RRLN of NPC
patients. The favorable performance of the model was confirmed by the
calibration plot and decision curve analysis.
Conclusion: The nomogram model constructed in the study could be reliable
in predicting the risk of RRLN after radiotherapy for NPC patients.
KEYWORDS
nasopharyngeal carcinoma, retropharyngeal lymph nodes, recurrence, prediction
model, EBV
Introduction
Nasopharyngeal carcinoma (NPC) is a malignant
tumor originating from the nasopharyngeal epithelium.
It is usually associated with Epstein–Barr virus (EBV)
infection (1). Compared with other head and neck
primary malignancy, NPC has a high propensity to
metastasize to cervical lymph nodes, and enlarged neck
nodes are seen in a large fraction of patients (2). NPC
responds well to radiotherapy, but disease recurrence
and metastasis are still the bottlenecks that hinder
the cure rate of NPC (3–5). Luo et al. (6) found that
neoplastic spindle cells, which generates cancer stem cells
(CSCs) and Epithelial-mesenchymal transition (EMT)
properties, is likely to account for the predominant
clinical characteristics of this disease. Several reports have
showed the EBV oncoprotein LMP1 may be the dominant
cause of facilitating the histogenesis and aggressiveness
of spindle cells in NPC (7–9). The retropharyngeal
lymph node (RLN) is the most commonly involved first-
echelon lymph node region in NPC due to the extensive
nasopharyngeal lymphatic network (10). Approximately
50% of NPC patients have enlargement of RLN after
the completion of treatment (11,12). RLN metastasis in
NPC has an essential bearing on radiotherapy treatment
planning (13–15). According to the National Cancer
Comprehensive Network (NCCN) guidelines, surgery
is recommended as the primary choice for resectable
recurrent tumor lesions. RLN recurrence in NPC is very
difficult to remove via surgery because of its location
in the retropharyngeal and parapharyngeal space that
is closely related to cranial nerves, the internal jugular
vein, and the internal carotid artery. For recurrent RLN,
radiotherapy is commonly used. However, salvage radiotherapy
presents a high rate of severe complications, such as
massive nasopharyngeal hemorrhage, nasopharyngeal
mucosal necrosis, radiation encephalopathy, and trismus,
and a >50% rate of ≥grade three radiotoxicities (16,
17). Thus, it is crucial to confirm the nature of the
enlarged RLN in NPC after radiotherapy as it is
related to selecting appropriate treatments and avoiding
unnecessary therapy.
Magnetic resonance imaging (MRI) is critical for evaluating
malignant retropharyngeal lymph nodes (18–20). Previous
studies focused on the size of RLN metastasis diagnosis based
on a minimum axial diameter (MAD) of 5 mm (21–23). Zhang
et al. (24) and Li et al. (25) found that MAD >6 mm was a more
accurate prognosticator of RLN metastasis from NPC. However,
large nodes can be reactive and non-metastatic, while small
nodes can contain metastases and are difficult to resect (24). In
our previous study (26), we proposed a novel minimally invasive
technique termed endonasopharyngeal ultrasonography-guided
fine-needle aspiration (EPUS-FNA) for sampling tissues from
RLN. The aspiration smear was sent for aspiration cytology,
and the tissue was sent for pathological examination. It is an
effective method for diagnosing RLNs metastasis in patients
with NPC, but its invasiveness and associated trauma limit its
routine use. Therefore, more non-invasive and easy-to-used tool
for assessing RLN recurrence are urgently needed.
Growing evidence revealed that the plasma Epstein–
Barr virus DNA (EBV DNA) may be released from tumor
cells during the process of apoptosis or generated from
viral replication and different EBV antigens are expressed at
different stages of infection (27–32). Ma et al. (31) analyzed
the relationship between the plasma EBV DNA level with
the delineated tumor volume and with tumor metabolic
activity by PET/CT scan, and showed that plasma EBV
DNA level reflects overall tumor load. Another remarkable
finding is that, the levels of plasma EBV DNA in patients
with NPC recurrence were higher than the levels of those
who remained in continuous clinical remission (27,33).
Thus, measuring plasma EBV DNA has been shown to
provide an almost real-time readout for monitoring the
recurrence, prognostication, treatment response prediction
and disease surveillance of NPC (34–36). However, the
relationship between EBV-DNA and recurrent RLN has
not been reported.
In this present study, we aimed to develop a predictive
model using magnetic resonance imaging characteristics and
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EBV DNA for the identification of high risk RLNs for
possible recurrence.
Materials and methods
Patients
A total of 76 NPC patients with RLN enlargement
after treatment who underwent EPUS-FNA biopsy at the
Sun Yat-sen University Cancer Center (Guangzhou, China)
between April 2015 and December 2021 were selected. The
inclusion criteria were: (1) NPC patients without distant
metastasis, (2) had one or more enlarged RLNs assessed with
MRI during regular follow-up >6 months after the end of
radiotherapy, (3) absence of additional known head and neck
cancers or acute inflammation, (4) had no chemotherapy,
radiotherapy, immunotherapy, or salvage surgery between
the completion of radiotherapy and the MRI diagnosis of
suspicious RLN metastasis. Patients enrolled in the study
were randomly divided into a discovery (n= 53) and the
validation (n= 23) group. All patients provided written
informed consent. The Institute Research Ethics Committee of
the Sun Yat-sen University Cancer Center approved this study
(NO: SL-B2022-687-01).
Treatment
All eligible patients received radiotherapy as their
primary treatment with or without chemotherapy. The
target volume delineation was referred to the International
Commission Radiological Units Guidelines. Gross tumor
and RLN were included within the primary gross target
volume at our cancer center. The prescribed dose was
66–72 Gy for the primary target and 60–66 Gy for the
involved cervical lymph nodes. Five daily fractions per
week were given to the patients, and the radiotherapy
lasted for 6–7 weeks.
Diagnostic criteria for recurrence of
retropharyngeal lymph node
All patients underwent EPUS-FNA. If pathology or cytology
indicated cancer cell-negative, the patient were recommended
for a second EPUS-FNA. If the results were also negative, the
patients were then closely followed up every 3 months using
MRI. During follow-up, if a suspicious RLN was stable for
6 months after the latest EPUS-FNA, they were still regarded
as cancer cell-negative and underwent further follow-up. The
diagnostic criteria for RRLN in our study were defined as: an
RLN with pathologic or cytologic confirmation, or progressive
enlargement of RLN during MRI follow-up.
Magnetic resonance imaging protocol
and Epstein–Barr virus DNA
measurement
All patients underwent MRI with a 1.5-T system (GE
Discovery MR750; General Electric Healthcare, Waukesha,
WI, USA), with a head and neck-combined coil for the
nasopharyngeal scans. The same MR imaging sequences for all
patients were obtained, including axial, coronal and sagittal T1-
weighted and T2-weighted images before intravenous injection
of the contrast material (Magnevist; Bayer Schering Pharma,
Berlin, Germany). The images were assessed by two experienced
radiologists. Any disagreements were resolved by mutual
discussion. The section thicknesses and intersection gaps were
5 and 1 mm for the axial plane, and 6 and 1 mm for the
coronal and sagittal planes. The following MR features are
recorded: minimum axial diameter (MAD), lymph nodes signal,
irregular margin, central nodal necrosis (CNN), and abnormal
enhancement. MAD was measured at the widest diameter of
the lymph node on T1 axial images without fat suppression.
EBV DNA results were obtained within 1 month prior to EPUS-
FNA biopsy which was detected by fluorescence quantitative
PCR using a commercial kit (Targene, Anhui). Samples of
peripheral blood (3 ml) were collected in an EDTA tube
from all NPC patients and were centrifuged at 1,600 ×g
for 15 min for isolation of plasma and PBC. Plasma DNA
was extracted using the QIAamp Blood Kit (Qiagen, Hilden,
Germany) and a total of 500 µl of the plasma samples was
used for DNA extraction per column, and a final elution
volume of 50 µl was used to elute the DNA from the
extraction column. A real-time quantitative PCR system was
developed for plasma EBV DNA detection toward the BamHI-
W region of the EBV genome. The sequences consisted of
the amplification primers W-44F (50-AGT CTC TGC CTC
AGG GCA-30) and W-119R (50-ACA GAG GGC CTG TCC
ACCG-30) and the dual-labeled fluorescent probe W-67 T [50-
(FAM) CAC TGT CTG TAA AGT CCA GCC TCC(TAMRA)-
30] (37).
Statistical analysis
The Statistical Product and Service Solutions (SPSS,
ver. 22.0; IBM, Chicago, IL, USA) and R (ver. 3.5.11)
software were used for statistical analyses. The application
value of EBV DNA was evaluated by the area under the
receiver operating characteristic (ROC) curve. Univariate and
multivariate logistic regression analyses were used to analyze the
risk factors associated with RRLN. A nomogram was conducted
using statistically significant features identified in multivariate
1http://www.Rproject.org
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TABLE 1 Patient characteristics in the discovery and validation cohort.
Variable Total Discovery cohort Validation cohort P
(n= 76) (n= 53) (n= 23)
Age, median (IQR), years 46 (39–53) 45 (39–53) 47 (41–52) 0.46
Sex
Male 53 46 17 0.17
Female 23 7 6
T stage
T1 8 6 2 0.82
T2 12 9 3
T3 38 26 12
T4 16 10 6
Nstage
N0 9 4 5 0.12
N1 25 20 5
N2 28 21 7
N3 12 6 6
Clinical stage
I 2 1 1 0.46
II 9 8 1
III 37 26 11
IV 26 16 10
Retropharyngeal lymph node
Positive 57 40 17 0.89
Negative 19 13 6
Smoking
Yes 16 10 6 0.36
No 47 35 12
Alcohol behavior
Previous/Current 8 6 2 0.81
Never 55 39 16
Family history of cancer
Yes 13 8 5 0.35
No 51 38 13
EBV-DNA (copy/ml)
≤200 40 28 12 0.96
>200 36 25 11
EBER
Positive 52 37 15 0.76
Negative 8 6 2
RRLN, recurrence retropharyngeal lymph node; MAD, minimum axis diameter.
analysis. We tested the accuracy of the nomogram by graphical
calibration using the observed outcome plotted against the
predicted probability of the outcome obtained from the fitted
logistic regression model in both the discovery and validation
cohorts. Discrimination of the model was summarized using the
area under the ROC curve. Furthermore, we plotted decision
curves to assess the benefits of the nomogram-assisted decisions
in a clinical context. A two tailed P-value <0.05 was considered
statistically significant.
Results
Patient characteristics
The data of a total of 76 NPC patients were retrieved
and assessed. There were 53 patients in the discovery
cohort, and 40 (75.4%) had detectable RRLN. In the
validation cohort, 23 patients were screened, and 17
(73.90%) patients had detectable RRLN. The study
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TABLE 2 Univariate and multivariate logistic regression to predict RRLN.
Univariate analysis Multivariate analysis
Characteristics HR (95% CI) PHR (95% CI) P
Sex
Male/Female 2.118 (0.231 −19.438)0.499
T stage
T1-2/T3-T4 0.400 (0.076 −2.099)0.268
Nstage
N0-1/N2-3 0.752 (0.203 −2.782)0.669
Smoking
Yes/No 1.185 (0.208 −6.744)0.848
Alcohol behavior
Yes/No 1.500 (0.155 −14.557)0.725
Family history of cancer
Yes/No 0.931 (0.159 −5.446)0.937
Abnormal enhancement
Yes/No 10.500 (2.338 −47.153)0.001 6.767 (0.997 −45.933)0.05
Necrosis
Yes/No 0.968 (0.218 −4.287)0.966
Uneven signal
Yes/No 0.511 (0.142 −1.838)0.3
Irregular margin
Yes/No 0.923 (0.239 −3.564)0.908
MAD (mm)
≤6/>6 12.750 (2.952 −55.067) < 0.001 5.860 (1.091 −31.479)0.039
EBV-DNA (copy/ml)
≤200/>200 7.441 (1.455 −38.049)0.008 8.915 (1.125 −70.661)0.038
EBER
Positive/Negative 2.583 (0.383 −17.432)0.318
RRLN, recurrence retropharyngeal lymph node; MAD, minimum axis diameter.
population comprised of 53 males and 23 females with
NPC. The median age was 46 (range: 39–53) years.
Based on 8th edition of American Joint Committee
on Cancer TNM classification, there were two (2.63%)
patients classified as stage I, nine (11.84%) as stage
II, 37 (48.68%) as stage III, and 26 (34.21%) as stage
IV. There are no significant differences between the
two cohorts. Their basic characteristics are given in
Table 1.
Model construction
To build a predictive model for RRLN, we first
performed logistic regression analyses using the data of
patients from the discovery cohort to study the association
between clinical factors (demographics, imaging features,
and blood parameters) and RRLN diagnosis. Univariate
analysis showed that abnormal enhancement (P= 0.001),
MAD (P<0.001), and EBV DNA (P= 0.008) were
significantly associated with RRLN. From the variables
associated with RRLN in the univariate analyses, the final
predictive model was built using parameters abnormal
enhancement (P = 0.050, HR = 6.767), MAD (P= 0.039,
HR = 5.860), and EBV DNA (P= 0.038, HR = 8.915),
which were determined by multivariate analysis (Table 2).
Actually based on the nomogram (Figure 1A), the first
horizontal line represented the point values for each
variable in vertical line, then all the corresponding points
are summed to obtain the total points. Finally, from
the total points we could have got the probability of
RRLN. A nomogram was developed and three subgroups
for predicting RRLN, based on the total points, were
delineated: low risk (0–100), medium risk (100–200)
and high risk (>200). We tested comparing for the
three predefined subgroups of predicted RRLN in the
discovery and validation cohorts: low risk (37.5, 33.3%),
medium risk (82.4, 77.7%), and high risk (100, 100%) in
Figures 1B,C.
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FIGURE 1
Risk calculator for recurrence of retropharyngeal lymph node (RRLN) in nasopharyngeal carcinoma (NPC) patients with post-radiotherapy.
(A) Nomogram predicting the risk of RRLN. Patients can be divided into three groups based on their scores: low-risk group (total points: ≤100),
middle-risk group (total points: 100–200) and high-risk group (total points: >200). (B) Bar plot showing RRLN in the three predefined
subgroups of predicted outcomes from the discovery cohort, and (C) the validation cohort.
Assessment of prediction model
performance
A crucial part of statistical analysis is evaluating a model’s
quality and fit. We used multiple model quality parameters
to evaluate the fitting of the established prediction model to
the data. The six common evaluation parameters (AIC, BIC,
R2, R2_adj, RMSE, and Sigma) showed that our model was
the best fitting model for predicting RRLN, compared with
a single indicator (Figures 2A,B). Meanwhile, the calibration
plots also demonstrated good agreement between predictions
and actual observations in the both discovery and validation
cohorts (Figures 2C,D).
Clinical usefulness
Decision curve analysis revealed that the model were
applicable if the threshold probability of a patient is >10%
(Figures 3A,B). The gray line represents the assumption
that all patients are with RRLN, and the horizontal line
represents the assumption that patients are without RRLN.
DCA showed that the net benefit of the prediction model
was better than a single factor (abnormal enhancement,
MAD, and EBV DNA) in predicting RRLN in NPC both
in the discovery and validation cohorts. Figures 3C,D
illustrate that the model possesses promising accuracy for
predicting RRLN. The Harrell’s concordance index (C-index)
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FIGURE 2
Comparison of model indices from (A) the discovery cohort and (B) the validation cohort. Calibration curves of the model for recurrence of
retropharyngeal lymph node (RRLN) prediction from (C) the discovery cohort and (D) the validation cohort.
for the model to predict RRLN was 0.882 (95% CI 0.782–
0.982) for the discovery cohort and 0.926 (95% CI 0.827–
1.000) for the validation cohort. The detailed statistical
results of the models’ performance for predicting RRLN
(Table 3).
Discussion
To the best of our knowledge, this is the first study
focusing on MRI features, EBV level to predict RRLIN in
NPC patients, and further established a prediction model
consisting of abnormal enhancement, minimum axis diameter
(MAD), and EBV-DNA. Based on the proposed model,
we divided patients into three groups: low-risk (RRLN%:
37.5%), medium-risk (RRLN%: 82.4%), and high-risk (RRLN%:
100%) groups. Compared with using MAD only to predict
RRLN in the previous study, our model exhibited promising
discrimination ability with a C-index of 0.882 (95% CI:
0.782–0.982) and may assist in determining the nature of
abnormal enlarged RLN.
Intensity-modulated radiotherapy (IMRT) is the
primary treatment modality as NPC is highly sensitive
to ionizing radiation (1,38,39). However, around
10% of patients either have residual disease or would
develop recurrence at the primary and/or regional site
after IMRT treatment (40,41). Early detection of RRLN
during follow-up in patients with NPC has been shown
to improve survival with salvage therapy (surgery or
radiotherapy) (42,43). Several recent internationally
consensus guidelines have standardized the contouring
of target and adjacent organs and dose prioritization and
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FIGURE 3
Decision curve analysis demonstrating the clinical utility in predicting recurrence of retropharyngeal lymph node (RRLN) of the model from (A)
the discovery cohort and (B) the validation cohort. ROC curves demonstrating the accuracy for predicting RRLN from (C) the discovery cohort
and (D) the validation cohort.
TABLE 3 Performance of model for predicting RRLN.
Cohort AUC (95% CI) SEN (%) SPE (%) ACC (%) PPV (%) NPV (%)
Discovery 0.882 (0.782–0.982) 85.0 76.9 84.9 91.9 62.5
Validation 0.926 (0.827–1.000) 88.2 66.7 82.6 88.2 66.7
RRLN, recurrence retropharyngeal lymph node; AUC, area under the receiver operating curve; CI, confidence interval; SEN, sensitivity; SPE, speci-ficity; ACC, accuracy; PPV, positive
predictive value; NPV, negative predictive value; US, ultrasound; PE, physical examination.
acceptance criteria for radiotherapy planning (44–46).
However, considering the potentially severe complications
of salvage treatment, assigned based on minimum
axial diameter for identifying RLN metastasis has been
debated. Therefore, it is necessary to identify significant
features of RRLN to improve the quality of clinical
decision-making.
It is currently accepted that MRI can provide superior
soft-tissue contrast compared to CT and is better for
differentiating between primary tumor extension and
RLN metastasis than computed tomography (CT) (47).
In addition, metastatic RLN lesions could shrink after the
completion of RT, while the sizes of reactive nodes would
usually stay stable (24). Therefore, MRI minimal axial
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diameter could be a reliable method for evaluating RRLN
in NPC patients after treatment. Lam et al. (48) proposed
a radiologic MAD of 5 mm to identify metastatic RLN.
To improve the accuracy of RRLN, some investigators
have suggested raising the cut-point value of MAD from
5 to 6 mm (24,25). In our study, the most accurate
size criterion of RRLNs was a MAD of 6 mm, resulting
in an accuracy of 76.3%. Our results differ from the
previous findings (24) suggesting that node with central
necrosis was not associated with RRLN (P= 0.966)
mainly because lymph node necrosis are seldom seen
with RLN, particularly with small nodes. On the other
hand, multivariate logistic regression showed that abnormal
enhancement signals were independent predictors of RRLN
with a diagnostic sensitivity of 85%. Structural changes
of RRLN became evident on postinjection T1-weighted
images, and nodal involvement on contrast enhanced MR
lymphangiograms were characterized with partial or marginal
enhancement of the node, indicating partial occupation by the
tumor (49).
Plasma EBV DNA could also be used as a complementary
surveillance method to conventional imaging for monitoring
failure (1). A prospective multicenter clinical trial
confirmed that post-radiotherapy plasma EBV DNA
levels correlated significantly with the corresponding
hazards of locoregional failure and distant metastasis
(50). Our study revealed that high levels of EBV DNA
(>200 copy/ml) were associated with the occurrence of
RRLN and improved the diagnostic efficacy of radiology
for RRLN with AUC from 0.798 to 0.882. The plasma
EBV DNA is mainly derived from tumor cells, appears
to correlate closely with the presence of residual tumors.
Li et al. (51) proposed that ultrasound-guided cervical
lymph node (CLN) FNA detection of EBV concentration
may provide a new method with high sensitivity and
specificity for the diagnosis of CLN metastasis. However,
there are still some controversies, including the effects
of sample collection, horizontal cutting, and plasma
EBV. The results of our study show that no difference
(P= 0.318) in EBER between patients with RRLN–
positive and RRLN–negative disease. This result may be
due to the complexity of EBV kinetics, which affects the
distribution, persistence, and interchange of EBV among
plasma and tissue (52). Plasma EBV DNA for predicting
RRLN in NPC may be related to the ability of the virus
to regulate cellular signaling pathways, block antiviral
cytokines, and regulate immunosuppressive biological
molecules to resist and escape host immunity (53). As
a result, detection of EBV DNA provides an almost
real-time readout of the tumor burden, and is useful for
predicting RRLN in NPC.
The present investigation had some limitations that warrant
consideration. First, the model was established based on
a small sample size from a single-center and some cases
were excluded because of incomplete information, which
may have resulted in certain level of selection bias. Second,
for patients with negative pathological results at the EPUS-
FNA, they were only followed up for 6 months and
were classified as stable non-recurrent lymph nodes, which
may have led to mis-grouping for slow-growing tumors.
Third, several novel imaging techniques such as ultrasound
elastography and contrast-enhanced ultrasonography were not
assessed and their adaptation may improve the prediction
models in the future.
Conclusion
This is the first study to investigate the correlation
between radiologic-EBV DNA and RLN histopathologic.
We also propose a predictive clinical model for the
risk stratification of RRLN in NPC. This prediction
tool may help identify RRLN high-risk patients who
could benefit from needle biopsy or more aggressive
follow-up scheme to improve their chances for
longer survival.
Data availability statement
The authenticity of this article has been validated by
uploading the key raw data onto the Research Data Deposit
public platform (www.researchdata.org.cn), with the approval
RDD number as RDDA2022117093.
Ethics statement
The studies involving human participants were reviewed
and approved by the Institute Research Ethics Committee of
the Sun Yat-sen University Cancer Center (No: SL-B2022-687-
01). Written informed consent to participate in this study was
provided by the participants’ legal guardian/next of kin. Written
informed consent was obtained from the individual(s), and
minor(s)’ legal guardian/next of kin, for the publication of any
potentially identifiable images or data included in this article.
Author contributions
WT and MM contributed to the conception and
design of the study and drafted the manuscript. XW,
SS, and WL contributed to the data analysis and
interpretation. PC and KH participated in data collection
and literature research. All authors read and approved the
final manuscript.
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Funding
This work was supported by the National Natural Science
Foundation of China (Grant No. 82073283), the Guangzhou
Science and Technology Plan Project (No. 202201011366),
and the Guangdong Medical Scientific Research Fund (Grant
No. A2019390). The funders had no influence on the
outcomes of this study.
Acknowledgments
We thank the staff of the Biochemical Laboratory
of Sun Yat-sen University Cancer Center who provided
various biochemical markers and all of the staff who
supported our study.
Conflict of interest
The authors declare that the research was conducted
in the absence of any commercial or financial relationships
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