Multiomics Study of Biological Behavior of Lymph Node Metastasis in Papillary Thyroid Carcinoma
Conditions: Papillary Thyroid Carcinoma, Lymph Node Cancer Metastatic
Sponsor: Tianhan Zhou

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Title: Multiomics Study of Biological Behavior 
of Lymph Node Metastasis in Papillary Thyroid 
Carcinoma 
 
NCT number: 
 
Date: 11/26/2024 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

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Protocol 
This protocol has been provided by the authors to give readers additional information 
about the work. 
 
Subtitle:Multi-omics integration of metabolomics, proteomics, 
ultrasound radiomics for prediction of central lymph node metastasis 
in stage T1 papillary thyroid carcinoma ：A real-world study 
 
version number：2.0 
 
version data：12/1/2024 
 
Research Centers： 
Hangzhou Traditional Chinese Medical Hospital  
 

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Summary of protocol 
Study name: Multi-omics integration of metabolomics, proteomics, ultrasound 
radiomics for prediction of central lymph node metastasis in stage T1 papillary thyroid 
carcinoma 
 
The expected total duration of the trial:2024.12-2026.12 
 
Study Objective 
Primary Objective: This study design aims to enhance the understanding of CLNM 
predictions using a multi-omics approach and aims to improve clinical decision-making 
in managing PTC patients. 
 
Study Design 
This study is a multicenter, observational cohort study aimed at assessing the accuracy 
and effectiveness of the ThyMPR-CLNM multi-omics model in predicting CLNM in 
patients diagnosed with stage T1 PTC. The design incorporates the following critical 
components: 
The study enrolled 2000 patients diagnosed with stage T1 PTC from Hangzhou 
Traditional Chinese Medical Hospital, affiliated with Zhejiang Chinese Medical 
University, between  Dec.2024 and Dec.2026. Fresh frozen tumor tissue, serum 
samples, and preoperative ultrasound images were collected from participants. These 
samples were utilized for comprehensive multi-omics analyses, including metabolomic 
and proteomic profiling, as well as ultrasound radiomic feature extraction. To minimize 
selection bias and balance covariates, propensity score matching was performed in two 
rounds, establishing a discovery set and a validation set with matched groups based on 
the propensity scores calculated through logistic regression. This ensured comparable 
groups for subsequent analyses. The study involved analyzing the collected samples 
through advanced techniques such as liquid chromatography-mass spectrometry (LC-
MS) for metabolomic and proteomic analyses, and Pyradiomics for extracting 
radiomics features from ultrasound images. Differentially expressed metabolites, 

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proteins, and radiomic features were identified and integrated for the development of 
the ThyMPR-CLNM prediction model. The Least Absolute Shrinkage and Selection 
Operator (LASSO) regression technique was utilized to construct the ThyMPR-CLNM 
model based on identified features from the multi-omics analyses. The model's 
performance was subsequently validated using an independent dataset. Statistical 
evaluations were performed using R software to determine the model's accuracy, 
sensitivity, specificity, and AUC values. Comparisons with conventional diagnostic 
methods were conducted to highlight the ThyMPR-CLNM model's advantages. 
 
 

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SOMMAIRE / TABLE OF CONTENT 
I．INTRODUCTION AND RATIONALE OF THE STUDY ........................................................ 5 
II．Study Objective .................................................................................................................................... 6 
II.1. Primary Objective .......................................................................................................................... 6 
II.2. Secondary Objective ..................................................................................................................... 6 
III. Methodology ......................................................................................................................................... 6 
III.1. Selection of patients ..................................................................................................................... 8 
III.1.1 Inclusion criteria .................................................................................................................... 8 
III.1.2 Exclusion criteria ................................................................................................................... 8 
III.2. Study termination and withdrawal criteria are as follows .............................................. 8 
III.3. Metabolomics and proteomics analyses .................................................................................. 9 
III.4. Habitat radiomics analysis ......................................................................................................... 8 
III.5. Surgical procedures .................................................................................................................... 10 
IV. Statistical methods ............................................................................................................................... 9 
IV.1. Sample size estimation ................................................................................................................ 9 
IV.2. Statistical Analysis ..................................................................................................................... 10 
IV.3. Statistical analysts ...................................................................................................................... 11 
V. Flow chart of the study implementation ......................................................................................... 12 
VI. The plan of study schedule............................................................................................................... 12 
VII. Ethics approval .................................................................................................................................. 13 
VII.1. Local institutional review boards or independent ethics committees ........................... 13 
VII.2. Responsibility of the investigators ........................................................................................ 13 
VII.3. Information and consent of participants ................................................................................ 4 
VII.4. Confidentiality of personal data ............................................................................................ 14 
VIII. Research management and quality assurance ........................................................................... 15 
VIII.1. Revised plan ............................................................................................................................. 15 
VIII.2. Case Report Form ................................................................................................................... 15 
VIII.3. Serious adverse event report ................................................................................................. 16 
VIII.4. Data Collection ........................................................................................................................ 17 
IX. Reference ......................................................................................................................................... 18 
 
 
 

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I．INTRODUCTION AND RATIONALE OF THE STUDY 
Thyroid cancer is one of common endocrine malignancies and its global incidence 
seems to be rising [1]. Papillary thyroid carcinoma (PTC), the predominant form, 
constitutes over 90% of all thyroid cancer cases [2]. Initially, Stage T1 PTC, commonly 
defined as a tumor less than 2 cm in size without extrathyroidal extension, comprises 
80% of PTC cases [3]. At diagnosis, 25-80% of this part of patients exhibit cervical 
lymph node metastasis (LNM), with central lymph node metastasis (CLNM) being 
notably prevalent [4-6]. A minority of patients with PTC develop locoregional 
recurrence, including cervical lymph node metastases, which eventually leads to 
mortality in some patients [7]. Conventionally, PTC patients presenting with clinical 
lymph node positivity (cN1) undergo thyroidectomy and therapeutic lymph node 
dissection [2]. However, the utility of prophylactic central neck dissection (pCND) in 
clinically lymph node-negative (cN0) patients remains a subject of debate. Guidelines 
from China and Asia-Pacific regions support pCND in cN0 PTC patients, referencing 
studies that detected occult LNM during dissections in these cases [8, 9]. In contrast, 
the 2015 American Thyroid Association (ATA) guidelines recommend against routine 
pCND in cN0 PTC [10]. This divergence in guidelines underscores the need for more 
evidence to inform surgical decisions on an individual basis. Preoperative, precise 
evaluation of CLNM is crucial to tailor appropriate treatment strategies for PTC, aiming 
to minimize risks such as recurrent laryngeal nerve damage and postoperative 
hypoparathyroidism, and to avert treatment delays or secondary surgery risks [11]. 
  Proteins, fundamental organic components of cells, play critical roles in biological 
processes [12]. Moreover, small molecules such as sugars, amino acids, and lipids, 
products of cellular metabolism, reflect the dynamic metabolic adaptations of living 
systems to various external stimuli or perturbations, including diseases, pharmaceutical 
interventions, dietary changes, or environmental conditions [13]. Recent advancements 
in proteomics and metabolomics have significantly enhanced the diagnosis and 
treatment of diseases [14]. Concurrently, radiomics, an emerging imaging analysis 
technique, has gained widespread application in tumor diagnostics, post-treatment 
evaluations, and prognostic assessments [15]. Furthermore, the integration of big data 

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and multi-omics approaches is increasingly supporting precision medicine. For instance, 
Vanguri et al. have provided a quantitative basis for utilizing multi-modal features 
through expert-guided machine learning to enhance the prediction of immunotherapy 
responses in patients with non-small cell lung cancer [16]. 
  In this study, we collected and analyzed metabolomic, proteomic, and ultrasound 
radiomic data from stage T1 PTC patients. We then developed a comprehensive multi-
omics model designed to predict CLNM in PTC patients. This non-invasive model 
facilitates the precise prediction of CLNM in low-risk PTC cases and provides critical 
guidance for surgical strategies in thyroid cancer management, thereby minimizing the 
risks associated with overtreatment or undertreatment. 
II．Study Objective 
II.1. Primary Objective  
The primary objective of this study is to evaluate the accuracy of the ThyMPR-CLNM 
model in predicting central lymph node metastasis (CLNM) in patients with stage T1 
papillary thyroid carcinoma (PTC).   
II.2. Secondary Objective 
The secondary objectives are to compare the performance of the ThyMPR-CLNM 
multi-omics model and evaluate differences in sensitivity, specificity and area under the 
receiver operating characteristic curve (AUC) values. 
III. Methodology 
This is a real-world retrospective cohort study. This study enrolled patients from 
Hangzhou TCM Hospital between August 2017 and June 2022. Preoperative serum 

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samples and ultrasound data were systematically collected from all participants. In the 
discovery set, fresh frozen tumor tissues were obtained during surgical procedures. 
 
III.1. Selection of patients 
III.1.1 Inclusion criteria 
(1) Pathological confirmation of PTC.  
(2) Patients who underwent primary surgery accompanied by central neck lymph node 
dissection. (3) Tumors measuring less than 2 cm in diameter. 
(4) Postoperative pathological reports including detailed information on the number of 
lymph nodes dissected and the number of metastatic lymph nodes.  
(5) Availability of comprehensive preoperative thyroid ultrasound images for analysis. 
III.1.2 Exclusion criteria 
(1) Postoperative pathological diagnosis indicating sub-types of PTC. 
(2) Tumor invasion into adjacent anatomic structures such as the sternothyroid 
muscle, surrounding soft tissues, trachea, esophagus, or laryngeal nerve.  
(3) History of neck trauma, previous tumor surgery, or adjuvant chemoradiotherapy. 
(4) Fewer than three lymph nodes dissected during surgery.  
(5) Concurrent acute inflammatory conditions or other hematologic disorders. 
 
III.2. Study termination and withdrawal criteria are as follows 
1）The inclusion criteria are not met after enrollment. 
2）The exclusion criteria are met after enrollment. 
3) The ultrasound image is unclear. 
4）Abnormalities in patient specimens, such as hemolysis or insufficient volume, etc. 
5) Patients cannot comply with the test procedure. 

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III.3. Metabolomics and proteomics analyses 
Each tumor sample and corresponding serum specimen were processed using pressure 
cycling technology (PCT). Metabolomic data were acquired through multiple reaction 
monitoring (MRM) mode analysis using triple quadrupole mass spectrometry, while 
proteomic data were gleaned via data-independent acquisition mass spectrometry 
(DIA-MS), with settings detailed in the supplementary materials. Metabolomic data 
were analyzed against a custom-built target standard product database, the Metware 
Database (MWDB), supplemented by a home-made metadata database and additional 
established metabolomic repositories such as MassBank, KNApSAcK, HMDB, and 
METLIN. Proteomic data were processed using DIA-NN software against a thyroid-
specific spectral library.  
 
III.4. Habitat radiomics analysis 
Color Doppler ultrasound machines equipped with a real-time, high-frequency (5-10 
MHz) linear-array probes are used in this study. Ultrasound images of all nodules are 
collected and stored according to the protocol specified in the guideline.  
The extracted data comprise a set of radiomic features, including first-order statistics, 
intensity histograms, shape and size statistics, and textured features, both raw and 
filtered. These handcrafted features are extracted using an in-house program 
implemented via Pyradiomics (http://pyradiomics.readthedocs.io). The K-means 
clustering algorithm stratifies the tumor voxel cluster into optimal spatial divisions 
across all modalities. For each spatial division, Pyradiomics facilitates the extraction of 
texture features specific to that habitat region. Features from any given modality 
represent its respective characteristics and are subsequently fused to enhance the data's 
robustness. 

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III.5. Surgical procedures 
All patients underwent open surgery where simultaneous thyroidectomy and cervical 
lymph node dissection were conducted. Central lymph nodes were consistently 
dissected as a standard procedure. Patients showing indications of lateral cervical 
lymph node metastases via fine-needle aspiration biopsy (FNA) or preoperative 
imaging underwent lateral cervical dissection. Critical structures, including the internal 
jugular vein, sternocleidomastoid muscle, and spinal accessory nerve, were preserved 
during the procedure. The number of dissected lymph nodes and identified metastases 
was meticulously quantified and independently assessed by two pathologists. 
IV. Statistical methods 
IV.1. Sample size estimation 
In this study, we enrolled a total of 1,062 patients diagnosed with papillary thyroid 
carcinoma (PTC) from Hangzhou Traditional Chinese Medical Hospital, affiliated with 
Zhejiang Chinese Medical University, between August 2017 and June 2022. To 
construct the study cohorts, we established a discovery set and a validation set using 
two rounds of propensity score matching (PSM). 
1. Initial Enrollment and Inclusion Criteria: 
• 
The initial sample consisted of 1,062 PTC patients. 
• 
Inclusion criteria were established to ensure that only patients meeting specific 
clinical and demographic characteristics were considered for the analysis. 
2. Propensity Score Calculation: 
• 
The propensity score for each patient was calculated using variables that were 
likely to influence the likelihood of central lymph node metastasis (CLNM) and 
treatment group assignment. This involved conducting a logistic regression 
analysis that included potential confounding factors, such as age, gender, tumor 
size, and other clinical characteristics. 

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3. Matching Process: 
• 
Propensity score matching was conducted in two rounds to balance the 
discovery and validation cohorts. 
• 
In the first round, patients were matched 1:1 based on their calculated propensity 
scores, aiming to achieve a balanced representation of both groups (e.g., CLNM 
positive versus negative patients). 
• 
The second round of matching further refined the cohorts by using specific 
thresholds (calipers) to ensure that matched patients were similar in their 
characteristics. 
4. Establishment of Discovery and Validation Sets: 
• 
Discovery Set: Following the propensity score matching, the first subset of 
patients designated as the discovery set contained a balanced number of patients 
with CLNM and non CLNM, allowing for model development and validation. 
• 
Validation Set: The-subsequent set served as an independent validation cohort, 
ensuring that the findings were robust and applicable across both groups. This 
set was essential for testing the predictive performance of the developed model. 
5. Final Sample Size: 
• 
The final count of patients included in the discovery and validation sets was 
determined after matching, ensuring a sufficient sample size for statistical 
power and generalizability of results. 
In summary, the approach to sample size estimation involved strategically enrolling a 
large cohort of PTC patients and applying propensity score matching to create balanced 
discovery and validation sets, ensuring the integrity and validity of the study's findings. 
 
IV.2. Statistical Analysis 
The statistical analyses were conducted using R software（version 4.3.1），with the 
utilization of various visual representation methods such as heatmaps, volcano plots, 
and receiver operating characteristic (ROC) curve plots. PSM analysis was conducted 

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to minimize the effects of potential confounders on selection bias. And PSM was 
performed as a 1:1 matching with a caliper value of 0.05. Differentially expressed 
metabolites (DEMs) and differentially expressed proteins (DEPs) were assessed 
utilizing the R package, with the significance threshold set at a P-value less than 0.05. 
The least absolute shrinkage and selection operator (LASSO) regression model was 
used on the discovery dataset training dataset for signature construction. Sensitivity, 
specificity, positive predictive value (PPV), and negative predictive value (NPV) were 
computed following standardized procedures, each accompanied by a 95% Wilson 
confidence interval.   
 
IV.3. Statistical analysts 
The members of the research team consist of Hangzhou Traditional Chinese Medical 
Hospital, Affiliated to Zhejiang Chinese Medical University, Hangzhou, China.  
 
 
 

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V. Flow chart of the study implementation  
 
 
 
VI. The plan of study schedule 
2024.12-2025.08 Include patients, fill out case report forms, collect clinical data. 
2025.08-2025.12 Patients are continued to be enrolled. 
2025.12-2026.8 Patients are continued to be enrolled and data analysis. 
2026.08-2026.12 Evaluate the diagnostic efficacy of ThyMPR-CLNM multi-omics 
model. 
 

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VII. Ethics approval  
Potential subjects will be fully informed of the risks and requirements of the study, and 
will be provided with any new information during the study period that may affect their 
continued participation in the study. They will be told that participation in the study is 
voluntary, that they can withdraw at any time without any reason, and that they will not 
be penalized or lose any due benefits. Only subjects who fully understand the risks, 
benefits, and potential adverse events of the study will be admitted after they voluntarily 
sign an informed consent form. 
 
VII.1. Local institutional review boards or independent ethics committees 
Protocol and informed consent must be reviewed and approved by a reasonably 
constituted independent ethics committee/institutional Review Committee (IEC/IRB) 
prior to trial initiation. A signed and dated IEC/IRB approval document for the protocol 
and informed consent must be submitted to the principal investigator prior to trial 
initiation. 
 
VII.2. Responsibility of the investigators 
The principal investigator of each establishment concerned promises to conduct the 
clinical trial in conformity with the protocol which has been approved by the competent 
authority.  
The principal investigator should not modify any aspect of the protocol without the 
approval of the proposed modifications by the competent authority.  
The Principal Investigator is responsible for:  
- providing the Sponsor with his/her CV as well as the investigators’ ones,  
-identifying members of his/her team participating in the trial and defining their 
responsibilities,  

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- recruiting patients after receiving the Sponsor’s approval 
- personally obtaining the informed consent form which has been dated and signed by 
the participant in the research prior to any specific trial selection procedure 
-  regularly completing the case report form (CRF) for each patient included in the trial 
- dating, correcting and signing the corrections on the CRF for each patient included in 
the trial 
- accepting regular visits from a CRA and possibly visits from auditors mandated by 
the Sponsor or inspectors from the regulatory authorities.  
All documentation concerning the trial (protocol, consent form, case report form, 
investigator file), as well as the original documents (laboratory results, imaging studies, 
medical consultation reports, clinical examination reports, etc.) is considered 
confidential and should be kept in a safe place. The Principal Investigator should keep 
data as well as a list of patient-identifying data for at least 10 years after the end of the 
study. 
 
VII.3. Information and consent of participants 
Prior to the conduct of any procedure linked to biomedical research, any person wishing 
to participate in a research study gives his/her free, informed and written consent. This 
consent is obtained once the participant has been informed by the investigator during a 
consultation and after the person had been given sufficient time to think it over.  
Having read the information notice the patient must date and sign the consent form) if 
he/she accepts to participate. This consent form must also be signed by the investigator. 
The original consent form must be kept in the study file by the investigator and the 
study participant should receive a copy. 
 
VII.4. Confidentiality of personal data 
   

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Personal data collected and processed by study subjects will be limited to data necessary 
for the purpose of the study. 
Adequate precautions must be taken to ensure confidentiality and comply with privacy 
laws and regulations when collecting and using such data.  Appropriate technical and 
organizational measures must be in place to protect personal data from unauthorized 
disclosure or access, or accidental loss or alteration. 
 
VIII. Research management and quality assurance 
 
VIII.1. Revised plan 
The principal investigator of each establishment concerned promises to conduct the 
clinical trial in conformity with the protocol which has been approved by the competent 
authority.  
The principal investigator should not modify any aspect of the protocol without the 
approval of the proposed modifications by the competent authority.  
 
VIII.2. Case Report Form 
All case report form recording, correction, and modification must be done by the 
investigator or other authorized study center personnel.  All case report forms and 
source documents supporting case report forms (e.g.  Informed consent, laboratory 
reports, treatment history, procedure records, physical examination and treatment 
results, diagnosis, follow-up results) must be kept in the files of the responsible 
investigator for at least 5 years upon receipt of notification from the sponsor that all 
studies have been discontinued and completed. 
 

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VIII.3. Serious adverse event report 
Fatal or life-threatening serious adverse events should be reported regardless of whether 
or not the study drug or the study process is causal.  It is the responsibility of 
investigator to inform the principal investigator and the ETHICS committee of serious 
adverse events. 
 
VIII.4. Data Collection 
Researchers must maintain adequate and accurate records to allow for complete 
archiving of studies and subsequent validation of data.  These documents will be 
divided into two distinct and independent categories: (1) investigator's study documents 
and (2) patient clinical source documents. 
The investigator's study documentation will include protocol/revision protocol, case 
report and inquiry form, Ethics approval, sample informed consent, surgical records, 
staff history, and other relevant documents. 
 
 
 

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IX. Reference 
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Statistical analysis plan –trial Final Version
1.Sample size justifications
The trial is designed as a diagnostic comparation trial to answer the question whether
the accuracy of thyroid Artificial Intelligence (AI) is non-inferior to that in fine needle
aspiration (FNA) biopsy group.
In a retrospective cohort, we perform a stratified test that the data is split into ten
sets. Then, the accuracy of each layer is calculated separately. Classification accuracy
is improved significantly as sample size increased initially, reaching above 70% at
349 samples, and then plateaued with a further increase of sample size.
In a prospective cohort, the sensitivity and specificity of AI are 0.96% and 89%,
and the sensitivity and specificity of control group are 87% and 96%, respectively.
Bilateral test A =0.05 and 90% certainty are set.
The sample size is calculated
according to the following sample size calculation formula:
N =2000 nodules are calculated, that is, 2000 nodules are required for each diagnostic
test. Meanwhile, the two diagnostic methods examine the same sample separately.
Considering15%~30% loss to follow-up and rejection, at least 2351~ 2426 cases are
included.
2.Statistical analyses
o Demographic and background characteristics
This project consists of two parts: a retrospective study and a prospective multicenter
study. Data from three centers are merged and analyzed as a single prospective dataset.
Demographic baseline characteristics will be summarized by a retrospective cohort
and a prospective cohort. Descriptive summary statistics will be provided for
continuous demographic and clinical variables. The descriptive summary statistics
will include the following: number of patients, means and standard deviation for

2
quantitative variables, and percentages for qualitative data.
o Populations
PTC patients underwent surgery from Hangzhou Traditional Chinese Medical Hospital, Affiliated to
Zhejiang Chinese Medical University. The inclusion and exclusion criteria were strictly defined as
follows: Inclusion Criteria: (1) pathological confirmation of PTC. (2) Patients who underwent primary
surgery accompanied by central neck lymph node dissection. (3) Tumors measuring less than 2 cm in
diameter. (4) Postoperative pathological reports including detailed information on the number of lymph
nodes dissected and the number of metastatic lymph nodes. (5) Availability of comprehensive
preoperative thyroid ultrasound images for analysis. Exclusion Criteria: (1) Postoperative pathological
diagnosis indicating sub-types of PTC. (2) Tumor invasion into adjacent anatomic structures such as
the sternothyroid muscle, surrounding soft tissues, trachea, esophagus, or laryngeal nerve. (3) History
of neck trauma, previous tumor surgery, or adjuvant chemoradiotherapy. (4) Fewer than three lymph
nodes dissected during surgery. (5) Concurrent acute inflammatory conditions or other hematologic
disorders. Preoperative plasma samples and ultrasound data were systematically collected from all
participants. In the discovery set, fresh frozen tumor tissues were obtained during surgical procedures. .
o Primary efficacy analysis
The statistical analyses were conducted using R software (version 4.3.1), with the utilization of various
visual representation methods such as heatmaps, volcano plots, and receiver operating characteristic
(ROC) curve plots. PSM analysis was conducted to minimize the effects of potential confounders on
selection bias. And PSM was performed as a 1:1 matching with a caliper value of 0.05. Differentially
expressed metabolites (DEMs) and differentially expressed proteins (DEPs) were assessed utilizing the
R package, with the significance threshold set at a P-value less than 0.05. The least absolute shrinkage
and selection operator (LASSO) regression model was used on the discovery dataset training dataset
for signature construction. Sensitivity, specificity, positive predictive value (PPV), and negative
predictive value (NPV) were computed following standardized procedures, each accompanied by a
95% Wilson confidence interval.

3
o Secondary outcomes analysis
Sensitivity and specificity will be compared between groups using a chi-square test.
The larger the κ coefficient, the higher the consistency of the test method with the
gold standard. Specifically, a κ coefficient ≥0.75 indicated high consistency; a κ
coefficient in the range of 0.40-0.75 indicated intermediate consistency, and a κ
coefficient <0.40 suggested low consistency. Ninety-five percent confidence intervals
(95%CIs) are reported for κ coefficient values. Confidence intervals were generated
by simulating 100 spatially random distributions with the same average molecular
density as the data regions. Data were compared using an independent samples t-test.
A DeLong test was used to compare the AUC between groups of diagnostic methods.