Multiomics Study of Biological Behavior of Lymph Node Metastasis in Papillary Thyroid Carcinoma

Establish a predictive model for assessing neck lymph node metastasis of papillary thyroid carcinoma based on metabolomics, proteomics, and imaging omics data, exploring an ideal protocal for the precise diagnosis and treatment of papillary thyroid carcinoma."

Study Overview

• Status: Not yet recruiting
• Conditions: Papillary Thyroid Carcinoma; Lymph Node Cancer Metastatic
• Intervention / Treatment: Diagnostic test: Thy_CLNM_multi_omics

Detailed Description

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, 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.

• Study Type: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Tianhan Zhou; Phone Number: 8615556960687; Email: 201911122511374@zcmu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

PTC patients underwent surgery in Hangzhou Traditional Chinese Medical Hospital

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
LNM group and NLNM group; 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).	Diagnostic test: Thy_CLNM_multi_omics; Surgery

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Lymph node metastasis status	Patients with thyroid cancer undergo thyroid cancer radical surgery according to the guidelines, with pathological results including the number of lymph node metastases and metastasis status, based on the final pathological diagnosis.	Record lymph node metastasis status until the end of the surgery, followed by a one-year follow-up until the conclusion of the study.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Surgical complications and postoperative recurrence	Hemorrhage，Infection，Recurrent laryngeal nerve injury，Hypothyroidism，Other related surgical complications and Recurrence status	Record surgical complications until the end of the surgery, followed by a one-year follow-up until the conclusion of the study.

Collaborators and Investigators

• Sponsor: Tianhan Zhou

Study record dates

Study Major Dates

• Study Start (Estimated): December 1, 2024
• Primary Completion (Estimated): December 1, 2024
• Study Completion (Estimated): December 1, 2024

Study Registration Dates

• First Submitted: November 22, 2024
• First Submitted That Met QC Criteria: December 4, 2024
• First Posted (Estimated): December 10, 2024

Study Record Updates

• Last Update Posted (Estimated): December 10, 2024
• Last Update Submitted That Met QC Criteria: December 4, 2024
• Last Verified: December 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Endocrine System Diseases; Pathologic Processes; Neoplasms by Site; Neoplasms; Neoplasms by Histologic Type; Endocrine Gland Neoplasms; Head and Neck Neoplasms; Neoplasms, Glandular and Epithelial; Adenocarcinoma; Neoplastic Processes; Neoplasm Metastasis; Adenocarcinoma, Papillary; Thyroid Cancer, Papillary; Thyroid Diseases; Carcinoma; Thyroid Neoplasms; Lymphatic Metastasis
• Other Study ID Numbers: 2022KY153-CX1; 2022ZA119 (Other Grant/Funding Number: Huang Hai)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No