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

NCT ID: NCT06725628

Last Updated: 2024-12-10

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

2000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2024-12-01

Study Completion Date

2024-12-01

Brief Summary

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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."

Detailed Description

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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.

Conditions

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Papillary Thyroid Carcinoma Lymph Node Cancer Metastatic

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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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).

Thy_CLNM_multi_omics

Intervention Type DIAGNOSTIC_TEST

Surgery

Interventions

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Thy_CLNM_multi_omics

Surgery

Intervention Type DIAGNOSTIC_TEST

Eligibility Criteria

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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.
Minimum Eligible Age

18 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Tianhan Zhou

OTHER_GOV

Sponsor Role lead

Responsible Party

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Tianhan Zhou

Clinical Professor

Responsibility Role SPONSOR_INVESTIGATOR

Central Contacts

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Tianhan Zhou

Role: CONTACT

Phone: 8615556960687

Email: [email protected]

Provided Documents

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Document Type: Study Protocol and Statistical Analysis Plan

View Document

Other Identifiers

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2022ZA119

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

2022KY153-CX1

Identifier Type: -

Identifier Source: org_study_id