Proteogenomics for Follicular Cell-derived Thyroid Cancer... | NCT06969768

Studies
Proteogenomics for Follicular Cell-derived Thyroid Cancer: Development of a Classification and Prognosis Prediction Model

NCT06969768

Sponsor

Seoul National University Hospital

Status

Completed

Last Update Posted

May 14, 2025Actual

Enrollment

275Actual

Phase

Not provided

Conditions

Thyroid Cancer

Interventions

Not provided

Countries

South Korea

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

May 2025

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Feb 21, 2018Actual

Primary Completion Date

Feb 21, 2021Actual

Completion Date

Feb 28, 2024Actual

First Submitted Date

Apr 30, 2025

First Submission Date that Met QC Criteria

May 12, 2025

First Posted Date

May 14, 2025Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

May 12, 2025

Last Update Posted Date

May 14, 2025Actual

Sponsor/Collaborators Module

Responsible Party, by Official Title

YoungJoo Park, Professor, Seoul National University HospitalPrincipal Investigator

Lead Sponsor

Seoul National University HospitalOTHER

Collaborators

Name	Class
Korea Health Industry Development Institute	OTHER_GOV
National Research Foundation of Korea	OTHER

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

Brief Summary

This study aims to refine the molecular classification of thyroid cancer (TC) using a multi-omics approach. By identifying a novel gene set and applying decision-tree modeling, the study seeks to improve diagnostic accuracy and predict tumor progression in BRAFV600E-like and RAS-like TC subtypes. Protein biomarkers were validated via immunohistochemistry (IHC), with findings confirmed across external datasets.

Detailed Description

Not provided

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

Retention

Enrollment

275Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
Thyroid cancer		All patients diagnosed with thyroid cancer who underwent thyroidectomy at Seoul National University Hospital

Interventions

Not provided

Outcomes Module

Primary Outcomes

Measure	Description	Time Frame
Classification Accuracy of Gene-Based Model	The accuracy of the decision-tree model using specific gene set to classify thyroid cancer into BRAFV600E-like, RAS-like, and NT (normal thyroid) -like subtypes. Model performance will be evaluated using accuracy, sensitivity, specificity, and Cohen's kappa.	1 year

Secondary Outcomes

Not provided

Other Outcomes

Not provided

Eligibility Module

Eligibility Criteria

Inclusion Criteria:

Patients with a confirmed clinical diagnosis of thyroid cancer
Availability of surgically resected thyroid tissue suitable for omics analysis

Exclusion Criteria:

- Patients who have received chemotherapy for other malignancies

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

Not provided

Standard Ages

AdultOlder Adult

Study Population

All patients diagnosed with thyroid cancer

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Seoul National University Hospital		Seoul			South Korea

Condition Browse Module

MeSH Terms

ID	Term
D013964	Thyroid Neoplasms

Ancestor Terms

ID	Term
D004701	Endocrine Gland Neoplasms
D009371	Neoplasms by Site
D009369	Neoplasms
D006258	Head and Neck Neoplasms

Browse Leaves

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Browse Branches

Not provided