AI-Driven Precision Diagnostic System for Lung Cancer Based on Liquid Biopsy

NCT07009769 | Completed DMC

• 1 other identifier: 2024GR0131
• Study Type: observational
• Target: 865
• Locations: 1 country
• Sites: 1

Brief Summary

The investigators aim to develop an AI-powered precision medicine system based on liquid biopsy that can comprehensively provide lung cancer patients with information on disease stage, histologic subtype, and presence of genetic mutations.

Conditions

Lung Cancer

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy of the AI-based classification model using SERS signals from plasma-derived exosomes

  To evaluate the diagnostic performance (sensitivity, specificity, and overall accuracy) of a machine learning model trained on SERS spectral data of plasma-derived exosomes in distinguishing lung cancer patients from healthy controls.

  Retrospective, based on pre-collected samples through study completion, an average of 1 year

Eligibility Criteria

• Age: 40 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

Adult lung cancer patients (≥18 years old) and healthy controls, all South Korean nationals, with plasma collected before any cancer treatment.

You may qualify if:

• Republic of Korea nationality
• Age ≥ 30 years
• Lung cancer based on radiologic and/or histopathologic examination
• No prior diagnosis of any other cancer and no history of anticancer treatment before the date of blood sample collection

You may not qualify if:

• History of other cancers before blood collection
• Prior cancer treatment before blood collection
• Incomplete clinical data
• No consent for sample use
• Republic of Korea nationality
• No history of any cancer
• Age ≥ 30 years
• History of any malignancy
• Incomplete clinical information or no consent for sample use

Sponsors & Collaborators

• Korea University Guro Hospital: lead

Study Sites (1)

Korea University Guro Hospital

Guro-gu, Seoul, 08308, South Korea

Location

Biospecimen

Retention: SAMPLES WITHOUT DNA

Blood plasma

MeSH Terms

Conditions

Lung Neoplasms

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor

Study Record Dates

• First Submitted: May 29, 2025
• First Posted: June 8, 2025
• Study Start: October 16, 2024
• Primary Completion: December 31, 2024
• Study Completion: May 1, 2025
• Last Updated: June 8, 2025

Record last verified: 2025-06

Locations

KR (1)