To Establish a Molecular Typing System for Early Diagnosis of Lung Cancer

April 15, 2024 updated by: Singlera Genomics Inc.

Molecular Typing System for Early Screening and Diagnosis of Lung Cancer Combined With Liquid Biopsy Technology

This topic to take large multicenter study real world, the advanced liquid biopsy will ctDNA methylation detection technique is applied to pulmonary nodules differential diagnosis and early lung cancer screening, validation of early lung cancer screening and diagnosis of molecular classification system model, the feasibility of the development of early lung cancer screening and diagnosis of molecular classification system, improve its early screening early detection accuracy and efficiency, Improve the survival status of lung cancer high-risk population. At the same time, this project combined AI analysis technology of LDCT image results with ctDNA methylation detection, so as to overcome false negatives caused by the deficiency of ctDNA methylation detection technology in sensitivity, specificity, stability and flux, and correct false positive results that may be caused by AI analysis technology of LDCT image results. The combination of the two can avoid missed diagnosis and over - examination and over - treatment.

Study Overview

Status

Recruiting

Conditions

Detailed Description

  1. All patients underwent low-dose CT pulmonary nodule AI detection and peripheral blood ctDNA methylation detection at baseline
  2. Follow-up plan: Low-risk and medium-risk nodules and some high-risk nodules (5-10mm) were followed up. 10ml peripheral blood was collected from each follow-up and stored for testing until the end of the study. The high-risk nodules over 10mm were evaluated by the expert group and the patients were informed by biopsy or surgical resection. Histopathological diagnosis was made and compared with ctDNA methylation results to analyze the sensitivity and specificity of ctDNA methylation markers of lung cancer.
  3. Endpoint: Tissue samples were pathologically diagnosed as benign or malignant.

Study Type

Observational

Enrollment (Estimated)

600

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

Study Locations

    • Beijing
      • Beijing, Beijing, China, 100029
        • Recruiting
        • China-Japan Friendship Hospital
        • Contact:
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 75 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

600 patients who were newly diagnosed with pulmonary nodules confirmed by chest CT

Description

Inclusion Criteria:

  1. Patients with pulmonary nodules confirmed by chest CT are not limited to single nodules;
  2. Nodule diameter 5-30mm
  3. Nodules include solid, semi-solid and ground glass nodules;
  4. Age 18-75, no gender limitation;
  5. The newly diagnosed patients did not receive surgery, radiotherapy, chemotherapy, targeted therapy or other tumor-related interventions;
  6. Sign informed consent.

Exclusion Criteria:

  1. Patients with diagnosed lung cancer and extrapulmonary malignant tumor;
  2. Pulmonary sarcoidosis, pulmonary vasculitis, pulmonary tuberculosis;
  3. Patients with poor compliance are expected to be unable to complete follow-up according to the study protocol;
  4. Major trauma requiring blood transfusion occurred within one week before enrollment;
  5. Pregnant and lactation patients.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Low-risk group
Combined with AI calculation of malignant probability and ctDNA methylation results, patients were divided into three groups. The high probability of malignancy calculated by AI was defined as positive, and vice versa. The methylation markers detected in specific peripheral blood of lung cancer were defined as positive, and vice versa. Negative for both items was considered as low risk group. Follow-up was conducted according to The Chinese Expert Consensus on the Diagnosis and Treatment of Pulmonary Nodules (2018 edition). 10ml peripheral blood was collected from each follow-up and stored for testing until the end of the study.
medium-risk group
As above, one positive patient was considered to be in the medium-risk group and was reexamined every 6 months, with a total of 3 reexaminations expected
High-risk group
Same as above, both positive are considered high-risk group.Part of high-risk nodules (5-10mm) will be reviewed every 3 months for the above two examinations, which is expected to be reviewed 6 times in total. Biopsy or surgical resection of high-risk nodules over 10mm will be performed after evaluation by the expert group and the patient's knowledge, and histopathological diagnosis will be made and compared with ctDNA methylation results. To analyze the sensitivity and specificity of ctDNA methylation markers in lung cancer.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
To develop a molecular typing system for early screening and diagnosis of lung cancer
Time Frame: assessed up to 36 months
The feasibility of the molecular typing system model for early screening and diagnosis of lung cancer was verified through clinical studies, which significantly improved the accuracy and efficiency of early screening and early diagnosis, and improved the survival status of high-risk population of lung cancer.
assessed up to 36 months
AI technology was combined with ctDNA methylation detection technology
Time Frame: assessed up to 36 months
In addition to overcoming false negatives caused by deficiencies in sensitivity, specificity, stability and flux of ctDNA methylation detection technology, and correcting false positive results that may be caused by AI, the combination of the two can avoid missed diagnosis, over-examination and over-treatment.
assessed up to 36 months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Investigators

  • Principal Investigator: Rui Liu, Doctor, Singlera Genomics Inc.

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2020

Primary Completion (Estimated)

December 31, 2025

Study Completion (Estimated)

December 31, 2025

Study Registration Dates

First Submitted

June 20, 2022

First Submitted That Met QC Criteria

June 20, 2022

First Posted (Actual)

June 27, 2022

Study Record Updates

Last Update Posted (Actual)

April 17, 2024

Last Update Submitted That Met QC Criteria

April 15, 2024

Last Verified

April 1, 2024

More Information

Terms related to this study

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

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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