Liquid Biopsy With PET/CT Versus PET/CT Alone in Diagnosis of Small Lung Nodules

December 3, 2023 updated by: Rajesh Shah, Palo Alto Veterans Institute for Research

Liquid Biopsy in Combination With PET/CT Versus PET/CT Alone in Diagnosis of Small Lung Nodules

The purpose of this study is to determine if a liquid biopsy, a method of detecting cancer from a blood draw, combined with a PET/CT scan, a type of radiological scan, is better at determining whether a lung nodule is cancerous when compared to a PET/CT scan alone. A PET/CT scan is already used for diagnosis of lung nodules, but its efficacy is uncertain in nodules 6-20 mm in size. Therefore, the PET/CT will be evaluated for its diagnostic ability in lesions this size alone and in combination with a liquid biopsy. Secondarily, a machine learning model will be created to see if the combination of the PET/CT imaging data and the liquid biopsy data can predict the presence of cancer.

Study Overview

Status

Terminated

Conditions

Study Type

Observational

Enrollment (Actual)

31

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 Locations

  • United States
    • California
      • Palo Alto, California, United States, 94304
        • VA Palo Alto Health Care System

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 and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients with lung nodules found via imaging will be selected from the VA Palo Alto Health Care System.

Description

Inclusion Criteria:

  • Over 18 years of age
  • ECOG performance status of 0,1, or 2

  • Newly discovered lung nodule with:

    1. At least one solid lung nodule measuring 6 mm- 20 mm in greatest diameter detected on screening CT
    2. No single lesion larger than 20 mm in greatest diameter when multiple nodules present
    3. High- or intermediate-risk nodules per American College of Chest Physician (ACCP) guidelines
  • Able to provide informed consent

Exclusion Criteria:

  • Prior cancer of any type for the previous 3 years
  • Prior organ, bone marrow, of hematopoietic stem cell transplant
  • Contraindication to administration of [18F]FDG
  • Inability to undergo a PET/CT scan
  • Ongoing infection, either acute or subacute
  • Severe COPD or lung disease requiring home oxygen use
  • Pregnancy

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

  • Observational Models: Cohort
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Patients with Pulmonary Nodule
Patients at high-risk of lung cancer found to have a pulmonary nodule 6-20 mm in size.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Comparison of liquid biopsy with PET/CT to PET/CT alone for predicting lung cancer
Time Frame: 24 months
Determine the sensitivity and specificity of liquid biopsy and PET/CT in combination compared with PET/CT alone for predicting lung cancer for lesions 6 mm- 20 mm in size
24 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
ctDNA gene panel ability in detecting lung cancer
Time Frame: 24 months
Determine the sensitivity and specificity of a ctDNA gene panel in detecting lung cancer for lesions 6 mm- 200 mm in size
24 months
DNA methylation liquid biopsy ability in detecting lung cancer
Time Frame: 24 months
Determine the sensitivity and specificity of DNA methylation liquid biopsy in detecting lung cancer for lesions 6 mm- 20 mm in size
24 months
Validate ML classifier for predicting lung cancer
Time Frame: 30 months
Validate a machine learning (ML) classifier for predicting lung cancer using imaging and clinical data.
30 months

Collaborators and Investigators

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

Sponsor

Palo Alto Veterans Institute for Research

Investigators

  • Principal Investigator: Rajesh Shah, MD, VA Palo Alto Health Care System

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

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)

September 23, 2021

Primary Completion (Actual)

February 1, 2023

Study Completion (Actual)

February 1, 2023

Study Registration Dates

First Submitted

September 23, 2021

First Submitted That Met QC Criteria

September 23, 2021

First Posted (Actual)

October 4, 2021

Study Record Updates

Last Update Posted (Actual)

December 8, 2023

Last Update Submitted That Met QC Criteria

December 3, 2023

Last Verified

December 1, 2023

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • SHANEW_0001

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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