Biomarker Platform (Virtual Nodule Clinic) for the Management of Indeterminate Pulmonary Nodules

NCT06638398 | Active Not Recruiting DMC FDA Device

• 3 other identifiers: VICC-IDTHO240595R01CA252964-02NCI-2024-08453
• Study Type: interventional
• Target: 400
• Locations: 1 country
• Sites: 5

Brief Summary

This clinical trial studies whether a biomarker platform, the Virtual Nodule Clinic, can be used for the management of lung (pulmonary) nodules that are not clearly non-cancerous (benign) or clearly cancerous (malignant) (indeterminate pulmonary nodules \[IPNs\]). The management of IPNs is based on estimating the likelihood that the observed nodule is malignant. Many things, such as age, smoking history, and current symptoms, are considered when making a prediction of the likelihood of malignancy. Radiographic imaging characteristics are also considered. Lung nodule management for IPNs can result in unnecessary invasive procedures for nodules that are ultimately determined to be benign, or potential delays in treatment when results of tests cannot be determined or are falsely negative. The Virtual Nodule Clinic is an artificial intelligence (AI) based imaging software within the electronic health record which makes certain that identified pulmonary nodules are screened by clinicians with expertise in nodule management. The Virtual Nodule Clinic also features an AI based radiomic prediction score which designates the likelihood that a pulmonary nodule is malignant. This may improve the ability to manage IPNs and lower unnecessary invasive procedures or treatment delays. Using the Virtual Nodule Clinic may work better for the management of IPNs.

Conditions

Lung Neoplasm

Outcome Measures

Primary Outcomes (1)

• Appropriate nodule management

  Will be defined as referral for imaging surveillance for ultimately benign nodules, and biopsy, positron emission tomography scan, surgery or empiric stereotactic body radiation therapy for malignant nodules. Standard consort diagram will be created. Descriptive statistics including means, standard deviations, and median and interquartile ranges for continuous parameters, as well as percentages and frequencies for categorical parameters will be presented. Will compare the proportion with appropriate management across the two arms using a two-sided Z-test of proportions with pooled variances.

  Up to 1 Year

Secondary Outcomes (3)

• Change in management

  Up to 2 years

• Unnecessary invasive procedures

  Up to 2 years

• Time to diagnosis in patients with malignant nodules

  From the time of expert provider evaluation to the time of final diagnosis in days, assessed up to 2 years

Study Arms (2)

Arm I (Radiomic Prediction Score)

EXPERIMENTAL

Patients undergo SOC CT evaluation and receive a Virtual Nodule Clinic radiomic prediction score on study. Patients then receive SOC lung nodule management on study.

Procedure: Computed Tomography; Device: Diagnostic Procedure; Other: Best Practice; Other: Electronic Health Record Review

Arm II (Usual Care)

ACTIVE COMPARATOR

Patients undergo SOC CT evaluation on study. Patients then receive SOC lung nodule management on study.

Procedure: Computed Tomography; Other: Best Practice

Interventions

Diagnostic Procedure DEVICE

Receive a Virtual Nodule Clinic radiomic prediction score obtained in Optellum software.

Arm I (Radiomic Prediction Score)

Best Practice OTHER

Receive standard of care lung nodule management

Arm I (Radiomic Prediction Score); Arm II (Usual Care)

Electronic Health Record Review OTHER

Ancillary Studies

Arm I (Radiomic Prediction Score)

Computed Tomography PROCEDURE

Undergo standard of care Computed Tomography

Arm I (Radiomic Prediction Score); Arm II (Usual Care)

Eligibility Criteria

• Age: 35 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• Adults 35-year-old and older with undiagnosed IPN(s) 8-30mm referred for evaluation
• Referral includes direct in-basket messages in the electronic healthcare record (EHR) to study providers, telehealth visits or clinic visit
• For multiple nodules, we will obtain the score from the dominant or most suspicious nodule based on providers or radiologist impression

You may not qualify if:

• Pure ground glass nodule
• Patients known to be a prisoners
• Patients known to be pregnant
• Known active malignancy within the last 5 years at time of enrollment (excluding non-melanoma skin cancers)
• More than 5 IPNs present on imaging
• Nodules referred after initial LDCT for screening with only one LDCT available. The Lung Cancer Prediction Convolutional Neural Network (LCP CNN) algorithm is not currently validated for screening studies
• Thoracic implants that impact the image appearance of the nodule
• Clinician determines that use of the LCP CNN model is required or contraindicated for the optimal care of the patient

Sponsors & Collaborators

• Vanderbilt-Ingram Cancer Center: lead
• National Cancer Institute (NCI): collaborator

Study Sites (5)

University of Colorado

Aurora, Colorado, 80045, United States

Location

Washington University in St. Louis

St Louis, Missouri, 63110, United States

Location

Vanderbilt University/Ingram Cancer Center

Nashville, Tennessee, 37203, United States

Location

Meharry Medical College

Nashville, Tennessee, 37208, United States

Location

VA Tennessee Valley Healthcare Center

Nashville, Tennessee, 37212, United States

Location

MeSH Terms

Conditions

Lung Neoplasms

Interventions

Diagnostic Techniques and Procedures; Practice Guidelines as Topic

Condition Hierarchy (Ancestors)

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

Intervention Hierarchy (Ancestors)

Diagnosis; Guidelines as Topic; Quality Assurance, Health Care; Quality of Health Care; Health Services Administration; Health Care Quality, Access, and Evaluation

Study Officials

• Fabien Maldonado, MD

  Vanderbilt University/Ingram Cancer Center

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Professor of Medicine

Study Record Dates

• First Submitted: October 9, 2024
• First Posted: October 15, 2024
• Study Start: October 31, 2024
• Primary Completion (Estimated): January 30, 2027
• Study Completion (Estimated): April 30, 2027
• Last Updated: March 27, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share

Locations

US (5)