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

Assessing a Biomarker Platform for Stratifying Indeterminate Pulmonary Nodules - The SPOT IT Platform Trial

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Lung Neoplasm
• Intervention / Treatment: Procedure: Computed Tomography; Device: Diagnostic Procedure; Other: Best Practice; Other: Electronic Health Record Review

Detailed Description

PRIMARY OBJECTIVES:

I. To test the hypothesis that usual care plus a radiomic prediction score impacts patient management compared to usual care alone.

II. To conduct a multicenter pragmatic randomized controlled platform trial using a validated biomarker, the radiomic prediction score.

III. To conduct a biomarker study that will evaluate the first necessary (but not sufficient) step to show clinical utility.

IV. To assess the magnitude of change in patient management with use of the radiomic prediction score.

V. To develop a platform that can be used as framework for future larger biomarker studies.

OUTLINE: Patients are randomized to 1 of 2 arms.

ARM I: Patients undergo standard of care (SOC) computed tomography (CT) evaluation and receive a Virtual Nodule Clinic radiomic prediction score on study. Patients then receive SOC lung nodule management on study.

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

• Study Type: Interventional
• Enrollment (Actual): 400
• Phase: Not Applicable

Contacts and Locations

Study Locations

• United States
  • Colorado
    • Aurora, Colorado, United States, 80045
      • University of Colorado
  • Missouri
    • St Louis, Missouri, United States, 63110
      • Washington University in St. Louis
  • Tennessee
    • Nashville, Tennessee, United States, 37208
      • Meharry Medical College
    • Nashville, Tennessee, United States, 37203
      • Vanderbilt University/Ingram Cancer Center
    • Nashville, Tennessee, United States, 37212
      • VA Tennessee Valley Healthcare Center

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

Description

Inclusion Criteria:

• 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
• Available CT scan with slice thickness of 3 mm or less with the nodule of interest present. Nodules identified during screening low dose computed tomography of the chest (LDCT) that have had a conventional, follow-up CT performed are eligible for inclusion

Exclusion Criteria:

• 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

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: Randomized
• Interventional Model: Parallel Assignment
• Masking: None (Open Label)

Number of Arms

2

Arms and Interventions

Participant Group / Arm	Intervention / Treatment
Experimental: Arm I (Radiomic Prediction Score); 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; Undergo standard of care Computed Tomography; Device: Diagnostic Procedure; Receive a Virtual Nodule Clinic radiomic prediction score obtained in Optellum software.; Other: Best Practice; Receive standard of care lung nodule management; Other: Electronic Health Record Review; Ancillary Studies
Active Comparator: Arm II (Usual Care); Patients undergo SOC CT evaluation on study. Patients then receive SOC lung nodule management on study.	Procedure: Computed Tomography; Undergo standard of care Computed Tomography; Other: Best Practice; Receive standard of care lung nodule management

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Change in management	Change in management Will be based on intervention probability curve (IPC) assessment. 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 fit an intervention probability curve to both arms independently, using the method described by Kammer et al., by using the bootstrap-resample-with-dither approach. Then, will test the hypothesis that the slope of the IPC in the intervention arm is different from the slope of the IPC in the control arm by using the analysis of covariance, with a significance level of 0.05.	Up to 2 years
Unnecessary invasive procedures	Unnecessary invasive procedures Will be defined as: 1) invasive procedure with definite benign findings on biopsy or 2) non-diagnostic findings on biopsy but stable follow up imaging, evaluated over two years of follow-up. 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. Any invasive procedures will be compared across arms using a two-sided Z-test with pooled variances.	Up to 2 years
Time to diagnosis in patients with 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 be compared across arms using a logrank test	From the time of expert provider evaluation to the time of final diagnosis in days, assessed up to 2 years

Collaborators and Investigators

• Sponsor: Vanderbilt-Ingram Cancer Center
• Collaborators: National Cancer Institute (NCI)
• Investigators: Principal Investigator: Fabien Maldonado, MD, Vanderbilt University/Ingram Cancer Center

Study record dates

Study Major Dates

• Study Start (Actual): October 31, 2024
• Primary Completion (Estimated): January 30, 2027
• Study Completion (Estimated): April 30, 2027

Study Registration Dates

• First Submitted: October 9, 2024
• First Submitted That Met QC Criteria: October 9, 2024
• First Posted (Actual): October 15, 2024

Study Record Updates

• Last Update Posted (Actual): March 27, 2026
• Last Update Submitted That Met QC Criteria: March 25, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Neoplasms by Site; Neoplasms; Respiratory Tract Diseases; Lung Diseases; Respiratory Tract Neoplasms; Thoracic Neoplasms; Lung Neoplasms; Health Services Administration; Health Care Quality, Access, and Evaluation; Diagnosis; Quality of Health Care; Guidelines as Topic; Quality Assurance, Health Care; Practice Guidelines as Topic; Diagnostic Techniques and Procedures
• Other Study ID Numbers: VICC-IDTHO24059; 5R01CA252964-02 (U.S. NIH Grant/Contract); NCI-2024-08453 (Registry Identifier: NCI, Clinical Trials Reporting Program)

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: Yes
• product manufactured in and exported from the U.S.: No