RECRUITING

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

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Study Overview

This clinical trial focuses on testing the efficacy of different digital interventions to promote re-engagement in cancer-related long-term follow-up care for adolescent and young adult (AYA) survivors of childhood cancer.

Description

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.

Official Title

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

Quick Facts

Study Start:2024-10-31

Study Completion:2027-12-01

Study Type:Not specified

Phase:Not Applicable

Enrollment:Not specified

Status:RECRUITING

Study ID

NCT06638398

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.

Ages Eligible for Study:35 Years

Sexes Eligible for Study:ALL

Accepts Healthy Volunteers:No

Standard Ages:ADULT, OLDER_ADULT

Inclusion Criteria	Exclusion 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	* 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

Inclusion Criteria

Exclusion 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

* 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

Contacts and Locations

Study Contact

Vanderbilt-Ingram Services for Timely Access

CONTACT

800-811-8480

cip@vumc.org

Principal Investigator

Fabien Maldonado, MD

PRINCIPAL_INVESTIGATOR

Vanderbilt University/Ingram Cancer Center

Study Locations (Sites)

Vanderbilt University/Ingram Cancer Center

Nashville, Tennessee, 37203

United States

Collaborators and Investigators

Sponsor: Vanderbilt-Ingram Cancer Center

Fabien Maldonado, MD, PRINCIPAL_INVESTIGATOR, Vanderbilt University/Ingram Cancer Center

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 Registration Dates

Study Start Date2024-10-31

Study Completion Date2027-12-01

Study Record Updates

Study Start Date2024-10-31

Study Completion Date2027-12-01

Terms related to this study

Additional Relevant MeSH Terms

Lung Neoplasm