Virtual Nodule Clinic for Pulmonary Nodules

Recruiting at 5 trial locations

Overseen By Vanderbilt-Ingram Services for Timely Access

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Vanderbilt-Ingram Cancer Center

Disqualifiers: Pregnancy, Active malignancy, Thoracic implants, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 3 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: Unknown

Duration: Up to 1 year

400 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial explores whether a new AI-based tool, the Virtual Nodule Clinic, can better manage potentially cancerous lung nodules. The tool uses smart imaging to predict if a nodule is cancerous, aiming to reduce unnecessary procedures and treatment delays. Participants will receive either this new AI assessment or standard care for their nodules. The trial is suitable for adults aged 35 and up with undiagnosed lung nodules between 8-30mm and a recent CT scan. As an unphased trial, it offers participants the chance to contribute to innovative research that could enhance future lung nodule management.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your doctor.

What prior data suggests that the Virtual Nodule Clinic is safe for managing indeterminate pulmonary nodules?

Research has shown that the Virtual Nodule Clinic helps doctors diagnose and treat lung cancer more accurately. It uses artificial intelligence (AI) to assess lung nodules, which are small spots in the lungs. The software provides a score to help determine if a nodule might be cancerous.

Healthcare settings have used the Virtual Nodule Clinic, indicating it is well-accepted. No reports of harmful effects have emerged from using the software. This tool aids doctors in making better decisions about managing lung nodules, potentially reducing unnecessary procedures.

Importantly, using this software does not involve taking any drugs or undergoing medical procedures. It serves as a tool for doctors to improve decision-making, focusing on enhancing care without introducing new medical risks. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the Virtual Nodule Clinic because it introduces a novel technique in managing pulmonary nodules, which could potentially revolutionize current standard care practices. Unlike traditional methods that rely solely on standard-of-care (SOC) CT evaluations, this approach includes a radiomic prediction score. This score uses advanced imaging analysis to provide a more detailed assessment of lung nodules, potentially leading to more personalized and accurate management decisions. By integrating cutting-edge technology into routine care, this method seeks to improve early detection and treatment outcomes for patients with pulmonary nodules.

What evidence suggests that the Virtual Nodule Clinic is effective for managing indeterminate pulmonary nodules?

Research has shown that AI tools like the Virtual Nodule Clinic, which participants in this trial may receive, can help doctors better predict if lung nodules are cancerous. Recent studies suggest these AI tools might be more accurate than current methods in assessing cancer risk. At institutions such as Atrium Health and Georgia Cancer Center, the Virtual Nodule Clinic has helped diagnose lung cancer earlier and with greater confidence. This tool uses an AI-powered score to analyze imaging data and estimate the chance of cancer. By improving nodule management, the Virtual Nodule Clinic aims to reduce unnecessary procedures and avoid treatment delays. ^{1 2 3 6 7}

Who Is on the Research Team?

Fabien Maldonado, MD

Principal Investigator

Vanderbilt University/Ingram Cancer Center

Are You a Good Fit for This Trial?

This trial is for individuals with lung nodules that are not clearly benign or malignant. It's designed to help manage these indeterminate pulmonary nodules by using an AI-based tool within the electronic health record system.

Inclusion Criteria

I have a CT scan of my nodule that is very detailed.

Referral includes direct in-basket messages in the electronic healthcare record (EHR) to study providers, telehealth visits, or clinic visit

I am 35 or older with a lung spot between 8-30mm that hasn't been diagnosed.

Exclusion Criteria

Pure ground glass nodule

More than 5 IPNs present on imaging

I have not had any cancer except for non-melanoma skin cancer in the last 5 years.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Treatment

Participants undergo standard of care CT evaluation and receive a Virtual Nodule Clinic radiomic prediction score, followed by standard lung nodule management

Up to 1 year

Follow-up

Participants are monitored for safety and effectiveness after treatment, including time to diagnosis and management changes

Up to 2 years

What Are the Treatments Tested in This Trial?

Interventions

• Virtual Nodule Clinic

Trial Overview

The study tests a Virtual Nodule Clinic, which uses artificial intelligence to evaluate lung nodules and predict their likelihood of being cancerous. This aims to reduce unnecessary invasive procedures and delays in treatment.

How Is the Trial Designed?

Group I: Arm I (Radiomic Prediction Score) Experimental Treatment 4 Interventions

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.

Group II: Arm II (Usual Care) Active Control 2 Interventions

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

Virtual Nodule Clinic is already approved in United States, European Union, United Kingdom for the following indications:

Approved in United States as Optellum Virtual Nodule Clinic for:

• Management of indeterminate pulmonary nodules

Approved in European Union as Optellum Virtual Nodule Clinic for:

• Management of indeterminate pulmonary nodules

Approved in United Kingdom as Optellum Virtual Nodule Clinic for:

• Management of indeterminate pulmonary nodules

Who Is Running the Clinical Trial?

Vanderbilt-Ingram Cancer Center

Lead Sponsor

Trials

221

Recruited

64,400+

National Cancer Institute (NCI)

Collaborator

Trials

14,080

Recruited

41,180,000+

Published Research Related to This Trial

An automated method combining diagnostic codes and natural language processing successfully identified 7112 patients with lung nodules, demonstrating high sensitivity (96%) and specificity (86%) compared to clinician review.

Among the identified patients, 14% were later diagnosed with lung cancer, highlighting the importance of this automated identification method in managing lung nodules in community settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Automated identification of patients with pulmonary nodules in an integrated health system using administrative health plan data, radiology reports, and natural language processing. Danforth, KN., Early, MI., Ngan, S., et al.[2022]

Indeterminate pulmonary nodules are common in asymptomatic individuals, and their management is often inconsistent due to varying guidelines and adherence issues.

Establishing a dedicated pulmonary nodule clinic can provide standardized, evidence-based care, utilizing multidisciplinary teams and advanced technology to improve patient outcomes and streamline the management process.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Systematic approach to the management of the newly found nodule on screening computed tomography: role of dedicated pulmonary nodule clinics. Korst, RJ.[2018]

A machine learning classifier called the malignancy Similarity Index (mSI) was developed using data from the National Lung Screening Trial and showed comparable accuracy to existing risk models for lung nodules, with an area under the curve of 0.89.

When combined with Lung-RADS, the mSI significantly improved sensitivity (by 25%-117%) and specificity (by 17%-33%) for lung nodule classification, potentially allowing for earlier diagnoses and reduced follow-up for 42% of malignant nodules detected in previous CT scans.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT. Adams, SJ., Madtes, DK., Burbridge, B., et al.[2023]

In a study of 53 patients with multiple pulmonary nodules, AI demonstrated an 88.8% agreement with postoperative pathological tests in identifying benign versus malignant lesions, highlighting its potential as a reliable diagnostic tool.

AI provided distinct probabilities of malignancy for different types of lesions, suggesting it can offer more accurate and objective assessments, which may improve treatment planning and follow-up for patients with pulmonary nodules.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Application of artificial intelligence in the diagnosis of multiple primary lung cancer. Li, X., Hu, B., Li, H., et al.[2020]

Pulmonary nodules are the most common incidental findings in chest imaging, especially with the increased use of CT scans, highlighting the need for careful assessment and management.

Established guidelines help differentiate between benign and potentially harmful nodules by evaluating their size, attenuation, and morphology, which are crucial for determining appropriate management strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Incidental Lung Nodules on Cross-sectional Imaging: Current Reporting and Management. Azour, L., Ko, JP., Washer, SL., et al.[2021]

Incidental pulmonary nodules are commonly found in CT scans, with rates as high as 48% in CT angiograms, highlighting the need for systematic management to prevent missed diagnoses of lung cancer.

The establishment of an incidental pulmonary nodule program improved the identification and follow-up of these nodules, ensuring better communication between radiologists, emergency department providers, and patients, which is crucial as the number of detected nodules is expected to rise with increased chest CT imaging.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Creating an Incidental Pulmonary Nodule Safety-Net Program. Van Gerpen, R.[2021]

An automated tool for identifying lung nodules was developed and validated using Electronic Healthcare Records from 14,586 patients, achieving high sensitivity (93%) and specificity (99%) for nodule identification, which can streamline healthcare service evaluations.

The tool demonstrated excellent inter-observer agreement (0.94 internally, 0.98 externally) and accurately predicted concerning nodule features with 94% accuracy, indicating its potential for improving the efficiency of lung nodule management in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre. Hunter, B., Reis, S., Campbell, D., et al.[2021]

The Virtual Lung Nodule Clinic (VLNC) evaluated 365 patients in its first year, primarily focusing on those at high risk for lung cancer, with 43.8% discharged after their initial assessment.

Analysis revealed that many discharged patients had previously received poor management, with 66.9% not adhering to follow-up guidelines, indicating a need for improved adherence to care protocols.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Multidisciplinary virtual management of pulmonary nodules. Polanco, D., González, J., Gracia-Lavedan, E., et al.[2022]

The 2017 Fleischner Society guidelines for managing incidental pulmonary nodules introduce a new size threshold for follow-up and emphasize the importance of nodule characteristics and patient risk factors, allowing for more personalized management decisions.

These updated guidelines may pose challenges for radiologists, as they require a more comprehensive assessment beyond just nodule size, including factors like growth patterns and morphologic features.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Updated Fleischner Society Guidelines for Managing Incidental Pulmonary Nodules: Common Questions and Challenging Scenarios. Bueno, J., Landeras, L., Chung, JH.[2022]

Low-dose computed tomography screening in adults aged 50 to 80 with a significant smoking history can reduce lung cancer mortality, highlighting its efficacy in early detection.

Management of detected pulmonary nodules involves risk stratification: solid nodules under 6 mm require surveillance, while those 8 mm or larger may need further assessment or biopsy, emphasizing the importance of tailored follow-up based on nodule size and risk factors.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Pulmonary Nodules: Common Questions and Answers. Langan, RC., Goodbred, AJ.[2023]

Citations

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optellum.com

optellum.com/transforming-lung-cancer-diagnosis-optellums-ai-powered-virtual-nodule-clinic-at-atrium-health/

Optellum's AI-powered Virtual Nodule Clinic at Atrium Health

Clinicians are using our AI-powered Virtual Nodule Clinic to help them diagnose and treat lung cancer earlier and assess cancer risk with greater confidence.

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pmc.ncbi.nlm.nih.gov

pmc.ncbi.nlm.nih.gov/articles/PMC10773382/

Determining the impact of an artificial intelligence tool on the ...

Recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models.

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optellum.com

optellum.com/case-study-georgia-cancer-center/

Case Study: Georgia Cancer Center

Implementing Optellum's Virtual Nodule Clinic (VNC) has significantly improved Georgia Cancer Center's ability to address diverse health challenges and ensure ...

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accessdata.fda.gov

accessdata.fda.gov/cdrh_docs/pdf20/K202300.pdf

Optellum Ltd. March 5, 2021 Mr. David Arrowsmith RA/QA ...

Virtual Nodule Clinic (VNC) is a software device used in the tracking, assessment and characterization of incidentally detected pulmonary ...

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clinicaltrials.gov

clinicaltrials.gov/study/NCT06638398

Biomarker Platform (Virtual Nodule Clinic) for the ...

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 ...

6.

optellum.com

optellum.com/lung-nodules-learning-center/

Lung nodules

Lung nodules or pulmonary nodules are small spots in the lung that are less than 3 cm in size and are often found during imaging scans.

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nhsaccelerator.com

nhsaccelerator.com/innovations/virtual-nodule-clinic-vnc/

Virtual Nodule Clinic (VNC) - AI-Driven Platform for Early ...

Optellum's Virtual Nodule Clinic (VNC) is an AI-enabled precision care platform focused on the early diagnosis and treatment optimisation of lung cancer.