Virtual Nodule Clinic for Pulmonary Nodules

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

Trial Summary

What is the purpose of this 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.

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 data supports the effectiveness of the Virtual Nodule Clinic treatment for pulmonary nodules?

Research shows that virtual nodule clinics, which use a team approach and advanced technology, provide high-quality care and help patients follow medical guidelines. Additionally, artificial intelligence (AI) has been effective in accurately diagnosing lung nodules, making it a useful tool in these clinics. ^{1 2 3 4 5}

Is the Virtual Nodule Clinic safe for humans?

The available research does not provide specific safety data for the Virtual Nodule Clinic or its variations, but it does highlight the importance of managing pulmonary nodules effectively to prevent delayed diagnoses of lung cancer, which suggests a focus on patient safety in its implementation. ^{2 6 7 8 9}

How is the Virtual Nodule Clinic treatment different from other treatments for pulmonary nodules?

The Virtual Nodule Clinic is unique because it uses artificial intelligence to manage pulmonary nodules virtually, offering a multidisciplinary approach that enhances guideline adherence and patient satisfaction, unlike traditional in-person methods. ^{2 3 6 9 10}

Research Team

Fabien Maldonado, MD

Principal Investigator

Vanderbilt University/Ingram Cancer Center

Eligibility Criteria

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

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

More than 5 IPNs present on imaging

Timeline

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

Treatment Details

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.

Participant Groups

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+

Findings from Research

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]

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]

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]

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]

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]

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]

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]

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]

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]

References

1. United States pubmed.ncbi.nlm.nih.gov

Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT. [2023]

2. Spain pubmed.ncbi.nlm.nih.gov

Multidisciplinary virtual management of pulmonary nodules. [2022]

3. United States 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. [2022]

4. United States 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. [2018]

5. Singapore pubmed.ncbi.nlm.nih.gov

Application of artificial intelligence in the diagnosis of multiple primary lung cancer. [2020]

6. Switzerland pubmed.ncbi.nlm.nih.gov

Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre. [2021]

7. United States pubmed.ncbi.nlm.nih.gov

Pulmonary Nodules: Common Questions and Answers. [2023]

8. United States pubmed.ncbi.nlm.nih.gov

Updated Fleischner Society Guidelines for Managing Incidental Pulmonary Nodules: Common Questions and Challenging Scenarios. [2022]

9. United States pubmed.ncbi.nlm.nih.gov

Creating an Incidental Pulmonary Nodule Safety-Net Program. [2021]

10. United States pubmed.ncbi.nlm.nih.gov

Incidental Lung Nodules on Cross-sectional Imaging: Current Reporting and Management. [2021]