Computer-Aided Diagnosis Tool for Lung Nodules

(ARCADES Trial)

Recruiting at 2 trial locations

Overseen By Anil Vachani, MD, MSCE

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Abramson Cancer Center at Penn Medicine

Disqualifiers: Lung cancer, Active cancer, others

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

Approved in 2 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: Unknown

300 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial aims to evaluate how a new computer-aided diagnosis tool assists doctors in managing lung nodules, small growths in the lungs that could be cancerous. The trial compares regular care with an added AI tool, the Optellum Virtual Nodule Clinic, which predicts lung cancer risk. It seeks participants who have recently discovered lung nodules measuring 8-30mm and have undergone a compatible chest CT scan. As an unphased trial, this study offers participants the chance to contribute to innovative research that could enhance lung cancer diagnosis.

Do I need to stop my current medications for the trial?

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 this computer-aided diagnosis tool is safe for evaluating pulmonary nodules?

Research has shown that the Optellum Virtual Nodule Clinic functions primarily as a software tool. It assists doctors in identifying and evaluating lung nodules, which are small growths in the lungs. Specific safety data for this tool is not available. However, as a computer program, it does not involve medication or medical procedures, typically resulting in fewer safety concerns compared to traditional treatments. Since this tool is used for diagnosis and planning rather than direct treatment, the emphasis is on its accuracy and effectiveness rather than safety. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the Optellum Virtual Nodule Clinic because it leverages artificial intelligence to enhance the diagnosis of lung nodules. Unlike the traditional approach where clinicians rely solely on their assessment, this tool provides a Lung Cancer Prediction report using advanced radiomics. This AI-driven method aims to improve risk stratification, potentially leading to more accurate and earlier detection of lung cancer. This innovative approach represents a significant advancement in how lung nodules are evaluated, offering the potential for improved outcomes for patients.

What evidence suggests that this computer-aided diagnosis tool is effective for managing pulmonary nodules?

Research has shown that the Optellum Virtual Nodule Clinic, a computer tool, effectively manages lung nodules. This trial will compare two approaches: one group will receive usual care with clinician assessment, while another group will receive clinician assessment enhanced by the Optellum Virtual Nodule Clinic's CAD-based risk stratification. This tool uses artificial intelligence to help differentiate between harmless and cancerous nodules. Studies have demonstrated that it improves doctors' ability to predict lung cancer risk in these nodules. The FDA has tested and approved the tool for this use. By providing a clearer view of nodules, it aims to improve early detection and treatment of lung cancer. ^{1 3 6 7 8}

Who Is on the Research Team?

Roger Y. Kim, MD, MSCE

Principal Investigator

University of Pennsylvania

Are You a Good Fit for This Trial?

Adults aged 35-89 with newly discovered solid pulmonary nodules (8-30mm) on CT scans, scheduled for evaluation at a pulmonary nodule clinic. Participants must have CT imaging compatible with the Optellum Virtual Nodule Clinic software available by their first clinic visit.

Inclusion Criteria

Chest CT imaging compatible with Optellum Virtual Nodule Clinic software

Scheduled to be evaluated at a pulmonary nodule clinic

I am between 35 and 89 years old.

Exclusion Criteria

I have had a low-dose CT scan of my chest.

I have had lung cancer in the past.

I have an implant in my chest that makes it hard to see my phrenic nerve.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Randomization and Initial Assessment

Participants undergo 1:1 randomization to either usual care or CAD-based risk stratification, followed by initial assessment of pulmonary nodules

1-2 weeks

Follow-up

Participants are monitored for safety and effectiveness of the management strategy, including imaging surveillance and biopsy outcomes

12 months

What Are the Treatments Tested in This Trial?

Interventions

• Optellum Virtual Nodule Clinic

Trial Overview This trial is testing the impact of a radiomics-based computer tool called Optellum Virtual Nodule Clinic on doctor's decisions in managing lung nodules, compared to standard care without this tool. Patients are randomly placed into one of these two groups.

How Is the Trial Designed?

Group I: Clinician assessment + CAD-based risk stratification Experimental Treatment 1 Intervention

In the experimental arm, evaluating clinicians will receive a Lung Cancer Prediction report from an artificial intelligence radiomics-based computer-aided diagnosis tool for risk stratification of pulmonary nodules.

Group II: Usual care (clinician assessment) Active Control 1 Intervention

In the usual care arm, clinicians will evaluate individuals with indeterminate pulmonary nodules as part of routine clinical care. No specific guidance regarding pulmonary nodule risk stratification will provided to evaluating clinicians.

Who Is Running the Clinical Trial?

Abramson Cancer Center at Penn Medicine

Lead Sponsor

Trials

425

Recruited

464,000+

Published Research Related to This Trial

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 increasing use of chest high-resolution computed tomography has led to more frequent detection of lung adenocarcinomas, but there is a need for better diagnostic tools to assess the risk and manage these varying lesions effectively.

The computer-aided nodule assessment and risk yield software is a validated, automated tool that can noninvasively stratify the risk of adenocarcinoma lung nodules, correlating well with histology and patient outcomes, which may improve individualized patient management.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-Aided Nodule Assessment and Risk Yield Risk Management of Adenocarcinoma: The Future of Imaging? Foley, F., Rajagopalan, S., Raghunath, SM., et al.[2019]

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]

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]

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]

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]

Computer-aided diagnosis (CAD) systems significantly enhance the ability of radiologists to detect lung nodules, improving diagnostic performance in clinical settings.

CAD can also assess nodule malignancy and treatment response through volumetry, while providing objective analysis of nodule morphology, which streamlines the workflow for follow-up studies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective. Goo, JM.[2022]

The automated computer system successfully identified 86% of nodules in chest CT scans, detecting 318 out of 370 nodules across 16 studies involving eight oncology patients.

The system's assessment of nodule size changes closely matched that of a thoracic radiologist, with a high correlation coefficient of 0.932, indicating its potential reliability for monitoring nodule progression.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Chest CT: automated nodule detection and assessment of change over time--preliminary experience. Ko, JP., Betke, M.[2016]

The International Lung Screening Trial (ILST) will assess the effectiveness of the PLCOm2012 model compared to the U.S. Preventive Services Task Force (USPSTF) criteria in selecting participants for lung cancer screening, involving 4,500 participants over a minimum of 5 years.

The trial will also evaluate the efficiency of nodule management using the PanCan probability calculator against the Lung-RADS protocol, aiming to improve lung cancer detection rates and management strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Protocol and Rationale for the International Lung Screening Trial. Lim, KP., Marshall, H., Tammemägi, M., et al.[2021]

A new computer program can quickly and flexibly simulate lung nodules in digital chest radiographs, improving the efficiency of lung nodule detection studies.

Psychophysical and statistical studies demonstrated that radiologists and nonradiologists could not visually distinguish between real and simulated lung nodules, confirming the realism and validity of the simulations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-simulated lung nodules in digital chest radiographs for detection studies. Yocky, DA., Seeley, GW., Ovitt, TW., et al.[2019]

Citations

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

Lung nodules In around 3-5% of cases, nodules result from early-stage lung cancer. Distinguishing between benign and cancerous nodules is a critical step in lung cancer ...

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

Determining the impact of an artificial intelligence tool on the ... Optellum has developed and extensively validated an AI-based computer-aided diagnostic (CADx) tool for lung cancer prediction (LCP) in pulmonary ...

3. withpower.com withpower.com/trial/computer-aided-diagnosis-tool-for-lung-nodules-05b7d

Computer-Aided Diagnosis Tool for Lung Nodules Trial Overview This trial is testing the impact of a radiomics-based computer tool called Optellum Virtual Nodule Clinic on doctor's decisions in managing lung ...

4. optellum.com optellum.com/providers-overview-usa/

Providers Overview - USA Optellum's Virtual Nodule Clinic, the only solution for lung nodule management, enables early lung cancer detection, prediction, and diagnosis to improve ...

5. clinicaltrials.gov clinicaltrials.gov/study/NCT05968898

Study Details | NCT05968898 | Assessment of a Radiomics ... The Optellum Virtual Nodule Clinic is an FDA-approved (Class II) device for risk stratification of pulmonary nodules. It uses a convolutional neural network to ...

6. 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 ...

7. optellum.com optellum.com/providers-overview-global/

Providers Overview - Global Optellum Virtual Nodule Clinic®, is a unique & complete solution for early lung cancer diagnosis with seamless EHR & PACS integration.

8. optellum.com optellum.com/publications/

Publications Explore Optellum's clinical evidence and publications on AI-powered lung cancer prediction, early lung cancer detection, and nodule management, ...