Virtual Nodule Clinic for Pulmonary Nodules
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May be covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Vanderbilt-Ingram Cancer Center
No Placebo Group
Approved in 3 jurisdictions
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.
12345Is 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.
26789How 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.
236910Eligibility 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.
I am 35 or older with a lung spot between 8-30mm that hasn't been diagnosed.
My chest nodules identified by LDCT have been followed up with a regular CT scan.
Exclusion Criteria
I have not had any cancer except for non-melanoma skin cancer in the last 5 years.
Participant Groups
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.
2Treatment groups
Experimental Treatment
Active Control
Group I: Arm I (Radiomic Prediction Score)Experimental Treatment4 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 Control2 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
Find A Clinic Near You
Research locations nearbySelect from list below to view details:
Vanderbilt University/Ingram Cancer CenterNashville, TN
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Who is running the clinical trial?
Vanderbilt-Ingram Cancer CenterLead Sponsor
National Cancer Institute (NCI)Collaborator
References
Clinical Impact and Generalizability of a Computer-Assisted Diagnostic Tool to Risk-Stratify Lung Nodules With CT. [2023]To evaluate whether an imaging classifier for radiology practice can improve lung nodule classification and follow-up.
Multidisciplinary virtual management of pulmonary nodules. [2022]Multidisciplinary nodule clinics provide high-quality care and favor adherence to guidelines. Virtual care has shown savings benefits along with patient satisfaction. Our aim is to describe the first year of operation of a multidisciplinary virtual lung nodule clinic, the population evaluated and issued decisions. Secondarily, among discharged patients, we aimed to analyze their follow-up prior to the existence of our consultation, evaluating its adherence to guidelines.
Automated identification of patients with pulmonary nodules in an integrated health system using administrative health plan data, radiology reports, and natural language processing. [2022]Lung nodules are commonly encountered in clinical practice, yet little is known about their management in community settings. An automated method for identifying patients with lung nodules would greatly facilitate research in this area.
Systematic approach to the management of the newly found nodule on screening computed tomography: role of dedicated pulmonary nodule clinics. [2018]Indeterminate pulmonary nodules in asymptomatic individuals are common, and their incidence is expected to increase. Although evidence-based guidelines exist for the management of these lesions, they are not in complete agreement and are often not followed, resulting in inconsistent management. A dedicated program or clinic for the management of lung nodules would allow an institution to deliver evidence-based, standardized care for patients with indeterminate nodules, and should include multidisciplinary care, state-of-the-art technology and expertise, and a patient navigation system to provide a user-friendly service for both patients and referring physicians. A dedicated pulmonary nodule clinic has many potential advantages.
Application of artificial intelligence in the diagnosis of multiple primary lung cancer. [2020]Artificial intelligence (AI) based on deep learning, convolutional neural networks and big data has been increasingly effective in the diagnosis and treatment of multiple primary pulmonary nodules. In comparison to previous imaging systems, AI measures more objective parameters such as three-dimensional (3D) volume, probability of malignant nodules, and possible pathological patterns, making the access to the properties of nodules more objective. In our retrospective study, a total of 53 patients with synchronous and metachronous multiple pulmonary nodules were enrolled of which 33 patients were confirmed by pathological tests to have primary binodules, and nine to have primary trinodules. A total of 15 patients had only one focus removed. The statistical results showed that the agreement in the AI diagnosis and postoperative pathological tests was 88.8% in identifying benign or malignant lesions. In addition, the probability of malignancy of benign lesions, preinvasive lesions (AAH, AIS) and invasive lesions (MIA, IA) was totally different (49.40±38.41% vs 80.22±13.55% vs 88.17±17.31%). The purpose of our study was to provide references for the future application of AI in the diagnosis and follow-up of multiple pulmonary nodules. AI may represent a relevant diagnostic aid that shows more accurate and objective results in the diagnosis of multiple pulmonary nodules, reducing the time required for interpretation of results by directly displaying visual information to doctors and patients and together with the clinical conditions of MPLC patients, offering plans for follow-up and treatment that may be more beneficial and reasonable for patients. Despite the great application potential in pneumosurgery, further research is needed to verify the accuracy and range of the application of AI.
Development of a Structured Query Language and Natural Language Processing Algorithm to Identify Lung Nodules in a Cancer Centre. [2021]Importance: The stratification of indeterminate lung nodules is a growing problem, but the burden of lung nodules on healthcare services is not well-described. Manual service evaluation and research cohort curation can be time-consuming and potentially improved by automation. Objective: To automate lung nodule identification in a tertiary cancer centre. Methods: This retrospective cohort study used Electronic Healthcare Records to identify CT reports generated between 31st October 2011 and 24th July 2020. A structured query language/natural language processing tool was developed to classify reports according to lung nodule status. Performance was externally validated. Sentences were used to train machine-learning classifiers to predict concerning nodule features in 2,000 patients. Results: 14,586 patients with lung nodules were identified. The cancer types most commonly associated with lung nodules were lung (39%), neuro-endocrine (38%), skin (35%), colorectal (33%) and sarcoma (33%). Lung nodule patients had a greater proportion of metastatic diagnoses (45 vs. 23%, p < 0.001), a higher mean post-baseline scan number (6.56 vs. 1.93, p < 0.001), and a shorter mean scan interval (4.1 vs. 5.9 months, p < 0.001) than those without nodules. Inter-observer agreement for sentence classification was 0.94 internally and 0.98 externally. Sensitivity and specificity for nodule identification were 93 and 99% internally, and 100 and 100% at external validation, respectively. A linear-support vector machine model predicted concerning sentence features with 94% accuracy. Conclusion: We have developed and validated an accurate tool for automated lung nodule identification that is valuable for service evaluation and research data acquisition.
Pulmonary Nodules: Common Questions and Answers. [2023]Pulmonary nodules are often incidentally discovered on chest imaging or from dedicated lung cancer screening. Screening adults 50 to 80 years of age who have a 20-pack-year smoking history and currently smoke or have quit smoking within the past 15 years with low-dose computed tomography is associated with a decrease in cancer-associated mortality. Once a nodule is detected, specific radiographic and clinical features can be used in validated risk stratification models to assess the probability of malignancy and guide management. Solid pulmonary nodules less than 6 mm warrant surveillance imaging in patients at high risk, and nodules between 6 and 8 mm should be reassessed within 12 months, with the recommended interval varying by the risk of malignancy and an allowance for patient-physician decision-making. A functional assessment with positron emission tomography/computed tomography, nonsurgical biopsy, and resection should be considered for solid nodules 8 mm or greater and a high risk of malignancy. Subsolid nodules have a higher risk of cancer and should be followed with surveillance imaging for longer. Direct physician-patient communication, clinical decision support within electronic health records, and guideline-based management algorithms included in radiology reports are associated with increased compliance with existing guidelines.
Updated Fleischner Society Guidelines for Managing Incidental Pulmonary Nodules: Common Questions and Challenging Scenarios. [2022]The new guidelines for managing incidental pulmonary nodules published by the Fleischner Society in 2017 reflect an improved understanding of the risk factors and biologic features of lung cancer. Specific topics emphasized in the updated guidelines include a new threshold size for follow-up, the importance of the morphologic features of nodules, accurate nodule measurements, recognition of subsolid components, understanding interval growth or change in nodule morphology, and knowledge of patient risk factors. The updated guidelines enable greater personal flexibility in the decision-making process and encourage individualized management of pulmonary nodules. These factors may introduce new challenges for radiologists, who previously used solely nodule size to make management recommendations. The authors describe eight scenarios that illustrate the challenges potentially encountered when applying the new guidelines to pulmonary nodule management. ©RSNA, 2018.
Creating an Incidental Pulmonary Nodule Safety-Net Program. [2021]Pulmonary nodules are a frequent, incidental finding on CT scans, ranging from up to 8.4% on abdominal scans and up to 48% on CT angiograms. Incidental findings are sometimes disregarded or overshadowed by critical situations and may not be disclosed or documented on discharge. The costs and risks associated with incidental findings are not insignificant, including the risk of a delayed diagnosis of lung cancer. A medical center commitment to prevent overlooked incidental pulmonary nodules led to the development of an incidental pulmonary nodule program. The program, led by an advanced practice nurse, established processes to identify patients with incidental lung nodules on CT scans and developed criteria for further follow-up with the primary care provider and the patient. Improvements with consistent use of Fleischner guidelines in scan reports by radiologists and increased ownership in informing patients of incidental nodules by ED and trauma providers have occurred. As the frequency of chest CT imaging is increasing, the number of incidental nodules identified will also increase. A lung nodule surveillance process would greatly benefit every lung nodule clinic or hospital system for management of pulmonary nodules.
Incidental Lung Nodules on Cross-sectional Imaging: Current Reporting and Management. [2021]Pulmonary nodules are the most common incidental finding in the chest, particularly on computed tomographs that include a portion or all of the chest, and may be encountered more frequently with increasing utilization of cross-sectional imaging. Established guidelines address the reporting and management of incidental pulmonary nodules, both solid and subsolid, synthesizing nodule and patient features to distinguish benign nodules from those of potential clinical consequence. Standard nodule assessment is essential for the accurate reporting of nodule size, attenuation, and morphology, all features with varying risk implications and thus management recommendations.