Computer-Aided Diagnosis Tool for Lung Nodules
(ARCADES Trial)
Recruiting in Palo Alto (17 mi)
+2 other locations
Overseen byRoger Y. Kim, MD, MSCE
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Abramson Cancer Center at Penn Medicine
Disqualifiers: Lung cancer, Active cancer, others
No Placebo Group
Approved in 2 Jurisdictions
Trial Summary
What is the purpose of this trial?This is a pragmatic clinical trial that will study the effect of a radiomics-based computer-aided diagnosis (CAD) tool on clinicians' management of pulmonary nodules (PNs) compared to usual care. Adults aged 35-89 years with 8-30mm PNs evaluated at Penn Medicine PN clinics will undergo 1:1 randomization to one of two groups, defined by the PN malignancy risk stratification strategy used by evaluating clinicians: 1) usual care or 2) usual care + use of a radiomics-based CAD tool.
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 data supports the effectiveness of the treatment Optellum Virtual Nodule Clinic for lung nodules?
Research shows that virtual nodule clinics, like the Optellum Virtual Nodule Clinic, provide high-quality care and help patients follow medical guidelines, leading to patient satisfaction and cost savings. Additionally, computer-aided diagnosis tools have been shown to help doctors identify more lung nodules and assess their risk, which can guide personalized patient care.
12345Is the Computer-Aided Diagnosis Tool for Lung Nodules safe for humans?
The research articles provided do not contain specific safety data for the Computer-Aided Diagnosis Tool for Lung Nodules or its related names. They focus on lung nodule identification and management, but do not address safety concerns directly.
26789How does the computer-aided diagnosis tool for lung nodules differ from other treatments?
The computer-aided diagnosis (CAD) tool for lung nodules is unique because it uses advanced computer systems to automatically detect and assess lung nodules on chest CT scans, helping radiologists identify more nodules and evaluate changes over time. This tool enhances diagnostic accuracy and workflow efficiency, unlike traditional methods that rely solely on manual evaluation by radiologists.
345910Eligibility Criteria
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.
+1 more
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.
+6 more
Trial Timeline
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
Participant Groups
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.
2Treatment groups
Experimental Treatment
Active Control
Group I: Clinician assessment + CAD-based risk stratificationExperimental Treatment1 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 Control1 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.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Perelman Center for Advanced MedicinePhiladelphia, PA
Penn Medicine Washington SquarePhiladelphia, PA
Penn Medicine University CityPhiladelphia, PA
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Who Is Running the Clinical Trial?
Abramson Cancer Center at Penn MedicineLead Sponsor
References
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.
Computer-Aided Nodule Assessment and Risk Yield Risk Management of Adenocarcinoma: The Future of Imaging? [2019]Increased clinical use of chest high-resolution computed tomography results in increased identification of lung adenocarcinomas and persistent subsolid opacities. However, these lesions range from very indolent to extremely aggressive tumors. Clinically relevant diagnostic tools to noninvasively risk stratify and guide individualized management of these lesions are lacking. Research efforts investigating semiquantitative measures to decrease interrater and intrarater variability are emerging, and in some cases steps have been taken to automate this process. However, many such methods currently are still suboptimal, require validation and are not yet clinically applicable. The computer-aided nodule assessment and risk yield software application represents a validated tool for the automated, quantitative, and noninvasive tool for risk stratification of adenocarcinoma lung nodules. Computer-aided nodule assessment and risk yield correlates well with consensus histology and postsurgical patient outcomes, and therefore may help to guide individualized patient management, for example, in identification of nodules amenable to radiological surveillance, or in need of adjunctive therapy.
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.
A computer-aided diagnosis for evaluating lung nodules on chest CT: the current status and perspective. [2022]As the detection and characterization of lung nodules are of paramount importance in thoracic radiology, various tools for making a computer-aided diagnosis (CAD) have been developed to improve the diagnostic performance of radiologists in clinical practice. Numerous studies over the years have shown that the CAD system can effectively help readers identify more nodules. Moreover, nodule malignancy and the response of malignant lung tumors to treatment can also be assessed using nodule volumetry. CAD also has the potential to objectively analyze the morphology of nodules and enhance the workflow during the assessment of follow-up studies. Therefore, understanding the current status and limitations of CAD for evaluating lung nodules is essential to effectively apply CAD in clinical practice.
Protocol and Rationale for the International Lung Screening Trial. [2021]Rationale: The NLST (National Lung Screening Trial) reported a 20% reduction in lung cancer mortality with low-dose computed tomography screening; however, important questions on how to optimize screening remain, including which selection criteria are most accurate at detecting lung cancers and what nodule management protocol is most efficient. The PLCOm2012 (Prostate, Lung, Colorectal and Ovarian) Cancer Screening Trial 6-year and PanCan (Pan-Canadian Early Detection of Lung Cancer) nodule malignancy risk models are two of the better validated risk prediction models for screenee selection and nodule management, respectively. Combined use of these models for participant selection and nodule management could significantly improve screening efficiency.Objectives: The ILST (International Lung Screening Trial) is a prospective cohort study with two primary aims: 1) Compare the accuracy of the PLCOm2012 model against U.S. Preventive Services Task Force (USPSTF) criteria for detecting lung cancers and 2) evaluate nodule management efficiency using the PanCan nodule probability calculator-based protocol versus Lung-RADS.Methods: ILST will recruit 4,500 participants who meet USPSTF and/or PLCOm2012 risk ≥1.51%/6-year selection criteria. Participants will undergo baseline and 2-year low-dose computed tomography screening. Baseline nodules are managed according to PanCan probability score. Participants will be followed up for a minimum of 5 years. Primary outcomes for aim 1 are the proportion of individuals selected for screening, proportion of lung cancers detected, and positive predictive values of either selection criteria, and outcomes for aim 2 include comparing distributions of individuals and the proportion of lung cancers in each of three management groups: next surveillance scan, early recall scan, or diagnostic evaluation recommended. Statistical powers to detect differences in the four components of primary study aims were ≥82%.Conclusions: ILST will prospectively evaluate the comparative accuracy and effectiveness of two promising multivariable risk models for screenee selection and nodule management in lung cancer screening.Clinical trial registered with www.clinicaltrials.gov (NCT02871856).
Computer-simulated lung nodules in digital chest radiographs for detection studies. [2019]Computer simulations of lung nodules overcome many shortcomings of creating radiographs using anthropomorphic nodule phantoms for lung nodule detection studies, but these algorithms can be cumbersome and involved. A simple, fast, and flexible computer program to simulate lung nodules in digital chest radiographs for detection studies is reported. To verify the realism of the simulated nodules, a psychophysical study and a statistical study were conducted. In the psychophysical study, six radiologists and four nonradiologists were asked to distinguish between 17 real lung nodules and 17 computer-simulated lung nodules shown in eight radiographs. The results show that the computer-simulated lung nodules are indistinguishable visually from real lung nodules. Using parameters from the Rose model of vision, results show that the simulated and real nodules are the same statistically. Thus, besides visual validity, statistical analysis in confirming the validity of the simulated lung nodules is included.
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.
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.
Chest CT: automated nodule detection and assessment of change over time--preliminary experience. [2016]The authors developed a computer system that automatically identifies nodules at chest computed tomography, quantifies their diameter, and assesses for change in size at follow-up. The automated nodule detection system identified 318 (86%) of 370 nodules in 16 studies (eight initial and eight follow-up studies) obtained in eight oncology patients with known nodules. Assessment of change in nodule size by the computer matched that by the thoracic radiologist (Spearman rank correlation coefficient, 0.932).