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Recruiting NCT05968898

Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Pulmonary nodulES

No phase Interventional Lung Cancer Pulmonary Nodule, Solitary

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: Optellum Virtual Nodule Clinic.
Who it may be relevant to
Registry conditions: Lung Cancer, Pulmonary Nodule, Solitary. Basic parameters: 35 years — 89 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
United States
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

Unsure about the terms? Read our patient guide →

Official title

Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules

Overview

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.

Detailed description

Accurate malignancy risk stratification of pulmonary nodules (PNs) is critical to ensuring that cancer is diagnosed in a timely manner and patients do not undergo unnecessary diagnostic procedures. Preliminary data suggests that a radiomics-based lung cancer prediction (LCP) computer-aided diagnosis (CAD) tool is effective in risk stratifying PNs and may improve clinicians' PN management decisions. This is a pragmatic clinical trial evaluating the effect of this CAD tool on clinicians' management of PNs compared to usual care. Individuals eligible for this study will include adults aged 35-89 years who are scheduled to be evaluated at a Penn Medicine PN clinic for a newly discovered PN 8-30mm in maximal diameter on CT imaging. Exclusion criteria include lack of CT imaging data at the time of index clinic visit, thoracic lymphadenopathy by CT size criteria, presence of pulmonary masses (\>3cm in maximal diameter), PNs with popcorn calcification (consistent with benign etiology), pure ground-glass subsolid PNs, a history of lung cancer, and history of any active cancer within 5 years. Enrolled participants will undergo 1:1 stratified randomization to one of two groups, defined by the PN malignancy risk stratification strategy used by evaluating clinicians: 1) usual care (clinician assessment) or 2) clinician assessment + CAD-based risk stratification using the LCP-CAD tool. The control arm will be usual care, defined as routine clinician assessment of PN malignancy risk. In the experimental arm, clinicians will be provided a report with the CAD tool estimate of malignancy risk for the PN being evaluated.

Interventions

  • Device Optellum Virtual Nodule Clinic
    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 evaluate CT imaging data to provide an estimate of malignancy risk for indeterminate pulmonary nodules.

Primary outcome measures

  • Appropriate management of pulmonary nodule [Time frame: 12 months]
Secondary outcome measures (4)
  • Timeliness of care [Time frame: 12 months]
  • Adverse events [Time frame: 12 months]
  • Diagnostic yield [Time frame: 12 months]
  • Healthcare costs [Time frame: 12 months]

Eligibility criteria

Inclusion criteria

  • Male or female, aged 35-89 years
  • Scheduled to be evaluated at a UPHS PN clinic
  • Newly discovered solid or part-solid indeterminate PN 8-30mm in maximal diameter on CT imaging within 60 days of index clinic visit
  • Chest CT imaging meeting the technical requirements for compatibility with Optellum Virtual Nodule Clinic software

Exclusion criteria

  • Chest CT imaging with discrete mediastinal or hilar lymphadenopathy by CT size criteria (>10mm in maximal short-axis diameter on axial CT images)
  • PNs with popcorn calcification (consistent with benign etiology)
  • Pure ground-glass subsolid PNs (may be associated with lower risk of clinically significant malignancy)
  • PN previously seen on CT imaging >60 days prior to most recent CT
  • More than one indeterminate PN 8-30mm in maximal diameter
  • History of lung cancer
  • History of active cancer within the previous 5 years
  • Presence of a thoracic implant that impedes PN visualization

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Open label
Primary purpose
Diagnostic

Study locations

United States · 3 centers
  • Penn Medicine University City — Philadelphia
  • Perelman Center for Advanced Medicine — Philadelphia
  • Penn Medicine Washington Square — Philadelphia

Publications

  • Kim RY, Oke JL, Pickup LC, Munden RF, Dotson TL, Bellinger CR, Cohen A, Simoff MJ, Massion PP, Filippini C, Gleeson FV, Vachani A. Artificial Intelligence Tool for Assessment of Indeterminate Pulmonary Nodules Detected with CT. Radiology. 2022 Sep;304(3):683-691. doi: 10.1148/radiol.212182. Epub 2022 May 24. PMID 35608444
  • Kim RY, Oke JL, Dotson TL, Bellinger CR, Vachani A. Effect of an artificial intelligence tool on management decisions for indeterminate pulmonary nodules. Respirology. 2023 Jun;28(6):582-584. doi: 10.1111/resp.14502. Epub 2023 Apr 5. No abstract available. PMID 37017091
  • Massion PP, Antic S, Ather S, Arteta C, Brabec J, Chen H, Declerck J, Dufek D, Hickes W, Kadir T, Kunst J, Landman BA, Munden RF, Novotny P, Peschl H, Pickup LC, Santos C, Smith GT, Talwar A, Gleeson F. Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules. Am J Respir Crit Care Med. 2020 Jul 15;202(2):241-249. doi: 10.1164/rccm.201903-0505OC. PMID 32326730
  • Baldwin DR, Gustafson J, Pickup L, Arteta C, Novotny P, Declerck J, Kadir T, Figueiras C, Sterba A, Exell A, Potesil V, Holland P, Spence H, Clubley A, O'Dowd E, Clark M, Ashford-Turner V, Callister ME, Gleeson FV. External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules. Thorax. 2020 Apr;75(4):306-312. doi: 10.1136/thoraxjnl-201 PMID 32139611
  • Paez R, Kammer MN, Balar A, Lakhani DA, Knight M, Rowe D, Xiao D, Heideman BE, Antic SL, Chen H, Chen SC, Peikert T, Sandler KL, Landman BA, Deppen SA, Grogan EL, Maldonado F. Longitudinal lung cancer prediction convolutional neural network model improves the classification of indeterminate pulmonary nodules. Sci Rep. 2023 Apr 15;13(1):6157. doi: 10.1038/s41598-023-33098-y. PMID 37061539
  • Paez R, Kammer MN, Tanner NT, Shojaee S, Heideman BE, Peikert T, Balbach ML, Iams WT, Ning B, Lenburg ME, Mallow C, Yarmus L, Fong KM, Deppen S, Grogan EL, Maldonado F. Update on Biomarkers for the Stratification of Indeterminate Pulmonary Nodules. Chest. 2023 Oct;164(4):1028-1041. doi: 10.1016/j.chest.2023.05.025. Epub 2023 May 25. PMID 37244587
  • Kim RY. Radiomics and artificial intelligence for risk stratification of pulmonary nodules: Ready for primetime? Cancer Biomark. 2025 Jan;42(1):CBM230360. doi: 10.3233/CBM-230360. Epub 2024 Feb 6. PMID 38427470

Identifiers

NCT: NCT05968898 · 10523

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗