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

NCT05968898 | Recruiting FDA Device

• 1 other identifier: 10523
• Study Type: interventional
• Target: 300
• Locations: 1 country
• Sites: 3

Brief Summary

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.

Conditions

Lung Cancer; Pulmonary Nodule, Solitary

Keywords

clinical effectiveness; clinical utility; artificial intelligence; medical decision-making; risk stratification

Outcome Measures

Primary Outcomes (1)

• Appropriate management of pulmonary nodule

  The composite proportion of benign pulmonary nodules managed with imaging surveillance and malignant pulmonary nodules managed with biopsy or empiric treatment. Final pulmonary nodule diagnosis will be categorized as malignant or benign based on pathologic evaluation. If pathology is unavailable or inconclusive (i.e., the biopsy was non-diagnostic), pulmonary nodule resolution, shrinkage, or diameter stability at 12 months will be defined as a benign diagnosis.

  12 months

Secondary Outcomes (4)

• Timeliness of care

  12 months

• Adverse events

  12 months

• Diagnostic yield

  12 months

• Healthcare costs

  12 months

Study Arms (2)

Usual care (clinician assessment)

NO 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.

Clinician assessment + CAD-based risk stratification

EXPERIMENTAL

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.

Device: Optellum Virtual Nodule Clinic

Interventions

Optellum Virtual Nodule Clinic DEVICE

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.

Clinician assessment + CAD-based risk stratification

Eligibility Criteria

• Age: 35 Years - 89 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• Abramson Cancer Center at Penn Medicine: lead

Study Sites (3)

Penn Medicine University City

Philadelphia, Pennsylvania, 19104, United States

RECRUITING

Perelman Center for Advanced Medicine

Philadelphia, Pennsylvania, 19104, United States

RECRUITING

Penn Medicine Washington Square

Philadelphia, Pennsylvania, 19107, United States

RECRUITING

Related Publications (7)

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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-2019-214104. Epub 2020 Mar 5.

  PMID: 32139611 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

• 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 BACKGROUND

MeSH Terms

Conditions

Lung Neoplasms; Solitary Pulmonary Nodule

Condition Hierarchy (Ancestors)

Respiratory Tract Neoplasms; Thoracic Neoplasms; Neoplasms by Site; Neoplasms; Lung Diseases; Respiratory Tract Diseases

Study Officials

• Roger Y. Kim, MD, MSCE

  University of Pennsylvania

  PRINCIPAL INVESTIGATOR

Central Study Contacts

Roger Y. Kim, MD, MSCE

CONTACT

215-662-3677; roger.kim@pennmedicine.upenn.edu

Anil Vachani, MD, MSCE

CONTACT

215-573-7931; avachani@pennmedicine.upenn.edu

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor of Medicine

Study Record Dates

• First Submitted: July 21, 2023
• First Posted: August 1, 2023
• Study Start: January 9, 2024
• Primary Completion (Estimated): December 31, 2026
• Study Completion (Estimated): December 31, 2027
• Last Updated: June 6, 2025

Record last verified: 2025-06

Data Sharing

• IPD Sharing: Will not share

Locations

US (3)