Clinical trial · Interventional
Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Pulmonary nodulES
Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules
NCT05968898CI-TRIAL-00113279ARCADESrecruitingN/AClinicalTrials.gov clinicaltrialsProvenance
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Lung Cancer | Malignant Lung Neoplasm | CURATED_EXACT | 0.92 |
| Pulmonary Nodule, Solitary | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Optellum Virtual Nodule Clinic | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- NO_INTERVENTION
- label
- Usual care (clinician assessment)
- description
- 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.
- type
- EXPERIMENTAL
- label
- Clinician assessment + CAD-based risk stratification
- description
- 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.
- interventionNames
- Device: Optellum Virtual Nodule Clinic
Primary outcomes (1)
- measure
- Appropriate management of pulmonary nodule
- timeFrame
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 35 Years
- Maximum age
- 89 Years
Show eligibility criteria text
Inclusion Criteria: 1. Male or female, aged 35-89 years 2. Scheduled to be evaluated at a UPHS PN clinic 3. Newly discovered solid or part-solid indeterminate PN 8-30mm in maximal diameter on CT imaging within 60 days of index clinic visit 4. Chest CT imaging meeting the technical requirements for compatibility with Optellum Virtual Nodule Clinic software Exclusion Criteria: 1. Chest CT imaging with discrete mediastinal or hilar lymphadenopathy by CT size criteria (\>10mm in maximal short-axis diameter on axial CT images) 2. PNs with popcorn calcification (consistent with benign etiology) 3. Pure ground-glass subsolid PNs (may be associated with lower risk of clinically significant malignancy) 4. PN previously seen on CT imaging \>60 days prior to most recent CT 5. More than one indeterminate PN 8-30mm in maximal diameter 6. History of lung cancer 7. History of active cancer within the previous 5 years 8. Presence of a thoracic implant that impedes PN visualization
References
Publications (7)
- BACKGROUNDKim 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
- BACKGROUNDKim 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
- BACKGROUNDMassion 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
- BACKGROUNDBaldwin 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
- BACKGROUNDPaez 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
- BACKGROUNDPaez 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.