Clinical trial NCT05968898: Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Pulmonary nodulES
Official title: Assessment of a Radiomics-based Computer-Aided Diagnosis Tool for Cancer Risk Stratification of Pulmonary Nodules
Study type: INTERVENTIONAL
Overall status: RECRUITING
Phase: NA
Conditions: Lung Cancer
Pulmonary Nodule, Solitary
Keywords: clinical effectiveness
clinical utility
artificial intelligence
medical decision-making
risk stratification
Lead sponsor: Abramson Cancer Center at Penn Medicine
Sponsor class: OTHER
First posted: 2023-08-01
Enrollment: 300
Sex: ALL
Minimum age (years): 35
Maximum age (years): 89
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: DIAGNOSTIC

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.

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.

Eligibility criteria: 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

Primary outcomes:
measure: Appropriate management of pulmonary nodule
timeFrame: 12 months
Secondary outcomes:
measure: Timeliness of care
timeFrame: 12 months
measure: Adverse events
timeFrame: 12 months
measure: Diagnostic yield
timeFrame: 12 months
measure: Healthcare costs
timeFrame: 12 months
Other outcomes:
Locations:
city: Philadelphia
country: United States
facility: Penn Medicine University City
state: Pennsylvania
city: Philadelphia
country: United States
facility: Perelman Center for Advanced Medicine
state: Pennsylvania
city: Philadelphia
country: United States
facility: Penn Medicine Washington Square
state: Pennsylvania
Study officials:
affiliation: University of Pennsylvania
name: Roger Y. Kim, MD, MSCE
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
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citation: 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.
citation: 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.
citation: 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.
citation: 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.