RecruitingNot ApplicableNCT05968898

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

Sponsor

Abramson Cancer Center at Penn Medicine

Enrollment

300 participants

Start Date

Jan 9, 2024

Study Type

INTERVENTIONAL

Conditions

Lung Cancer Pulmonary Nodule, Solitary

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.

Eligibility

Min Age: 35 YearsMax Age: 89 Years

Inclusion Criteria4

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 Criteria8

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

Interventions

DEVICEOptellum 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.