biomarker platform (virtual nodule clinic) for the management of indeterminate pulmonary nodules

Conditions: Lung Neoplasm

Summary: this clinical trial studies whether a biomarker platform, the virtual nodule clinic, can be used for the management of lung (pulmonary) nodules that are not clearly non-cancerous (benign) or clearly cancerous (malignant) (indeterminate pulmonary nodules \[ipns\]). the management of ipns is based on estimating the likelihood that the observed nodule is malignant. many things, such as age, smoking history, and current symptoms, are considered when making a prediction of the likelihood of malignancy. radiographic imaging characteristics are also considered. lung nodule management for ipns can result in unnecessary invasive procedures for nodules that are ultimately determined to be benign, or potential delays in treatment when results of tests cannot be determined or are falsely negative. the virtual nodule clinic is an artificial intelligence (ai) based imaging software within the electronic health record which makes certain that identified pulmonary nodules are screened by clinicians with expertise in nodule management. the virtual nodule clinic also features an ai based radiomic prediction score which designates the likelihood that a pulmonary nodule is malignant. this may improve the ability to manage ipns and lower unnecessary invasive procedures or treatment delays. using the virtual nodule clinic may work better for the management of ipns.

primary objectives: i. to test the hypothesis that usual care plus a radiomic prediction score impacts patient management compared to usual care alone. ii. to conduct a multicenter pragmatic randomized controlled platform trial using a validated biomarker, the radiomic prediction score. iii. to conduct a biomarker study that will evaluate the first necessary (but not sufficient) step to show clinical utility. iv. to assess the magnitude of change in patient management with use of the radiomic prediction score. v. to develop a platform that can be used as framework for future larger biomarker studies. outline: patients are randomized to 1 of 2 arms. arm i: patients undergo standard of care (soc) computed tomography (ct) evaluation and receive a virtual nodule clinic radiomic prediction score on study. patients then receive soc lung nodule management on study. arm ii: patients undergo soc ct evaluation on study. patients then receive soc lung nodule management on study.

Eligibility criteria:
inclusion criteria: * adults 35-year-old and older with undiagnosed ipn(s) 8-30mm referred for evaluation * referral includes direct in-basket messages in the electronic healthcare record (ehr) to study providers, telehealth visits or clinic visit * for multiple nodules, we will obtain the score from the dominant or most suspicious nodule based on providers or radiologist impression * available ct scan with slice thickness of 3 mm or less with the nodule of interest present. nodules identified during screening low dose computed tomography of the chest (ldct) that have had a conventional, follow-up ct performed are eligible for inclusion exclusion criteria: * pure ground glass nodule * patients known to be a prisoners * patients known to be pregnant * known active malignancy within the last 5 years at time of enrollment (excluding non-melanoma skin cancers) * more than 5 ipns present on imaging * nodules referred after initial ldct for screening with only one ldct available. the lung cancer prediction convolutional neural network (lcp cnn) algorithm is not currently validated for screening studies * thoracic implants that impact the image appearance of the nodule * clinician determines that use of the lcp cnn model is required or contraindicated for the optimal care of the patient