Advancing Precision Lung Cancer Surveillance and Outcomes in Diverse Populations

This study, Advancing Precision Lung Cancer Surveillance and Outcomes in Diverse Populations (PLuS2), is an observational cohort study designed to evaluate real-world computed tomography (CT) imaging surveillance strategies following curative-intent treatment for early-stage non-small cell lung cancer (NSCLC). The study uses existing clinical data and does not assign interventions. The study population includes adults ages 20-90 with pathologically confirmed stage I-IIIA NSCLC who completed curative-intent therapy. Patients treated between 2012 and 2026 will be identified within the OneFlorida+ Clinical Research Consortium and followed for up to five years after treatment to assess surveillance patterns and outcomes. UF Health serves as the data coordinating site in collaboration with the OneFlorida+ Data Trust. Data sources include structured electronic health records, tumor registry data, selected claims, and unstructured clinical notes. Data extraction and cohort identification are conducted centrally using standardized definitions aligned with the PCORnet Common Data Model. The primary objectives of the study are to: 1. Describe utilization and adherence to guideline-recommended CT surveillance and evaluate determinants of use by race, ethnicity, socioeconomic status, and clinical factors. 2. Compare recurrence, second primary lung cancers, complications, and survival among patients undergoing semi-annual versus annual CT surveillance. 3. Use observational findings to inform microsimulation models that project long-term outcomes under alternative surveillance strategies. Clinical natural language processing (NLP) methods are used to classify CT scans as routine surveillance versus symptom-directed diagnostic imaging and to extract recurrence indicators, smoking history, and selected social determinants of health from unstructured clinical notes. NLP outputs are integrated with structured data to improve classification accuracy and completeness. Quality assurance procedures include automated checks for completeness, range, and internal consistency, validation against source EHR and tumor registry records, and maintenance of a standardized data dictionary defining all variables and coding systems. Standard operating procedures govern cohort assembly, quarterly data refreshes, data management, analysis, and reporting. The expected cohort size is approximately 1,700 patients, providing sufficient power to evaluate surveillance utilization patterns and outcome differences by surveillance interval. Missing data will be addressed using multiple imputation and sensitivity analyses. Statistical analyses include descriptive methods, multivariable and mixed-effects regression models, and time-to-event and competing-risk analyses. Results from the observational analyses will be used as inputs for microsimulation modeling to estimate long-term population-level outcomes associated with different CT surveillance strategies.