Stage Shift Following Implementation of a Comprehensive Lung Cancer Identification Program using Blood-Based Biomarkers in a U.S. Community Health System

Abstract Background: Lung cancer mortality is strongly associated with stage at diagnosis. Although low-dose CT (LDCT) screening reduces mortality among high-risk populations, implementation barriers persist in community settings, including fragmented care pathways and inadequate follow-up of incidental pulmonary nodules. In 2019, Frederick Health implemented the Combined Lung Cancer Identification Program (CLIP), a multidisciplinary initiative integrating structured incidental nodule tracking, LDCT screening coordination, and blood-based malignancy risk stratification. Research Question: Was implementation of CLIP associated with earlier stage diagnosis of lung cancer? Study Design and Methods: This retrospective study evaluated stage at diagnosis before and after CLIP implementation and compared contemporaneous patients enrolled in CLIP with those managed outside CLIP. The pre–post analysis included patients diagnosed in 2018 and 2022–2023. The contemporaneous cohort included patients diagnosed between January 1, 2022 and March 20, 2023 categorized by CLIP participation. Stage was assigned using AJCC 8th Edition criteria. Results : A total of 268 patients with newly diagnosed lung cancer were included (122 in 2018; 146 in 2022–2023). Early-stage disease (Stage I–II) increased from 36.9% in 2018 to 47.3% in 2022–2023. Stage I diagnoses increased from 29.5% to 40.4%, while Stage IV decreased from 39.3% to 32.9%. In the contemporaneous cohort (n=146), Stage I disease occurred in 67.6% of CLIP patients compared with 12.5% of non-CLIP patients, while Stage IV disease occurred in 6.7% versus 59.7%. Early-stage disease comprised 77.1% of CLIP diagnoses compared with 16.7% in non-CLIP patients. Interpretation : Implementation of a multidisciplinary lung cancer identification program was associated with earlier stage at diagnosis and fewer advanced-stage presentations. Programs integrating structured incidental nodule management, screening coordination, and risk-based diagnostic prioritization may represent scalable models to improve early lung cancer detection in community practice settings.