Late-Stage Presentation and Diagnostic Gaps in Romanian Lung Cancer Patients: A Retrospective Tertiary Center Analysis with Implications for Scalable Screening.

Background/Objectives: Lung cancer in Romania is characterized by high mortality and a lack of organized screening programs, resulting in frequent late-stage diagnoses. This study evaluates diagnostic patterns, stage distribution, and screening eligibility in a Romanian tertiary cohort to highlight the limitations of standard screening criteria and provide evidence for scalable, regionally adapted early detection strategies. Methods: We conducted a retrospective observational study of consecutive lung cancer patients treated at the Oncological Institute of Bucharest. To balance external validity with analytical robustness, the population was divided into a full cohort (Set A, n = 3764) for population-level descriptive analyses and a complete-case subset (Set B, n = 1768). Set B was utilized to simulate theoretical screening eligibility using established guidelines (USPSTF, NCCN, ERS) and to calculate a Missed Opportunity Index (MOI) for early-stage case capture. Results: Advanced-stage disease (Stages III-IV) overwhelmingly dominated the cohort, accounting for approximately 82% of diagnoses among patients with available staging information. Essential screening variables like smoking history were missing in 71.4% of the full cohort. In the complete-case subset, established guideline-based screening models demonstrated high MOI values, failing to capture approximately 69-73% of early-stage (Stage I-II) cases. Conversely, age-only minimal models appeared highly performant, structurally capturing approximately 95% of the cohort due to broad inclusiveness. Conclusions: In retrospective eligibility simulations, smoking-based screening models were associated with high missed opportunity rates for Stage I-II cases, highlighting a mismatch between simplified Western risk models and the multidimensional reality of lung cancer epidemiology in this cohort. These findings strongly support the need to move toward multivariable risk prediction models and population-adapted screening strategies that integrate non-traditional risk factors to better align screening eligibility with the true distribution of disease in real-world settings.