A Chronobiological Prognostic Model for Resected Stage I Non-Small-Cell Lung Cancer

Objectives: Chronobiology may influence outcomes in non-small-cell lung cancer (NSCLC), but its prognostic role in early-stage disease remains unclear. We evaluated the association between perioperative chronobiological variables, systemic inflammation, and long-term outcomes after complete resection for stage I NSCLC. Methods: 587 patients who underwent curative-intent resection for pathological stage I NSCLC between 2009 and 2018 were retrospectively reviewed. Chronobiological and inflammatory variables included season of surgery (spring/summer [21st March–22nd September], autumn/winter [23rd September–20th March]), time of day, day of week, and preoperative (CRP0 levels: low ≤ 3 mg/L, high > 3 mg/L) and postoperative day-3 (CRP3 levels: low ≤ 126 mg/L, high > 126 mg/L) C-reactive protein levels. The cohort was randomly divided into training (70%) and testing (30%) sets to develop and validate a prognostic model for overall survival (OS), taking into consideration the follow-up time. Disease-free survival (DFS) was analyzed as a secondary endpoint. Results: Median follow-up was 6.7 years, with 80 deaths and 108 recurrences recorded. In the training set, older age (p = 0.0049), stage IB disease (p = 0.0301), high CRP0 (p = 0.0049), and high CRP3 (p = 0.0280) were associated with worse OS, while surgery performed during autumn/winter was associated with improved OS (p = 0.0312). The multivariate Cox model including age, stage, and season of surgery showed the best prognostic performance (C-index 0.67, 95% Bonferroni-adjusted CI 0.50–0.83) and was internally validated in the testing cohort (C-index 0.69, 95%CI 0.52–0.86). The resulting linear predictor remained associated with both OS and DFS in the overall population. Conclusions: A simple model integrating age, pathological stage, and season of surgery was associated with long-term outcomes after surgery in stage I NSCLC. This model provides an exploratory framework integrating host-, tumor-, and chronobiological dimensions of prognosis. These findings require external validation and further investigation of the biological mechanisms underlying seasonal variation.