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Towards Personalised Screening for Lung Cancer: A Sex-Stratified Approach in a Danish Cohort Integrating
María Del Pilar Fernández Montejo1,2, Uffe Bodtger3,4,5, Randi Jepsen6
1Centre for Health Research, Zealand University Hospital, Nykøbing F., Nykøbing Falster, Denmark, mafer@regionsjaelland.dk.
Introduction:
Sex disparities in lung cancer risk assessment may lead to inequities in screening eligibility. Criteria based on smoking history and age can underrepresent women. Systemic inflammatory indexes derived from routine blood tests are potential biomarkers in cancer risk assessment. We investigated the association between systemic inflammatory indexes and lung cancer risk, stratified by sex, in a Danish cohort.
Methods:
We conducted a population-based cohort study using data from the Lolland-Falster Health Study (LOFUS) linked to the Danish Pathology Data Bank (Patobank). Participants aged 40-74 years without a history of lung cancer were included. We calculated four systemic inflammatory indexes and assessed their association with lung cancer using logistic regression models adjusted for age, sex, and smoking status. Optimal cut-off points were determined using the Youden index. Bootstrapping was applied for internal validation.
Results:
Among 10,887 participants with complete data, 58 (0.53%) were diagnosed with lung cancer during a maximum follow-up period of 6.6 years. High levels of all four inflammatory indexes were associated with increased lung cancer risk. Sex-stratified analyses revealed stronger associations between Neutrophil-to-Lymphocyte Ratio (NLR) and Systemic Immune Inflammatory Index (SII) and lung cancer in men, and between C-reactive protein to albumin ratio (CAR) and lung cancer in women. Model discrimination improved after adding inflammatory indexes.
Conclusion:
Our findings support sex-specific lung cancer risk assessment and personalised screening strategies. Pre-diagnostic inflammatory indexes may serve as biomarkers to complement existing screening criteria. Further research is needed to validate their potential in improving lung cancer prediction models.
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