Lung cancer origin: a seven-state linear model: pulmonary power target combinations, SEER calibration, and state-resolved readouts

Abstract Background Lung cancer is the leading cause of cancer death, and its incidence is the end readout of a decades-long latent process. We treat the tissue layer of that process as the pulmonary clearance module and ask whether a seven-state linear model, with the five pulmonary actions at L3 and six product groups at L4, reproduces SEER incidence and outperforms one-state and three-state models. Methods We built a nine-state model (H, L1-L7, D). Targets were assigned to hua, san, lian, jiang and bu by measured function, and PubMed records measured evidence volume and coverage. The model was calibrated to SEER 2012–2016 age-specific incidence, and AIC, BIC and leave-one-age-group cross-validation compared the three structures. Results The seven-state model reproduced incidence (AIC 34.28; one-state 84.82; three-state 46.00) and produced a peak at 80–84 years with a decline above 85. The five actions covered 109 targets and 7,083 records, and the integrated combination carried 2.43 times the records of the largest single action. The six product groups covered 51 targets and 5,535 records. State occupancy followed L1-L7, and current smokers moved through the chain three decades earlier than never-smokers. Conclusion The tissue layer of lung cancer is the pulmonary clearance module, and the five actions form its target set. The seven-state linear model reproduces SEER incidence with the module at L3. L4 to L5 is the cell-mutation axiom, and L5-L7 is the established mutation-clone chain. The model identifies the tissue state on which prevention and early detection act.