Leveraging Cancer Registry Survival Data to Estimate Lung Cancer Recurrence.

Population-based insights into the risk of metastatic recurrence are typically unavailable due to the absence of recurrence-specific data in cancer registries. This study presents an innovative method for estimating recurrence risk using disease-specific survival data from cancer registries. The methodology integrates an illness-death model with a mixture-cure framework for net cancer survival. Recurrence risk is derived by analysing survival outcomes for the non-cured subgroup, complemented by existing data on post-recurrence survival. The approach was applied to disease-specific survival data for Lung cancer cases recorded in the SEER registry (2000-2021). Findings reveal higher recurrence rates among older patients, those with advanced-stage diagnoses, and individuals with small cell Lung cancer. This research delivers the first population-level estimates of Lung cancer recurrence risk in US patients, showcasing how disease-specific survival data can inform recurrence risk estimation to support early detection and tailored oncology treatments.