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Leveraging Cancer Registry Survival Data to Estimate Lung Cancer Recurrence.
Aaron Percy Pereira1, Alexis Andrew Miller2, Hoa Dam1
1University of Wollongong, NSW, Australia.
Studies in Health Technology and Informatics
|August 8, 2025
Summary
This study introduces a new method to estimate lung cancer recurrence risk using survival data. It provides the first population-level estimates for US patients, aiding early detection and personalized treatments.
Area of Science:
- Oncology
- Cancer Epidemiology
- Biostatistics
Background:
- Population-based metastatic recurrence risk data is limited due to lack of registry data.
- Existing cancer registries lack recurrence-specific data, hindering population-level insights.
- Accurate recurrence risk estimation is crucial for effective cancer management.
Purpose of the Study:
- To develop and validate an innovative method for estimating population-based cancer recurrence risk.
- To utilize disease-specific survival data from cancer registries for recurrence risk assessment.
- To provide the first population-level estimates of Lung cancer recurrence risk in US patients.
Main Methods:
- Integrated an illness-death model with a mixture-cure framework for net cancer survival.
- Derived recurrence risk by analyzing survival outcomes in the non-cured subgroup.
- Applied the methodology to Lung cancer (LC) disease-specific survival data from the SEER registry (2000-2021).
Main Results:
- Identified higher recurrence rates in older patients and those with advanced-stage diagnoses.
- Observed elevated recurrence rates in individuals with small cell Lung cancer (SCLC).
- Generated the first population-level estimates of Lung cancer recurrence risk for US patients.
Conclusions:
- Disease-specific survival data from cancer registries can effectively inform recurrence risk estimation.
- The developed method provides valuable insights for early Lung cancer detection.
- Findings support the development of tailored oncology treatments and improved patient outcomes.
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