Accepted for/Published in: JMIR Public Health and Surveillance
Date Submitted: Feb 23, 2023
Open Peer Review Period: Feb 23, 2023 - Mar 9, 2023
Date Accepted: Apr 24, 2024
(closed for review but you can still tweet)
Predicting Lung Cancer Survival to the Future: A Population-Based Cancer Survival Modeling Study
ABSTRACT
Background:
Lung cancer remains the leading cause of cancer-related mortality globally, with late diagnoses often resulting in poor prognosis. In response, the Lung Ambition Alliance aims to double the 5-year survival rate by 2025.
Objective:
Utilizing the Taiwan Cancer Registry, this study employs the survivorship–period–cohort (SPC) model to assess the feasibility of achieving this goal by predicting future survival rates of lung cancer patients in Taiwan.
Methods:
This retrospective study analyzed data from 205,104 lung cancer patients registered between 1997 and 2018. Survival rates were calculated using the SPC model, focusing on 1-year interval survival rates and extrapolating to predict 5-year outcomes for diagnoses up to 2020, as viewed from 2025. Model validation involved comparing predicted rates with actual data using symmetric mean absolute percentage error
Results:
The study identified notable improvements in survival rates beginning in 2004, with the predicted 5-year survival rate for 2020 reaching 38.7%, marking a considerable increase from the most recent available data of 23.8% for patients diagnosed in 2013.Subgroup analysis revealed varied survival improvements across different demographics and histological types. Predictions based on current trends indicate that achieving the Lung Ambition Alliance’s goal could be within reach.
Conclusions:
The analysis demonstrates notable improvements in lung cancer survival rates in Taiwan, driven by the adoption of low-dose computed tomography screening, alongside advances in diagnostic technologies and treatment strategies. While the ambitious target set by the Lung Ambition Alliance appears achievable, ongoing advancements in medical technology and health policies will be crucial. The study underscores the potential impact of continued enhancements in lung cancer management and the importance of strategic health interventions to further improve survival outcomes. Keywords: lung cancer, survival, survivorship-period-cohort model, prediction. Clinical Trial: Not applicable
Citation
Per the author's request the PDF is not available.
Copyright
© The authors. All rights reserved. This is a privileged document currently under peer-review/community review (or an accepted/rejected manuscript). Authors have provided JMIR Publications with an exclusive license to publish this preprint on it's website for review and ahead-of-print citation purposes only. While the final peer-reviewed paper may be licensed under a cc-by license on publication, at this stage authors and publisher expressively prohibit redistribution of this draft paper other than for review purposes.