Stage Migration, Treatment Patterns, and Machine Learning-Based Prediction of 12-Month Mortality in Lung Adenocarcinoma Across the COVID-19 Era: A SEER Population-Based Study

Background/Objectives: The COVID-19 pandemic coincided with widespread disruption of cancer care. We examined changes in lung adenocarcinoma presentation and outcomes across the COVID-19 era and whether diagnosis-time information identifies patients at risk of early death.

Methods

Adults with microscopically confirmed lung adenocarcinoma diagnosed during 2018–2023 were identified from the Surveillance, Epidemiology, and End Results 17 Registries database and grouped into three diagnostic periods. Logistic regression, time-varying Cox, and Fine-Gray models evaluated stage, metastasis, surgery, delay, and mortality. Four machine-learning algorithms using only diagnosis-time variables were developed (2018–2021; n = 87,281) and temporally validated (2022; n = 22,012).