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# Lung adenocarcinoma across the COVID-19 era: small stage shifts, longer treatment delays
- URL: https://www.coios.me/lung-adenocarcinoma-across-the-covid-19-era-small-stage/
- Published: 2026-10-31T11:13:46.000Z
- Updated: 2026-11-01T07:06:36.000Z
- Description: Lung adenocarcinoma stage barely shifted across the COVID-19 era, but treatment delays over 90 days rose from 10.2% to 14.5%.
- Author: Daniel Ryan
- Tags: Health today, Cancer, AI in medicine, United States, #peer-reviewed, #refines, #item, #source-2026-10, #score-62

The study used SEER 17 registry data on 133,306 US adults with microscopically confirmed lung adenocarcinoma, diagnosed 2018–2023\. Stage IV disease was 47.9% in 2020–2021 versus 47.4% in 2018–2019 (adjusted OR 1.03). Treatment delay over 90 days rose from 10.2% to 11.1%, and to 14.5% in 2022–2023 (OR 1.45). A gradient boosting model trained on 2018–2021 diagnosis-time data predicted 12-month death in 2022 (ROC AUC 0.840). The model flagged 81.6% of patients who died, against 75.4% when all stage IV patients were classed as high risk. Validation was within SEER only, which lacks performance status, smoking history, comorbidity and molecular markers.

*Why it is interesting: Stage shifts during the pandemic were small, but delays to treatment grew and persisted after the acute phase. Against a stage IV rule, the model flagged 28 more patients per 1000; 21 of them died within a year.*

Source

[Diagnostics, 27 October 2026](https://doi.org/10.3390/diagnostics16203369?ref=coios.me)

Paper

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

Authors

Çınar ÖE, Adalı Y

DOI

10.3390/diagnostics16203369

Full text

[Open copy](https://pmc.ncbi.nlm.nih.gov/articles/PMC13703649/?ref=coios.me) (open-access copy, PubMed Central)

Type

Peer-reviewed article

Design

Population-based registry cohort, SEER 17, lung adenocarcinoma 2018-2023, n=133,306; logistic, time-varying Cox and Fine-Gray models plus four ML models trained 2018-2021 (n=87,281) and temporally validated on 2022 (n=22,012)

Verdict

Refines prior evidence

Driver

[Cancer](https://www.coios.me/d-cancer/)

Driver

[AI in medicine](https://www.coios.me/d-ai-medicine/)

Free full text

[Free to read at the publisher](https://doi.org/10.3390/diagnostics16203369?ref=coios.me)

Abstract — the authors' own words, CC BY

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). Results : Among 133,306 patients, COVID-19 versus pre-COVID-19 differences were significant but small in absolute terms: stage IV disease 47.9% versus 47.4% (adjusted odds ratio \[OR\], 1.03; 95% CI, 1.00-1.06), organ-specific metastasis 45.8% versus 44.4% (OR, 1.06; 1.04-1.09), and treatment delay exceeding 90 days 11.1% versus 10.2% (OR, 1.11; 1.06-1.17). Post-COVID-19 (2022-2023) diagnosis showed less advanced disease but substantially longer delay (14.5% versus 10.2%; OR, 1.45; 1.39-1.52). Gradient boosting achieved a 2022 ROC AUC of 0.840 (0.834-0.845); it flagged 46.8% of patients and correctly flagged 81.6% of those who died, against 44.0% and 75.4% for stage IV classification. Conclusions : COVID-era changes in stage and metastatic presentation were small in absolute terms, whereas the increase in treatment delay was larger and persisted. A diagnosis-time model modestly improved identification of early death beyond stage alone, flagging 28 more patients per 1000, of whom 21 died within the year. These findings support further evaluation; external validation is required before clinical use.