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Article: Prediction model on disease recurrence for low risk resected stage I lung adenocarcinoma

TitlePrediction model on disease recurrence for low risk resected stage I lung adenocarcinoma
Authors
Keywordsgenetics
low risk
lung cancer
NSCLC
stage I non-small cell carcinoma of lung
Issue Date27-Apr-2023
PublisherWiley
Citation
Respirology, 2023, v. 28, n. 7, p. 669-676 How to Cite?
Abstract

Background and Objective

Although stage I non-small cell lung carcinoma (NSCLC) typically carries a good prognosis following complete resection, early disease recurrence can occur. An accurate survival prediction model would help refine a follow-up strategy and personalize future adjuvant therapy. We developed a post-operative prediction model based on readily available clinical information for patients with stage I adenocarcinoma.

Methods

We retrospectively studied the disease-free survival (DFS) of 408 patients with pathologically confirmed low-risk stage I adenocarcinoma of lung who underwent curative resection from 2013 to 2017. A tree-based method was employed to partition the cohort into subgroups with distinct DFS outcome and stepwise risk ratio. These covariates were included in multivariate analysis to build a scoring system to predict disease recurrence. The model was subsequently validated using a 2011–2012 cohort.

Results

Non-smoker status, stage IA disease, epidermal-growth factor receptor mutants and female gender were associated with better DFS. Multivariate analysis identified smoking status, disease stage and gender as factors necessary for the scoring system and yielded 3 distinct risk groups for DFS [99.4 (95% CI 78.3–125.3), 62.9 (95% CI 48.2–82.0), 33.7 (95% CI 24.6–46.1) months, p < 0.005]. External validation yielded an area under the curve by receiver operating characteristic analysis of 0.863 (95% CI 0.755–0.972).

Conclusion

The model could categorize post-operative patients using readily available clinical information, and may help personalize a follow-up strategy and future adjuvant therapy.


Persistent Identifierhttp://hdl.handle.net/10722/329009
ISSN
2023 Impact Factor: 6.6
2023 SCImago Journal Rankings: 1.559
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorKwok, Wang Chun-
dc.contributor.authorMa, Ting Fung-
dc.contributor.authorHo, James Chung Man-
dc.contributor.authorLam, David Chi Leung-
dc.contributor.authorSit, Ko Yung-
dc.contributor.authorIp, Mary Sau Man-
dc.contributor.authorAu, Timmy Wing Kuk-
dc.contributor.authorTam, Terence Chi Chun-
dc.date.accessioned2023-08-05T07:54:35Z-
dc.date.available2023-08-05T07:54:35Z-
dc.date.issued2023-04-27-
dc.identifier.citationRespirology, 2023, v. 28, n. 7, p. 669-676-
dc.identifier.issn1323-7799-
dc.identifier.urihttp://hdl.handle.net/10722/329009-
dc.description.abstract<h3>Background and Objective</h3><p>Although stage I non-small cell lung carcinoma (NSCLC) typically carries a good prognosis following complete resection, early disease recurrence can occur. An accurate survival prediction model would help refine a follow-up strategy and personalize future adjuvant therapy. We developed a post-operative prediction model based on readily available clinical information for patients with stage I adenocarcinoma.</p><h3>Methods</h3><p>We retrospectively studied the disease-free survival (DFS) of 408 patients with pathologically confirmed low-risk stage I adenocarcinoma of lung who underwent curative resection from 2013 to 2017. A tree-based method was employed to partition the cohort into subgroups with distinct DFS outcome and stepwise risk ratio. These covariates were included in multivariate analysis to build a scoring system to predict disease recurrence. The model was subsequently validated using a 2011–2012 cohort.</p><h3>Results</h3><p>Non-smoker status, stage IA disease, epidermal-growth factor receptor mutants and female gender were associated with better DFS. Multivariate analysis identified smoking status, disease stage and gender as factors necessary for the scoring system and yielded 3 distinct risk groups for DFS [99.4 (95% CI 78.3–125.3), 62.9 (95% CI 48.2–82.0), 33.7 (95% CI 24.6–46.1) months, <em>p</em> < 0.005]. External validation yielded an area under the curve by receiver operating characteristic analysis of 0.863 (95% CI 0.755–0.972).</p><h3>Conclusion</h3><p>The model could categorize post-operative patients using readily available clinical information, and may help personalize a follow-up strategy and future adjuvant therapy.</p>-
dc.languageeng-
dc.publisherWiley-
dc.relation.ispartofRespirology-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectgenetics-
dc.subjectlow risk-
dc.subjectlung cancer-
dc.subjectNSCLC-
dc.subjectstage I non-small cell carcinoma of lung-
dc.titlePrediction model on disease recurrence for low risk resected stage I lung adenocarcinoma-
dc.typeArticle-
dc.identifier.doi10.1111/resp.14508-
dc.identifier.scopuseid_2-s2.0-85156220261-
dc.identifier.volume28-
dc.identifier.issue7-
dc.identifier.spage669-
dc.identifier.epage676-
dc.identifier.eissn1440-1843-
dc.identifier.isiWOS:000975699200001-
dc.identifier.issnl1323-7799-

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