Prediction model on disease recurrence for low risk resected stage I lung adenocarcinoma
© 2023 The Authors. Respirology published by John Wiley & Sons Australia, Ltd on behalf of Asian Pacific Society of Respirology..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:28 |
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| Enthalten in: |
Respirology (Carlton, Vic.) - 28(2023), 7 vom: 30. Juli, Seite 669-676 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kwok, Wang Chun [VerfasserIn] |
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| Links: |
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| Themen: |
Genetics |
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| Anmerkungen: |
Date Completed 19.06.2023 Date Revised 07.12.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1111/resp.14508 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM356101460 |
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| 500 | |a Date Revised 07.12.2023 | ||
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a © 2023 The Authors. Respirology published by John Wiley & Sons Australia, Ltd on behalf of Asian Pacific Society of Respirology. | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 520 | |a 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) | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a NSCLC | |
| 650 | 4 | |a genetics | |
| 650 | 4 | |a low risk | |
| 650 | 4 | |a lung cancer | |
| 650 | 4 | |a stage I non-small cell carcinoma of lung | |
| 700 | 1 | |a Ma, Ting Fung |e verfasserin |4 aut | |
| 700 | 1 | |a Ho, James Chung Man |e verfasserin |4 aut | |
| 700 | 1 | |a Lam, David Chi Leung |e verfasserin |4 aut | |
| 700 | 1 | |a Sit, Ko Yung |e verfasserin |4 aut | |
| 700 | 1 | |a Ip, Mary Sau Man |e verfasserin |4 aut | |
| 700 | 1 | |a Au, Timmy Wing Kuk |e verfasserin |4 aut | |
| 700 | 1 | |a Tam, Terence Chi Chun |e verfasserin |4 aut | |
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