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Expanding TNM for lung cancer through machine learning

BACKGROUND: Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorporating additional prognostic factors into the con...

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Autores principales: Hueman, Matthew, Wang, Huan, Liu, Zhenqiu, Henson, Donald, Nguyen, Cuong, Park, Dean, Sheng, Li, Chen, Dechang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons Australia, Ltd 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8088955/
https://www.ncbi.nlm.nih.gov/pubmed/33713568
http://dx.doi.org/10.1111/1759-7714.13926
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author Hueman, Matthew
Wang, Huan
Liu, Zhenqiu
Henson, Donald
Nguyen, Cuong
Park, Dean
Sheng, Li
Chen, Dechang
author_facet Hueman, Matthew
Wang, Huan
Liu, Zhenqiu
Henson, Donald
Nguyen, Cuong
Park, Dean
Sheng, Li
Chen, Dechang
author_sort Hueman, Matthew
collection PubMed
description BACKGROUND: Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorporating additional prognostic factors into the conventional TNM for stratifying patients with lung cancer and evaluating survival. METHODS: Data were extracted from SEER. A total of 77 953 patients were analyzed using factors including primary tumor (T), regional lymph node (N), distant metastasis (M), age, and histology type. Ensemble algorithm for clustering cancer data (EACCD) and C‐index were applied to generate prognostic groups and expand the current staging system. RESULTS: With T, N, and M, EACCD stratified patients into 11 groups, resulting in a significantly higher accuracy in survival prediction than the 10 AJCC stages (C‐index = 0.7346 vs. 0.7247, increase in C‐index = 0.0099, 95% CI: 0.0091–0.0106, p‐value = 9.2 × 10(−147)). There nevertheless remained a strong association between the EACCD grouping and AJCC staging (rank correlation = 0.9289; p‐value = 6.7 × 10(−22)). A further analysis demonstrated that age and histological tumor could be integrated with the TNM. Data were stratified into 12 prognostic groups with an even higher prediction accuracy (C‐index = 0.7468 vs. 0.7247, increase in C‐index = 0.0221, 95% CI: 0.0212–0.0231, p‐value <5 × 10(−324)). CONCLUSIONS: EACCD can be successfully applied to integrate additional factors with T, N, M for lung cancer patients.
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spelling pubmed-80889552021-05-10 Expanding TNM for lung cancer through machine learning Hueman, Matthew Wang, Huan Liu, Zhenqiu Henson, Donald Nguyen, Cuong Park, Dean Sheng, Li Chen, Dechang Thorac Cancer Original Articles BACKGROUND: Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorporating additional prognostic factors into the conventional TNM for stratifying patients with lung cancer and evaluating survival. METHODS: Data were extracted from SEER. A total of 77 953 patients were analyzed using factors including primary tumor (T), regional lymph node (N), distant metastasis (M), age, and histology type. Ensemble algorithm for clustering cancer data (EACCD) and C‐index were applied to generate prognostic groups and expand the current staging system. RESULTS: With T, N, and M, EACCD stratified patients into 11 groups, resulting in a significantly higher accuracy in survival prediction than the 10 AJCC stages (C‐index = 0.7346 vs. 0.7247, increase in C‐index = 0.0099, 95% CI: 0.0091–0.0106, p‐value = 9.2 × 10(−147)). There nevertheless remained a strong association between the EACCD grouping and AJCC staging (rank correlation = 0.9289; p‐value = 6.7 × 10(−22)). A further analysis demonstrated that age and histological tumor could be integrated with the TNM. Data were stratified into 12 prognostic groups with an even higher prediction accuracy (C‐index = 0.7468 vs. 0.7247, increase in C‐index = 0.0221, 95% CI: 0.0212–0.0231, p‐value <5 × 10(−324)). CONCLUSIONS: EACCD can be successfully applied to integrate additional factors with T, N, M for lung cancer patients. John Wiley & Sons Australia, Ltd 2021-03-13 2021-05 /pmc/articles/PMC8088955/ /pubmed/33713568 http://dx.doi.org/10.1111/1759-7714.13926 Text en © 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Articles
Hueman, Matthew
Wang, Huan
Liu, Zhenqiu
Henson, Donald
Nguyen, Cuong
Park, Dean
Sheng, Li
Chen, Dechang
Expanding TNM for lung cancer through machine learning
title Expanding TNM for lung cancer through machine learning
title_full Expanding TNM for lung cancer through machine learning
title_fullStr Expanding TNM for lung cancer through machine learning
title_full_unstemmed Expanding TNM for lung cancer through machine learning
title_short Expanding TNM for lung cancer through machine learning
title_sort expanding tnm for lung cancer through machine learning
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8088955/
https://www.ncbi.nlm.nih.gov/pubmed/33713568
http://dx.doi.org/10.1111/1759-7714.13926
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