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Journal article
Expanding TNM for lung cancer through machine learning
Thoracic cancer, v 12(9), pp 1423-1430
May 2021
PMID: 33713568
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
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.
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.
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
). There nevertheless remained a strong association between the EACCD grouping and AJCC staging (rank correlation = 0.9289; p-value = 6.7 × 10
). 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
).
EACCD can be successfully applied to integrate additional factors with T, N, M for lung cancer patients.
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Details
- Title
- Expanding TNM for lung cancer through machine learning
- Creators
- Matthew Hueman - Walter Reed National Military Medical CenterHuan Wang - George Washington UniversityZhenqiu Liu - Penn State Cancer InstituteDonald Henson - Uniformed Services University of the Health SciencesCuong Nguyen - Uniformed Services University of the Health SciencesDean Park - Walter Reed National Military Medical CenterLi Sheng - Drexel UniversityDechang Chen - Uniformed Services University of the Health Sciences
- Publication Details
- Thoracic cancer, v 12(9), pp 1423-1430
- Publisher
- Wiley
- Grant note
- Using Dendrograms to Create Prognostic Systems / John P. Murtha Cancer Center Research Program Four Diamonds Fund from Penn State University / Pennsylvania State University
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Mathematics
- Web of Science ID
- WOS:000628345200001
- Scopus ID
- 2-s2.0-85102469373
- Other Identifier
- 991019169703804721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Oncology
- Respiratory System