Matthew Hueman1, Huan Wang2, Zhenqiu Liu3, Donald Henson4, Cuong Nguyen5, Dean Park6, Li Sheng7, Dechang Chen4. 1. Department of Surgical Oncology, John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, Maryland, USA. 2. Department of Biostatistics, George Washington University, Washington, District of Columbia, USA. 3. Department of Public Health Sciences, Penn State Cancer Institute, Hershey, Pennsylvania, USA. 4. Department of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA. 5. Department of Pathology, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA. 6. Department of Hematology-Oncology, John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, Maryland, USA. 7. Department of Mathematics, Drexel University, Philadelphia, Pennsylvania, USA.
Abstract
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.
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.
Authors: Kari Chansky; Jean-Paul Sculier; John J Crowley; Dori Giroux; Jan Van Meerbeeck; Peter Goldstraw Journal: J Thorac Oncol Date: 2009-07 Impact factor: 15.609
Authors: Dechang Chen; Huan Wang; Li Sheng; Matthew T Hueman; Donald E Henson; Arnold M Schwartz; Jigar A Patel Journal: J Med Syst Date: 2016-05-17 Impact factor: 4.460
Authors: Aaron M Praiss; Yongmei Huang; Caryn M St Clair; Ana I Tergas; Alexander Melamed; Fady Khoury-Collado; June Y Hou; Jianhua Hu; Chin Hur; Dawn L Hershman; Jason D Wright Journal: Gynecol Oncol Date: 2020-10-02 Impact factor: 5.482
Authors: Mathew T Hueman; Huan Wang; Charles Q Yang; Li Sheng; Donald E Henson; Arnold M Schwartz; Dechang Chen Journal: Cancer Med Date: 2018-07-02 Impact factor: 4.452
Authors: Matthew Hueman; Huan Wang; Zhenqiu Liu; Donald Henson; Cuong Nguyen; Dean Park; Li Sheng; Dechang Chen Journal: Thorac Cancer Date: 2021-03-13 Impact factor: 3.500