TY - JOUR
T1 - Expanding TNM for lung cancer through machine learning
AU - Hueman, Matthew
AU - Wang, Huan
AU - Liu, Zhenqiu
AU - Henson, Donald
AU - Nguyen, Cuong
AU - Park, Dean
AU - Sheng, Li
AU - Chen, Dechang
N1 - Publisher Copyright:
© 2021 The Authors. Thoracic Cancer published by China Lung Oncology Group and John Wiley & Sons Australia, Ltd.
PY - 2021/5
Y1 - 2021/5
N2 - 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.
AB - 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.
KW - C-index
KW - lung cancer
KW - machine learning
KW - staging
KW - survival
UR - http://www.scopus.com/inward/record.url?scp=85102469373&partnerID=8YFLogxK
U2 - 10.1111/1759-7714.13926
DO - 10.1111/1759-7714.13926
M3 - Article
C2 - 33713568
AN - SCOPUS:85102469373
SN - 1759-7706
VL - 12
SP - 1423
EP - 1430
JO - Thoracic Cancer
JF - Thoracic Cancer
IS - 9
ER -