Expanding TNM for lung cancer through machine learning

Matthew Hueman, Huan Wang, Zhenqiu Liu, Donald Henson, Cuong Nguyen, Dean Park, Li Sheng, Dechang Chen

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
JournalThoracic Cancer
DOIs
StateAccepted/In press - 2021

All Science Journal Classification (ASJC) codes

  • Oncology
  • Pulmonary and Respiratory Medicine

Fingerprint Dive into the research topics of 'Expanding TNM for lung cancer through machine learning'. Together they form a unique fingerprint.

Cite this