Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning

作者：Charles Yang, Huan Wang, Zhenqiu Liu, Matthew T. Hueman, Aadya Bhaskaran, Donald E. Henson, Sheng Li, Dechang Chen · 发表于：PLoS ONE · 年份：2021 · DOI：10.1371/journal.pone.0257949 · 被引用次数：13 · 研究领域：Cutaneous Melanoma Detection and Management、AI in cancer detection、Infrared Thermography in Medicine

BACKGROUND: Integrating additional factors into the TNM staging system is needed for more accurate risk classification and survival prediction for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system. METHODS AND FINDINGS: Cancer-specific survival data for cutaneous melanoma with at least a 5 years follow-up were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and split into the training set (40,781 cases) and validation set (5,390 cases). Five factors were studied: the primary tumor (T), regional lymph nodes (N), distant metastasis (M), age (A), and sex (S). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to the training set to generate prognostic groups. Utilizing only T, N, and M, a basic prognostic system was built where patients were stratified into 10 prognostic groups with well-separated survival curves, similar to 10 AJCC stages. These 10 groups had a significantly higher accuracy in survival prediction than 10 stages (C-index = 0.7682 vs 0.7643; increase in C-index = 0.0039, 95% CI = (0.0032, 0.0047); p-value = 7.2×10-23). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman's rank correlation coefficient = 0.8316; p-value = 4.5×10-13). With additional information from A and S, a more advanced prognostic...