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Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning
Journal article   Open access   Peer reviewed

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

Charles Q. Yang, Huan Wang, Zhenqiu Liu, Matthew T. Hueman, Aadya Bhaskaran, Donald E. Henson, Li Sheng and Dechang Chen
PloS one, v 16(9), pp e0257949-e0257949
30 Sep 2021
DOI: https://doi.org/10.1371/journal.pone.0257949
PMID: 34591891
Featured in Collection :   UN Sustainable Development Goals @ Drexel
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https://doi.org/10.1371/journal.pone.0257949View
Published, Version of Record (VoR)CC0 V1.0,  Open

Abstract

Multidisciplinary Sciences Science & Technology Science & Technology - Other Topics
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 = 7.2x10(-23)). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman's rank correlation coefficient = 0.8316; p-value = 4.5x10(-13)). With additional information from A and S, a more advanced prognostic system was established using the training data that stratified patients into 10 groups and further improved the prediction accuracy (C-index = 0.7865 vs 0.7643; increase in C-index = 0.0222, 95% CI = (0.0191, 0.0254); p-value = 8.8x10(-43) ). Both internal validation using the training set and temporal validation using the validation set showed good stratification and a high predictive accuracy of the prognostic systems. Conclusions The EACCD allows additional factors to be integrated into the TNM to create a prognostic system that improves patient stratification and survival prediction for cutaneous melanoma. This integration separates favorable from unfavorable clinical outcomes for patients and improves both cohort selection for clinical trials and treatment management.

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9 citations in Web of Science
6 citations in Scopus

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UN Sustainable Development Goals (SDGs)

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#3 Good Health and Well-Being

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Collaboration types
Domestic collaboration
Web of Science research areas
Oncology
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