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. 2021 Jul 7;11:14057. doi: 10.1038/s41598-021-92072-8

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Ensemble model performance over test data. The ensemble model discrimination was evaluated using the AUC metric over the test data for two survival outcomes: (a) Overall Survival (OS) and (b) Recurrence Free Survival (RFS). Comparison is done between a Clinical baseline model using seven clinical covariates: age, hpv status, smoking status, T-category, N-category, therapeutic combination, AJCC staging, and the models including additional model covariates: selected radiomic features (Clinical + rsf/ + cox), and the proposed cluster labels (Clinical + N Clusters). In all cases, the inclusion of the cluster labels outperforms the Clinical model. The models including the cluster labels show comparable performance to the models including a subset of radiomic features while being considerably more parsimonious models.