Development and external validation of a nomogram for overall survival in oral tongue squamous cell carcinoma using machine learning-assisted feature selection
Scientific Reports, Published online: 05 August 2026; doi:10.1038/s41598-026-65230-z Development and external validation of a nomogram for overall survival in oral tongue squamous cell carcinoma using machine learning-assisted feature selection
A retrospective study employed machine learning algorithms to identify prognostic factors for overall survival in oral tongue squamous cell carcinoma (OTSCC). Utilizing data from 5746 patients in the SEER database, researchers developed a Cox-based nomogram incorporating age, sex, tumor grade, and tumor-node-metastasis stage. The multivariable model outperformed a TNM-only model across all cohorts, with C-indexes of 0.717, 0.720, and 0.711 in training, internal validation, and external validation cohorts, respectively.
The external validation cohort, comprising 174 patients from the Bengbu Medical University, showed good calibration and suggested potential net clinical benefit. This externally validated nomogram may aid in pre-operative risk assessment and prognostic stratification for OTSCC patients. Support for this work came from the Bengbu Science and Technology Innovation Guidance Project, Key Special Projects in Translational Medicine at Bengbu Medical University, and the University Cooperative Research and Public Health Collaborative Innovation Project of Anhui Province.
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