Collaborative machine learning-guided overall survival prediction of oral squamous cell carcinoma
Year: 2024
Type: article
Study design: Observational Study
Abstract: BACKGROUND: There is a lack of prognosticators of overall survival (OS) for Oral Squamous Cell Carcinoma (OSCC). OBJECTIVES: We examined collaborative machine learning (cML) in estimating the OS of OSCC patients. The prognostic significance of the clinicopathological parameters was examined. METHODOLOGY: Altogether, 9439 OSCC patients were extracted from the Surveillance, Epidemiology, and End Results database (US). Five ML models - voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression were used to predict OS. Three of these ML algorithms were combined to form a cluster of cML models. The performance of the cML was compared with the best performing individual ML algorithm following model training. RESULTS: The performance accuracy of the voting ensemble, stacked ensemble, extreme gradient boosting, light boosting, and logistic regression models was 70.2%, 69.9%, 69.1%, 69.4%, and 69.5% respectively, following model training. When the voting ensemble model was compared with cML using temporal validation, the cML showed a comparable performance accuracy. The most significant prognostic factors were age of the patient at diagnosis, T stage, tumor grade, marital status, gender, primary site, surgery, N stage, radiotherapy, ethnicity, chemotherapy, and M stage. CONCLUSIONS: cML appears to give reliability to the final prediction and thereby may mark a paradigm shift from model individualism to a more cooperative paradigm. This approach may aid in determining an enhanced individualized treatment for OSCC patients.
Source: Acta Oto-Laryngologica
Language: English
Topic: Head and Neck Cancer Studies
Subfield: Otorhinolaryngology
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W4405942714