Machine learning for survival outcome in head and neck squamous cell carcinoma: a multicenter validation study
A machine learning model combining clinicopathological parameters can predict overall survival in head and neck squamous cell carcinoma, achieving an AUC of 0.76 and an accuracy of 70.0%.
Most head and neck squamous cell carcinoma (HNSCC) cases are diagnosed late, with an increased risk of recurrence and distant metastasis. In recent years, there has been a surge in the development of prognostic and predictive machine learning (ML) models for personalized treatment planning. However, only a small number of these have been externally validated. This study aimed to build a prognostic system by combining clinicopathological parameters and treatment-related factors as integrative inputs to build a machine learning (ML) model using data from the Surveillance, Epidemiology, and End Results (SEER, United States) program. We further validated the developed model using multicenter data obtained from the Thuringian Cancer Registry (Germany) and a multicenter prospective observational study obtained from the Uppsala University Hospital (Sweden) to estimate the overall survival (OS) of patients with HNSCC. Additionally, we explored the complementary prognostic potentials of these input parameters using permutation feature importance (PFI).
Evidence level: Sejtvonalas. Laboratóriumi sejtekben vizsgálták.
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