In Acta oto-laryngologica
BACKGROUND : A significant number of tongue squamous cell carcinoma (TSCC) patients are diagnosed at late stage.
OBJECTIVES : We primarily aimed to develop a machine learning (ML) model based on ensemble ML paradigm to stratify advanced-stage TSCC patients into the likelihood of overall survival (OS) for evidence-based treatment. We compared the survival outcome of patients who received either surgical treatment only (Sx) or surgery combined with postoperative radiotherapy (Sx + RT) or postoperative chemoradiotherapy (Sx + CRT).
MATERIAL AND METHODS : A total of 428 patients from Surveillance, Epidemiology, and End Results (SEER) database were reviewed. Kaplan-Meier and Cox proportional hazards models examine OS. In addition, a ML model was developed for OS likelihood stratification.
RESULTS : Age, marital status, N stage, Sx, and Sx + CRT were considered significant. Patients with Sx + RT showed better OS than Sx + CRT or Sx alone. A similar result was obtained for T3N0 subgroup. For T3N1 subgroup, Sx + CRT appeared more favorable for 5-year OS. In T3N2 and T3N3 subgroups, the numbers of patients were small to make insightful conclusions. The OS predictive ML model showed an accuracy of 86.3% for OS likelihood prediction.
CONCLUSIONS AND SIGNIFICANCE : Patients stratified as having high likelihood of OS may be managed with Sx + RT. Further external validation studies are needed to confirm these results.
Alabi Rasheed Omobolaji, Elmusrati Mohammed, Leivo Ilmo, Almangush Alhadi, Mäkitie Antti A
2023-Feb-15
SEER, Tongue cancer, chemoradiotherapy, machine learning, overall survival, radiation, radiotherapy, surgery
