Advanced-stage tongue squamous cell carcinoma : a machine learning model for risk stratification and treatment planning - Alabi, Rasheed Omobolaji; Elmusrati, Mohammed; Leivo, Ilmo; Almangush, Alhadi; Mäkitie, Antti A. (Taylor & Francis, 2023)
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.
Keywords: chemoradiotherapy; machine learning; overall survival; radiation; SEER; surgery; tongue cancer; radiotherapy; fi=Tietoliikennetekniikka|en=Telecommunications Engineering|