AI model helps doctors choose best treatment for advanced tongue cancer
Researchers developed a machine learning system to predict survival odds for patients with advanced tongue cancer, finding that surgery combined with radiation therapy often outperforms chemotherapy. The tool could help oncologists tailor treatment plans and improve outcomes for a disease typically diagnosed late.
Originaltitel: Advanced-stage tongue squamous cell carcinoma: a machine learning model for risk stratification and treatment planning.
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.