Artificial intelligence-powered real-time multimodal model for predicting recurrence and survival in head and neck...

Head and neck squamous-cell carcinoma (HNSCC) accounts for ∼5.3% of cancer-related mortality worldwide, with an estimated 890 000 new diagnoses and 450 000 deaths annually. Despite curative-intent therapy, 10% to 50% of patients experience recurrence. Prognosis for recurrent or metastatic disease is poor, with limited treatment options, underscoring the need for accurate prognostic models to guide treatment escalation or de-escalation and avoid over-treatment.

We conducted a multicenter prognostic study of patients undergoing curative-intent surgery at Samsung Medical Center and Massachusetts Eye and Ear Infirmary/Massachusetts General Hospital from 2008 to 2024. Baseline clinicopathologic variables were integrated with longitudinal laboratory measurements from surveillance. A random 80/20 split defined development and internal-validation cohorts. Using XGBoost, we trained two models to predict recurrence-free survival (RFS) and overall survival (OS) at 1, 2, 3, 4, and 5 years from each visit.

A total of 975 patients with HNSCC (oral cavity, oropharyngeal, hypopharyngeal, and laryngeal subsites) were included. The areas under the curve (AUCs) for predicting 1-, 2-, 3-, 4-, and 5-year RFS from the surveillance time point were 0.785 (sensitivity, 72.8%; specificity, 71.5%), 0.831 (79.7%; 73.7%), 0.788 (74.0%; 73.3%), 0.769 (72.6%; 70.5%), and 0.795 (72.1%; 74.7%), respectively. For OS prediction, AUCs were 0.788 (72.1%; 73.6%), 0.797 (75.7%; 71.8%), 0.796 (81.0%; 68.4%), 0.820 (77.5%; 76.5%), and 0.815 (75.8%; 75.8%), respectively. In subgroup analysis, the model showed strong OS prediction in human papilloma virus (HPV)-positive oropharyngeal cancer, with AUCs of 0.943, 0.736, 0.699, 0.835, and 0.765 at 1-, 2-, 3-, 4-, and 5-years, respectively. In non-HPV-positive HNSCC, OS AUCs ranged from 0.780 to 0.813 and RFS AUCs from 0.774 to 0.830 across the same time points.

In this multicenter study, an artificial intelligence (AI)-powered model using multimodal and longitudinal data accurately predicted RFS and OS at multiple time points following curative-intent surgery for HNSCC.