Abstract

PURPOSE

Recurrent and metastatic head and neck squamous cell carcinoma (RM-HNSCC) is a challenging malignant disease due to limited treatment options and heterogeneous therapeutic responses. Despite Immunotherapy with PD-1 inhibitors such as Nivolumab and Pembrolizumab has become a standard of care, it is still complex and multifactorial to make a clinical decision in giving the patient its best individualized therapy. This study refines the use of Bayesian networks (BN) in application of supporting therapy selection in RM-HNSCC, integrating clinical, pathological, and molecular features.

METHODS

The previously introduced immune-oncologic BN for head and neck cancer was rechallenged by clinical data from 82 patients with RM-HNSCC. Therapy decisions (Nivolumab vs. Pembrolizumab) were calculated from the primary patient data. Model performance was assessed by comparing predicted versus actual therapy using retrospective data.

RESULTS

The reevaluation of the immune-oncologic BN revealed an inconsistency in the conditional probabilities of the initial model leading to a correction. Consequently, the model achieved 94.7% predictive accuracy for Nivolumab administration (18/19 cases) and 95.2% (60/63) for Pembrolizumab, yielding an overall prediction accuracy of 95.1% with a Cohens Kappa of 0.859. The model provided transparent reasoning paths for therapy selection and enabled hypothetical simulations in clinical scenarios.

CONCLUSION

The immune-oncologic BN offers a promising approach to enhance personalized immunotherapy selection in RM-HNSCC patients by integrating diverse clinical variables into an interpretable decision support framework. The results support further prospective validation and clinical integration. Given the increasing complexity of biomarker-driven therapy, probabilistic models may contribute to more consistent and evidence-aligned treatment decisions.

MeSH

Cells, Carcinoma, Squamous Cell, Decision Making, Immunotherapy, Squamous Cell Carcinoma Of Head And Neck, Therapeutics