This proof-of-principle retrospective study included 558 surgically treated HNSCC patients. Baseline clinical data, routine blood markers, and MRI-based radiomic features were collected before treatment. Additional postsurgical treatments within one year were also recorded. Random forest classifiers were trained to predict one-year survival and feeding tube dependence. Model explainability was assessed using Shapley Additive exPlanation (SHAP) values.
Clinical data appeared to be the strongest predictor of one-year survival in surgically treated HNSCC, although overall predictive performance was moderate. Postsurgical treatment information played a key role in predicting tube feeding dependence. While multimodal integration did not enhance overall model performance, it showed modest gains for weaker individual modalities, suggesting potential complementarity that warrants further investigation.
Using tenfold stratified cross-validation, clinical data showed the highest predictive performance for survival (AUC = 0.75 ± 0.10; p < 0.001). Blood (AUC = 0.67 ± 0.17; p = 0.001) and imaging (AUC = 0.68 ± 0.16; p = 0.26) showed moderate performance, and multimodal integration did not improve predictions (AUC = 0.68 ± 0.16; p = 0.38). For feeding tube dependence, all modalities had low predictive power (AUC ≤ 0.66; p > 0.05). However, postsurgical treatment information outperformed all other modalities (AUC = 0.67 ± 0.07; p = 0.002), but had the lowest predictive value for survival (AUC = 0.57 ± 0.11; p = 0.08).
Accurate prediction of treatment outcomes is crucial for personalized treatment in head and neck squamous cell carcinoma (HNSCC). Beyond one-year survival, assessing long-term enteral nutrition dependence is essential for optimizing patient counseling and resource allocation. This preliminary study aimed to predict one-year survival and feeding tube dependence in surgically treated HNSCC patients using classical machine learning.
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