Machine learning-based treatment outcome prediction in head and neck cancer using integrated noninvasive diagnostics.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Limburg, The Netherlands.
- Department of Radiology, Hospital Fundacion Jimenez-Diaz, Madrid, Spain.
- Department of Vascular and Interventional Radiology, Hospital Universitario de Navarra, Pamplona, Navarra, Spain.
- Department of Radiotherapy, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Department of Head and Neck Oncology and Surgery, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Department of Machine Learning and Neural Computing, Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Gelderland, The Netherlands.
- Faculty of Health Science, University of Southern Denmark, Odense, Denmark.
- The Netherlands Cancer Institute, Amsterdam, The Netherlands.
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands. [email protected].
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, Limburg, The Netherlands. [email protected].
Abstract
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. 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. 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). 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.