Development and Validation of Prognostic Models Using Radiomic Features from Pre-Treatment Positron Emission Tomography (PET) Images in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients

Simple Summary Time-to-event analysis holds significant relevance for diseases like cancer since accurate disease prognosis is crucial for better patient management and for personalizing treatment. In recent years, survival models using machine learning (ML)-based tools have shown promise in cancer prognosis. We compared four survival models in the ML framework to predict adverse outcomes-all-cause mortality (ACM), locoregional recurrence/residual disease (LR), and distant metastasis (DM)-in head and neck cancer patients. Using radiomic features from pre-treatment positron emission tomography (PET) images, we assessed the performance of these models in an external independent validation cohort. The best-performing model for each outcome was identified based on the highest concordance index and the lowest error in training data. The penalized Cox model for ACM and DM and the random forest model for LR showed promising results. Further training and validation of these models in a larger cohort is required for clinical implementation.Abstract High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models-penalized Cox model, random forest, gradient boosted model and support vector machine-to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months of follow-up, respectively. We developed models with five-fold cross-validation, selected the best-performing model for each outcome based on the concordance index (C-statistic) and the integrated Brier score (IBS) and validated them in an independent cohort of 102 patients. The penalized Cox model demonstrated better performance for ACM (C-statistic = 0.70, IBS = 0.12) and DM (C-statistic = 0.70, IBS = 0.08) while the random forest model displayed better performance for LR (C-statistic = 0.76, IBS = 0.07). We conclude that the ML-based prognostic model can aid clinicians in quantifying prognosis and determining effective treatment strategies, thereby improving favorable outcomes in HNSCC patients.