Image-Level Data Augmentation for Radiomics-Based Classification of Vital Versus Non-Vital Persistent Cervical Lymph Nodes After Chemoradiotherapy in HNSCC.
Authors
Affiliations (4)
Affiliations (4)
- Department of Otorhinolaryngology-Head and Neck Surgery, Medical University of Innsbruck, 6020 Innsbruck, Austria.
- Datathings, 5, Rue de L'Industrie, L-1811 Luxembourg, Luxembourg.
- University Hospital of Radiology, Medical University of Innsbruck, 6020 Innsbruck, Austria.
- University Hospital of Radio-Oncology, Medical University of Innsbruck, 6020 Innsbruck, Austria.
Abstract
<b>Background:</b> Distinguishing vital from non-vital persistent cervical lymph nodes after chemoradiotherapy in HNSCC remains clinically challenging. We investigated whether image-level data augmentation improves CT-based radiomics classification for this task. <b>Methods:</b> We evaluated eight augmentation strategies and their 28 pairwise combinations in 55 patients, using Bayesian hyperparameter tuning with Optuna for parameter optimization. A radiomics pipeline comprising Radiomics features, five feature selectors, and seven classifiers was assessed using patient-level stratified 5-fold cross-validation. Configurations were ranked using a composite score defined as the mean of AUC, ACC and F1-score. <b>Results:</b> Feature selection improved the composite score from 0.659 to 0.742. The best augmented configuration, Window Contrast Variation, achieved a composite score of 0.803 and an AUC of 0.831, corresponding to an 8.2% relative point-estimate gain over feature selection alone and a 21.9% gain over the no-selection baseline when feature selection and augmentation were combined. <b>Conclusions:</b> These findings suggest that feature selection with optimized augmentation may enhance radiomics-based lymph node classification. However, individual augmentation-versus-baseline differences did not reach statistical significance in this limited sample, requiring confirmation in larger cohorts.