Abstract
This study investigates whether dedicated tumor segmentation for radiomics (TRAD) offers any advantage over gross tumor volume (GTV) in CT radiomics for predicting adenoid cystic carcinoma (ACC) progression after proton therapy (PT). Fifty-six patients with histologically proven salivary gland ACC were included, and 107 original features were extracted using PyRadiomics v3.1.0. Signatures were selected (n = 3) with sequential backward elimination using multiple classifiers, all optimized for improving cross-validated area under the ROC curve (AUC). Signature similarity was quantified using the Spearman correlation coefficient. Random forest (RF) yielded the best discriminative performance, with no statistical difference in AUCs between contour choices (GTV: 0.87 vs. TRAD: 0.80; ΔAUCmedian = 0.0, p = 0.589). Time-to-event analysis confirmed both signatures stratified patients into distinct progression-free survival risk groups (Log-rank p < 0.0001) and demonstrated robust prognostic accuracy (GTV: C-index = 0.74, HR = 11.63; TRAD: C-index = 0.72, HR = 7.01). Biologically, GTV and TRAD signatures were borderline associated with perineural spread (p = 0.056) and solid tumor patterns (p = 0.053), respectively. Overall, CT-based radiomics models performed comparably across both segmentation strategies, supporting GTV as a practical and efficient alternative to TRAD for predicting ACC progression after PT.