Radiomics- and dosiomics-based prediction of treatment failure after chemoradiotherapy for pharyngeal squamous cell carcinoma patients

Abstract

Abstract We investigated an approach to predict treatment failure after chemoradiation using radiomics and dosiomics in patients with pharyngeal squamous cell carcinoma (PSCC). Data of 172 patients were collected from a public database. The cases were divided into the training (n = 140) and testing (n = 32) datasets. A total of 1027 features, including radiomic (R) features (first-order, texture, and wavelet features), local binary pattern-based (L) features, and topological (T) features, were extracted from the computed tomography (CT) image and dose distribution (DD) of the gross tumor volume. The Coxnet algorithm was employed on the training dataset to select significant features. Twenty-One treatment failure prediction models were constructed based on Rad scores. The overall adequacy of the treatment failure prediction models was evaluated using the concordance index (C-index) and statistically significant differences (p-values) between the Kaplan–Meier curves of the two risk groups. The DD-based LT (DD-LT) model and the combined CT with DD-based RLT (CD-RLT) model showed statistically significant differences in the Kaplan–Meier curves. The C-indices were 0.74 for the DD-LT model and 0.64, the CD-RLT model. These models exhibited higher performance than the conventional approach. The proposed radiomics/dosiomics-based model may be more accurate in predicting treatment failure after chemoradiation in patients with PSCC.