Identification of CT based radiomic biomarkers for progression free survival in head and neck squamous cell carcinoma.

This study addresses the limited noninvasive tools for Head and Neck Squamous Cell Carcinoma (HNSCC) progression-free survival (PFS) prediction by identifying Computed Tomography (CT)-based biomarkers for predicting prognosis. A retrospective analysis was conducted on data from 203 HNSCC patients. An ensemble feature selection involving correlation analysis, univariate survival analysis, best-subset selection, and the LASSO-Cox algorithm was used to select functional features, which were then used to build final Cox Proportional Hazards models (CPH). Our CPH achieved a 0.69 concordance index in an external indepedent cohort of 77 patients. The model identified five CT-based radiomics features, Gradient ngtdm Contrast, Log(σ=3)3D-FirstorderRootMeanSquared, Log(σ=0.1)3D-glszm SmallAreaLowGrayLevelEmphasis, Exponential-gldm LargeDependenceHighGrayLevelEmphasis, and Gradient ngtdm Strength as survival biomarkers (p-value < 0.05). These findings contribute to our knowledge of how radiomics can be used to predict the outcome so that treatment plans can be tailored for people with HNSCC to improve their prognosis.