Interpretable survival network for progression risk analysis of multimodality imaging biomarkers in poor-prognosis head and neck cancers.

ABSTRACT: This study explores the predictive utility of multi-time point, multi-modality quantitative imaging biomarkers (QIBs) and clinical factors in patients with poor-prognosis head and neck cancers (HNCs) using interpretable machine learning. We examined 93 patients with p16 + oropharyngeal squamous cell carcinoma or locally advanced p16- HNCs enrolled in a phase II adaptive radiation dose escalation trial. FDG-PET and multiparametric MRI scans were conducted before radiation therapy and at the 10th fraction (2 weeks). A survival network analyzed MRI and PET-derived biomarkers such as gross tumor volume (GTV), blood volume (BV), and metabolic tumor volume (MTV₅₀), along with clinical factors to predict local (LF) and distant failures (DF). Feature attributions and interactions were a

SUBMITTER: Wei L 

PROVIDER: S-EPMC11612283 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature