A prognostic model integrating radiomics and deep learning based on CT for survival prediction in laryngeal squamous cell carcinoma.

ABSTRACT: Accurate prognostic prediction is crucial for patients with laryngeal squamous cell carcinoma (LSCC) to guide personalized treatment strategies. This study aimed to develop a comprehensive prognostic model leveraging clinical factors alongside radiomics and deep learning (DL) based on CT imaging to predict recurrence-free survival (RFS) in LSCC patients. We retrospectively enrolled 349 patients with LSCC from Center 1 (training set: n = 189; internal testing set: n = 82) and Center 2 (external testing set: n = 78). A combined model was developed using Cox regression analysis to predict RFS in LSCC patients by integrating independent clinical risk factors, radiomics score (RS), and deep learning score (DLS). Meanwhile, separate clinical, radiomics, and DL models were also constructed for co

SUBMITTER: Jiang H 

PROVIDER: S-EPMC12357885 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature