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

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 comparison. Furthermore, the combined model was represented visually through a nomogram to provide personalized estimation of RFS, with its risk stratification capability evaluated using Kaplan-Meier analysis. The combined model achieved a higher C-index than did the clinical model, radiomics model, and DL model in the internal testing (0.810 vs. 0.634, 0.679, and 0.727, respectively) and external testing sets (0.742 vs. 0.602, 0.617, and 0.729, respectively). Additionally, following risk stratification via nomogram, patients in the low-risk group showed significantly higher survival probabilities compared to those in the high-risk group in the internal testing set [hazard ratio (HR) = 0.157, 95% confidence interval (CI): 0.063-0.392, p < 0.001] and external testing set (HR = 0.312, 95% CI: 0.137-0.711, p = 0.003). The proposed combined model demonstrated a reliable and accurate ability to predict RFS in patients with LSCC, potentially assisting in risk stratification.