CT-based radiomics and deep learning models for predicting thyroid cartilage invasion and patient prognosis in laryngeal carcinoma.

ABSTRACT: Accurate assessment of thyroid cartilage invasion is crucial for treatment decision-making and prognosis evaluation in laryngeal squamous cell carcinoma (LSCC). This study aimed to compare the performance of the radiomics and deep learning (DL) models for predicting thyroid cartilage invasion in LSCC patients, and evaluate prognostic value of the optimal predictive model. A total of 418 pathologically confirmed LSCC patients from two centers were enrolled and divided into a training cohort (n = 247), an internal validation cohort (n = 110), and an external validation cohort (n = 61). Models were developed based on venous-phase CT images and compared with two radiologists. A nomogram incorporating the optimal model and clinical risk factors was also constructed. Additionally, the prognostic

SUBMITTER: Chen X 

PROVIDER: S-EPMC12623722 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature