Development and validation of the risk stratification based on deep learning and radiomics to predict survival of advanced cervical cancer.

ABSTRACT: Advanced cervical cancer (aCC) is associated with a poor prognosis. This study aimed to develop and validate a deep learning-based risk stratification model to predict overall survival in aCC patients using pre-treatment CT images. A total of 396 patients with aCC were retrospectively enrolled and randomly allocated into training (n = 198) and validation (n = 198) cohorts. A deep learning model integrating a Vision Transformer (ViT) for feature extraction and a Recurrent Neural Network (RNN) for sequence modeling was developed to generate a prognostic radiomic signature (Rad-score) from baseline CT scans. The Rad-score was incorporated into a Cox proportional hazards model alongside clinical variables to construct an integrative nomogram. The model's performance was evaluated using the con

SUBMITTER: Guy MM 

PROVIDER: S-EPMC12639044 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature