Abstract
Purpose
Advanced cervical cancer (aCC) is aggressive and has a low 5-year survival rate. A novel integrative model needs to be developed to predict survival and guide clinical practice.
Procedures:
Patients were retrospectively studied from July 2014 to August 2016. The dataset was randomly split into training and validation cohorts. A deep learning model based on a Vision Transformer (ViT) and Recurrent Neural Network (RNN) was built to output a prognostic score (Rad-score) based on CT images. After combining Rad-score with clinical and pathological characteristics, Cox regression analysis was performed to establish the clinical model, radiomics model and integrative model, respectively, and the C-index was calculated. The time-dependent C-index curve, receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were drawn to evaluate the performance of the model. Finally, a Nomogram was drawn to visualize the integrative model and develop the risk stratification.
Results
396 patients were eventually enrolled in the study. The Cox regression analysis indicated that the Rad-score was an independent prognostic factor (HR = 4.06, 95% CI: 2.46–6.70, p < 0.001). Kaplan-Meier analysis showed that Rad-score allows significant risk stratification of patients. The integrative model achieved better performance in training (C-index: 0.784[0.733–0.835]) and validation cohorts (C-index: 0.726[0.677–0.785]) showed good survival prediction performance, which was superior to clinical model (training cohort: 0.686[0.625–0.747], validation cohort: 0.632[0.569–0.695]) and radiomics model (training cohort: 0.730[0.675–0.785], validation cohort: 0.723[0.658–0.788]). In addition, the ROC curve, time-dependent C-index curve, calibration curve and DCA curve of the integrative model all showed significant predictive performance advantages. Finally, the Nomogram visualized the survival prediction model. Kaplan-Meier curve showed the risk stratification had significant clinical value.
Conclusions
The integrative model and risk stratification based on Rad-score, clinical and pathological characteristics can be widely used in clinical practice to provide reliable clinical information for medical decisions.