Biomedizinische Technik. Biomedical engineering · 2026
A nnU-Net-based clinical radiomics model for predicting poor prognosis in sudden deafness.
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Zha Dahui, Guo Shuo, Yu Ping, Hong Fei
Аннотация
OBJECTIVES: To develop and validate a nnU-Net-based clinical radiomics model for predicting poor outcome in patients with sudden sensorineural hearing loss (SSNHL).
METHODS: A retrospective cohort of 124 SSNHL patients undergoing temporal bone high-resolution computed tomography (HRCT) was analyzed (54 good prognosis; 70 poor prognosis). Patients were randomly divided into training (n=87) and test (n=37) sets. The cochlea, vestibule, and internal auditory canal were manually segmented and used to train a nnU-Net 3D full-resolution model. Segmentation performance was evaluated using the Dice similarity coefficient (DSC). Radiomics features were extracted and reduced through variance thresholding, correlation analysis, univariate Cox regression, and random survival forest modeling to construct a radiomics score (Radscore). Independent prognostic factors were identified using multivariate Cox regression. A combined clinical-radiomics nomogram was developed and compared with clinical-only and Radscore-only models using C-index, calibration, and decision curve analysis (DCA).
RESULTS: The nnU-Net achieved DSCs of 0.91 ± 0.07 (training) and 0.73 ± 0.14 (test). Twelve radiomics features were selected. High-risk Radscore and four clinical factors were independent predictors. The combined model showed superior discrimination (C-index: 0.812 training; 0.783 test) and the highest clinical net benefit.
CONCLUSIONS: The nnU-Net-based clinical radiomics model provides accurate prognostic stratification for SSNHL.
METHODS: A retrospective cohort of 124 SSNHL patients undergoing temporal bone high-resolution computed tomography (HRCT) was analyzed (54 good prognosis; 70 poor prognosis). Patients were randomly divided into training (n=87) and test (n=37) sets. The cochlea, vestibule, and internal auditory canal were manually segmented and used to train a nnU-Net 3D full-resolution model. Segmentation performance was evaluated using the Dice similarity coefficient (DSC). Radiomics features were extracted and reduced through variance thresholding, correlation analysis, univariate Cox regression, and random survival forest modeling to construct a radiomics score (Radscore). Independent prognostic factors were identified using multivariate Cox regression. A combined clinical-radiomics nomogram was developed and compared with clinical-only and Radscore-only models using C-index, calibration, and decision curve analysis (DCA).
RESULTS: The nnU-Net achieved DSCs of 0.91 ± 0.07 (training) and 0.73 ± 0.14 (test). Twelve radiomics features were selected. High-risk Radscore and four clinical factors were independent predictors. The combined model showed superior discrimination (C-index: 0.812 training; 0.783 test) and the highest clinical net benefit.
CONCLUSIONS: The nnU-Net-based clinical radiomics model provides accurate prognostic stratification for SSNHL.