Survival nomogram for medulloblastoma and multi-center external validation cohort

Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. We analyzed data from 2,322 medulloblastoma patients using the SEER database and randomly divided the dataset into training and testing datasets in a 7:3 ratio. We chose three models to build, one based on neural networks (DeepSurv), one based on ensemble learning that Random Survival Forest (RSF), and a typical Cox Proportional-hazards (CoxPH) model. The DeepSurv model outperformed the RSF and classic CoxPH models with C-indexes of 0.751 and 0.763 for the training and test datasets. Additionally, the DeepSurv model showed better accuracy in predicting 1-, 3-, and 5-year survival rates (AUC: 0.767–0.793). Therefore, our prediction model based on deep learning algorithms can more accurately predict the survival rate and survival period of medulloblastoma compared to other models.

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Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis

Sun,

2024

Preprint

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Background Medulloblastoma is a malignant neuroepithelial tumor of the central nervous system. Accurate prediction of prognosis is essential for therapeutic decisions in medulloblastoma patients. Several prognostic models have been developed using multivariate Cox regression to predict the1-, 3- and 5-year survival of medulloblastoma patients, but few studies have investigated the results of integrating deep learning algorithms. Compared to simplifying predictions into binary classification tasks, modelling the probability of an event as a function of time by combining it with deep learning may provide greater accuracy and flexibility. Methods Patients diagnosed with medulloblastoma between 2000 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) registry. Three models—one based on neural networks (DeepSurv), one based on ensemble learning (random survival forest [RSF]), and a typical Cox proportional-hazards (CoxPH) model—were selected for training. The dataset was randomly divided into training and testing datasets in a 7:3 ratio. The model performance was evaluated utilizing the concordance index (C-index), Brier score and integrated Brier score (IBS). The accuracy of predicting 1-, 3- and 5- year survival was assessed using receiver operating characteristic curves (ROC), and the area under the ROC curves (AUC). Results The 2,322 patients with medulloblastoma enrolled in the study were randomly divided into the training cohort (70%, n = 1,625) and the test cohort (30%, n = 697). There was no statistically significant difference in clinical characteristics between the two cohorts (p > 0.05). We performed Cox proportional hazards regression on the data from the training cohort, which illustrated that age, race, tumour size, histological type, surgery, chemotherapy, and radiotherapy were significant factors influencing survival (p < 0.05). The Deepsurv outperformed the RSF and classic CoxPH models with C-indexes of 0.763 and 0.751 for the training and test datasets. The DeepSurv model showed better accuracy in predicting 1-, 3- and 5-year survival (AUC: 0.805–0.838). Conclusion The predictive model based on a deep learning algorithm that we have developed can exactly predict the survival rate and duration of medulloblastoma.

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Survival nomogram for medulloblastoma and multi-center external validation cohort

Cited by 3 publications

References 23 publications

Tenovin-6 exhibits inhibitory effects on the growth of Sonic Hedgehog (SHH) medulloblastoma, as evidenced by both in vitro and in vivo studies

Tenovin-6 exhibits inhibitory effects on the growth of Sonic Hedgehog (SHH) medulloblastoma, as evidenced by both in vitro and in vivo studies

Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis

Deep learning models for predicting the survival of patients with medulloblastoma based on a surveillance, epidemiology, and end results analysis

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