This registry aims to collect clinical, molecular and histopathology imaging including detailed survival data, clinical parameters, molecular pathology (1p/19q codeletion, MGMT methylation, IDH and TERTp mutations, etc) and images of HE slices in primary gliomas. By leveraging artificial intelligence, this registry will seek to construct and refine hstopathology imaging based algorithms that able to predict patients' survivals in the frame of molecular pathology or subgroups of gliomas.
Non-invasive and precise prediction for survivals of glioma patients is challenging. With the development of artificial intelligence, much more potential lies in the histopathology images of HE slices in primary gliomas could be excavated to aid prediction of patients' prognosis in the frame of molecular pathology of gliomas. The creation of a registry for primary glioma with detailed survival data, molecular pathology, histopathology image data and with sufficient sample size for deep learning (\>1000) provides opportunities for personalized prediction of survival of glioma patients with non-invasiveness and precision.
Study Type
OBSERVATIONAL
Enrollment
3,500
Histopathology images based survival prediction of glioma patients in the frame of molecular pathology by leveraging AI
Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
Zhengzhou, Henan, China
RECRUITINGAUC of survival prediction performance
AUC of survival prediction performance=sensitivity+specificity-1
Time frame: up to 10 years
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