Apatinib, also known as YN968D1, is a small-molecule tyrosine kinase inhibitor (TKI) that selectively binds to and inhibits vascular endothelial growth factor receptor 2 (VEGFR-2). This study aims to collect clinical, radiological and histopathology imaging including detailed radiological data, survival data, clinical parameters, molecular pathology and images of HE slices in patients with recurrent gliomas whose are treated with Apatinib, for evaluating the efficacy and safety of Apatinib. Moreover, by leveraging artificial intelligence, this study seeks to construct and refine MR and histopathology imaging based algorithms that are able to predict the responses to Apatinib of patients with recurrent gliomas.
Effective treatment for recurrent gliomas is still challenging. Malignant gliomas are considered to be one of the most angiogenic cancers and are mostly sustained by vascular endothelial growth factor (VEGF) signaling via its endothelial tyrosine kinase receptor VEGF receptor 2 (VEGFR-2). Apatinib, also known as YN968D1, is a small-molecule tyrosine kinase inhibitor (TKI) that selectively binds to and inhibits VEGFR-2. Apatinib has been demonstrated as monotherapy that prolongs OS in patients with gastric cancers after two or more lines of chemotherapy with moderate, reversible, and easily managed adverse effects. This study aims to collect clinical, radiological and histopathology imaging including detailed radiological data, survival data, clinical parameters, molecular pathology and images of HE slices in patients with recurrent gliomas whose are treated with Apatinib, for evaluating the efficacy and safety of Apatinib. Moreover, by leveraging artificial intelligence, this study also seeks to construct and refine MR and histopathology imaging based algorithms that are able to predict the responses to Apatinib of patients with recurrent gliomas. The creation of a registry for patients with recurrent gliomas treated by Apatinib with detailed survival data, radiological data, histopathology image data and with sufficient sample size for artificial intelligence provides opportunities for personalized prediction of responses to Apatinib.
Study Type
OBSERVATIONAL
Enrollment
600
Apatinib 0.5g orally daily until the untolerable toxicities, disease progression or death
Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University
Zhengzhou, Henan, China
RECRUITINGChanges of Response to Treatment
Response were evaluated with Response Assessment in Neuro-Oncology (RANO) criteria every 1 month after treament.
Time frame: From enrollment to progression of disease. Estimated about 6 months
Progression-Free Survival (PFS)
The length of time from enrollment until the time of progression of disease (PFS, progression-free survival)
Time frame: From enrollment to progression of disease. Estimated about 6 months.
Overall Survival (OS)
The length of time from enrollment until the time of death (OS, overall survival)
Time frame: From enrollment to death of patients. Estimated about 1 year.
Incidence of treatment-related adverse events
The incidence of treatment-related adverse events were graded with the use of the National Cancer Institute Common Terminology Criteria for Adverse Events, version 4.0.
Time frame: Time Frame: 0 to 1 year
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