Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-24 @ 4:48 PM
Ignite Modification Date:
2025-12-24 @ 4:48 PM
NCT ID:
NCT04216550
Brief Summary:
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.
Detailed Description:
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:
NCT04216550
Study Brief:
Protocol Section:
NCT04216550