Clinical, patHOlogical and Imaging Project of nEuro-oncology (HOPE)

Primary central nervous system (CNS) tumors, the vast majority (\>90%) occurring in the brain and the remainder occurring in the meninges, spinal cord, and cranial nerves, showing an annual incidence of about 6-8 people per 100,000 population but its effects on health-care systems is out of proportion with incidence due to the substantial high rates of morbidity and mortality. Among which, glioma disease is the most common primary malignant CNS tumor, while the glioblastoma that showed the highest degree of malignancy and the worst prognosis accounts for 70-75%.

The construction goal of this project is to construct a multivariate retrospective CNS tumor database (over 50,000 cases, including 10,000 glioma) integrating clinical information, preoperative magnetic resonance imaging examination and molecular pathological results, and a prospective glioma database (3,000 cases) integrating advanced magnetic resonance sequences and postoperative follow-up. It aims to form a standardized database integrating magnetic resonance imaging, pathological results, and clinical-prognostic information.

Based on the construction of the above standardized database, the specifications for the acquisition of cranial magnetic resonance images, the image segmentation, tumor classification and labeling process, and the expert consensus on database construction and use management of CNS tumors were established. We aim to form a multimodal, large-capacity, high-quality, and rich medical imaging database that conforms to the characteristics of Chinese groups and clinical diagnosis and treatment norms. On this basis, the data are dynamically updated, in-depth mining, and the classification and grading standards of CNS tumor diseases, prognosis judgment criteria and treatment efficacy evaluation system are formulated, and providing comprehensive resources of retrospective data and prospective cohorts for large-scale reasearches, such as classification or treatment intervention predictions.