Data From REMBRANDT

Finding better therapies for the treatment of brain tumors is hampered by the lack of consistently obtained molecular data in a large sample set and the ability to integrate biomedical data from disparate sources enabling translation of therapies from bench to bedside. Hence, a critical factor in the advancement of biomedical research and clinical translation is the ease with which data can be integrated, redistributed, and analyzed both within and across functional domains. Novel biomedical informatics infrastructure and tools are essential for developing individualized patient treatment based on the specific genomic signatures in each patient's tumor. The Repository of Molecular Brain Neoplasia Data (REMBRANDT) is aimed at facilitating discovery by connecting the dots between clinical information and genomic characterization data. Rembrandt contains data generated through the Glioma Molecular Diagnostic Initiative from 874 glioma specimens comprising approximately 566 gene expression arrays, 834 copy number arrays, and 13,472 clinical phenotype data points. These data are currently housed in Georgetown University's G-DOC System and are described in a related manuscript. This image collection was created as a companion data set to augment the larger REMBRANDT project. It contains the pre-surgical magnetic resonance (MR) multi-sequence images from 130 REMBRANDT patients. More Description

开发更优质的脑肿瘤治疗方案，其进程受限于两大核心障碍：一是难以获取大样本量的标准化分子数据，二是缺乏整合多源生物医学数据的能力——而后者本可推动治疗方案从实验室研发向临床应用的转化。因此，实现生物医学研究进展与临床转化的关键要素之一，是能够便捷地在功能域内部及跨功能域间完成数据的整合、再分发与分析。依托每位患者肿瘤的特异性基因组特征开发个性化诊疗方案，离不开新型生物医学信息学基础设施与工具的支撑。
分子脑肿瘤数据资源库（Repository of Molecular Brain Neoplasia Data, REMBRANDT）旨在通过关联临床信息与基因组表征数据，助力科研发现。该资源库包含由神经胶质瘤分子诊断倡议（Glioma Molecular Diagnostic Initiative）生成的874份神经胶质瘤样本数据，涵盖约566组基因表达芯片数据、834组拷贝数阵列数据以及13472条临床表型数据点。目前这些数据存储于乔治城大学的G-DOC系统中，相关细节已在一篇关联手稿中详述。
本图像集作为配套数据集制作，用于扩充REMBRANDT主项目的内容，其包含130名REMBRANDT研究受试者的术前多序列磁共振（magnetic resonance, MR）影像数据。更多描述见后续内容。

搜集汇总

背景与挑战

背景概述

该数据集是REMBRANDT项目的一部分，专注于脑肿瘤（胶质瘤）研究，整合了临床信息、基因组数据（如基因表达和拷贝数阵列）以及术前磁共振影像。它旨在通过多模态数据连接，促进个性化治疗方法的开发，数据来源于874个样本和130名患者的影像。