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利用专家分割标签和放射组学特征推进癌症基因组图谱胶质细胞瘤 MRI 数据集。

Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.

机构信息

Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USA.

Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USA.

出版信息

Sci Data. 2017 Sep 5;4:170117. doi: 10.1038/sdata.2017.117.

DOI:10.1038/sdata.2017.117
PMID:28872634
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5685212/
Abstract

Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.

摘要

神经胶质瘤属于中枢神经系统肿瘤的一种,由各种不同的子区域组成。在放射影像学中对这些子区域进行金标准标记对于临床和计算研究都至关重要,包括放射组学和放射基因组学分析。为此,我们发布了来自癌症基因组图谱(TCGA)多机构神经胶质瘤数据集的所有术前多模态磁共振成像(MRI)(n=243)的分割标签和放射组学特征,这些数据可在癌症成像档案库(TCIA)中公开获取。术前扫描通过放射学评估在胶质母细胞瘤(TCGA-GBM,n=135)和低级别神经胶质瘤(TCGA-LGG,n=108)两个集合中被识别出来。胶质瘤子区域标签由自动化的最先进方法生成,并由经过专业认证的神经放射科医生进行手动修订。根据手动修订的标签提取了大量的放射组学特征。这组标签和特征应能 i)直接利用 TCGA/TCIA 神经胶质瘤数据集进行可重复、可重现和可比的定量研究,从而实现新的预测、预后和诊断评估,以及 ii)评估计算机辅助分割方法的性能,并与我们的最先进方法进行比较。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5728/5685212/140ebfc034c9/sdata2017117-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5728/5685212/9ac77033dd85/sdata2017117-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5728/5685212/88782a88389c/sdata2017117-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5728/5685212/140ebfc034c9/sdata2017117-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5728/5685212/9ac77033dd85/sdata2017117-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5728/5685212/88782a88389c/sdata2017117-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5728/5685212/140ebfc034c9/sdata2017117-f3.jpg

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