Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features.
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DOI：
10.1038/sdata.2017.117
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发表时间：
2017-09-05
期刊：
影响因子：
9.8
通讯作者：
Davatzikos C
中科院分区：
文献类型：
--
作者：
Bakas S;Akbari H;Sotiras A;Bilello M;Rozycki M;Kirby JS;Freymann JB;Farahani K;Davatzikos C
胶质瘤属于中枢神经系统肿瘤的一组，并且由多个亚区域组成。放射成像中这些子区域的金标准标记对于临床和计算研究（包括放射组学和放射基因组学分析）都是必不可少的。为此，我们发布了所有术前多模态磁共振成像（MRI）（n=243）的癌症基因组图谱（TCGA）的多机构胶质瘤集合的分割标签和放射组学特征，可在癌症成像档案（TCIA）中公开获得。通过放射学评估，在胶质母细胞瘤（TCGA-GBM，n=135）和低级别胶质瘤（TCGA-LGG，n=108）集合中确定了术前扫描。神经胶质瘤亚区域标签由自动化最先进的方法产生，并由专家委员会认证的神经放射科医生手动修订。基于手动修订的标签提取了广泛的放射组学特征面板。这组标记和特征应能够i）直接利用TCGA/TCIA神经胶质瘤集合进行可重复、可再现和可比较的定量研究，从而产生新的预测、预后和诊断评估，以及ii）计算机辅助分割方法的性能评估，并与我们的最新方法进行比较。
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.
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DOI：
10.1080/21681163.2015.1029642
发表时间：
2017-01-01
影响因子：
1.6
作者：
Bakas, Spyridon;Chatzimichail, Katerina;Makris, Dimitrios
通讯作者：
Makris, Dimitrios
DOI：
10.1007/978-3-319-30858-6_1
发表时间：
2016
期刊：
Brainlesion : glioma, multiple sclerosis, stroke and traumatic brain injuries. BrainLes (Workshop)
影响因子：
--
作者：
Bakas S;Zeng K;Sotiras A;Rathore S;Akbari H;Gaonkar B;Rozycki M;Pati S;Davatzikos C
通讯作者：
Davatzikos C
影响因子：
19.7
作者：
Akbari H;Macyszyn L;Da X;Wolf RL;Bilello M;Verma R;O'Rourke DM;Davatzikos C
通讯作者：
Davatzikos C
DOI：
10.1158/1078-0432.ccr-16-1871
发表时间：
2017-08-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Bakas S;Akbari H;Pisapia J;Martinez-Lage M;Rozycki M;Rathore S;Dahmane N;O'Rourke DM;Davatzikos C
通讯作者：
Davatzikos C
影响因子：
4.8
作者：
Akbari H;Macyszyn L;Da X;Bilello M;Wolf RL;Martinez-Lage M;Biros G;Alonso-Basanta M;OʼRourke DM;Davatzikos C
通讯作者：
Davatzikos C