Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.
Reproducibility analysis of multi-institutional paired expert annotations and radiomic features of the Ivy Glioblastoma Atlas Project (Ivy GAP) dataset.
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DOI：
10.1002/mp.14556
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发表时间：
2020-12
期刊：
影响因子：
3.8
通讯作者：
Bakas S
中科院分区：
文献类型：
--
作者：
Pati S;Verma R;Akbari H;Bilello M;Hill VB;Sako C;Correa R;Beig N;Venet L;Thakur S;Serai P;Ha SM;Blake GD;Shinohara RT;Tiwari P;Bakas S
Ivy 胶质母细胞瘤图谱项目 (Ivy GAP) 的放射磁共振成像 (MRI) 扫描的可用性为开发用于胶质母细胞瘤 (GBM) 预后/预测应用的放射组学标记物提供了机会。在这项工作中，我们解决了关于开发稳健的放射组学方法的两个关键挑战：（a）缺乏针对胶质母细胞瘤肿瘤亚室（即增强肿瘤、非增强肿瘤核心、瘤周水肿/浸润组织）的可靠分割标签和（b）识别对读者/站点之间的分割变异性具有鲁棒性的“可重复”放射组学特征。从 TCIA 的 Ivy GAP 队列中，我们获得了由宾夕法尼亚大学 (UPenn) 医院和凯斯西储大学 (CWRU) 两名经过委员会认证的神经放射科医生批准的专家注释配对集 (n = 31)。对于这些研究，我们进行了一项再现性研究，评估了这些配对注释之间（a）分割标签和（b）放射组学特征的变异性。放射组学变异性在由 11 700 个放射组学特征组成的综合面板上进行评估，包括强度、体积、形态、基于直方图和纹理参数，为每组注释的配对提取。我们的结果表明 (a) 评估者间高度一致（所有子区室的 DICE 中值≥0.8），以及 (b) 约 24% 的提取放射组学特征与注释变异高度相关（基于 Spearman 等级相关系数）。这些强大的特征主要属于形态（描述形状特征）、强度（捕获强度分布统计数据）和拼贴（捕获梯度方向的异质性）特征系列。我们在 TCIA 的分析结果目录 (https://doi.org/10.7937/9j41-7d44) 上公开提供 (a) 肿瘤亚区室的多机构专家注释、(b) 11 700 个放射组学特征和 (c) 相关的再现性荟萃分析的完整集。 Ivy GAP 的注释和相关元数据发布的目的是使研究人员能够开发基于图像的生物标志物，用于 GBM 的预后/预测应用。 © 2020 美国医学物理学家协会 [https://doi.org/10.1002/mp.14556]
The availability of radiographic magnetic resonance imaging (MRI) scans for the Ivy Glioblastoma Atlas Project (Ivy GAP) has opened up opportunities for development of radiomic markers for prognostic/predictive applications in glioblastoma (GBM). In this work, we address two critical challenges with regard to developing robust radiomic approaches: (a) the lack of availability of reliable segmentation labels for glioblastoma tumor sub-compartments (i.e., enhancing tumor, non-enhancing tumor core, peritumoral edematous/infiltrated tissue) and (b) identifying “reproducible” radiomic features that are robust to segmentation variability across readers/sites. From TCIA’s Ivy GAP cohort, we obtained a paired set (n = 31) of expert annotations approved by two board-certified neuroradiologists at the Hospital of the University of Pennsylvania (UPenn) and at Case Western Reserve University (CWRU). For these studies, we performed a reproducibility study that assessed the variability in (a) segmentation labels and (b) radiomic features, between these paired annotations. The radiomic variability was assessed on a comprehensive panel of 11 700 radiomic features including intensity, volumetric, morphologic, histogram-based, and textural parameters, extracted for each of the paired sets of annotations. Our results demonstrated (a) a high level of inter-rater agreement (median value of DICE ≥0.8 for all sub-compartments), and (b) ≈24% of the extracted radiomic features being highly correlated (based on Spearman’s rank correlation coefficient) to annotation variations. These robust features largely belonged to morphology (describing shape characteristics), intensity (capturing intensity profile statistics), and COLLAGE (capturing heterogeneity in gradient orientations) feature families. We make publicly available on TCIA’s Analysis Results Directory (https://doi.org/10.7937/9j41-7d44), the complete set of (a) multi-institutional expert annotations for the tumor sub-compartments, (b) 11 700 radiomic features, and (c) the associated reproducibility meta-analysis. The annotations and the associated meta-data for Ivy GAP are released with the purpose of enabling researchers toward developing image-based biomarkers for prognostic/predictive applications in GBM. © 2020 American Association of Physicists in Medicine [https://doi.org/10.1002/mp.14556]
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DOI：
10.1186/s13058-017-0846-1
发表时间：
2017-05-18
期刊：
Breast cancer research : BCR
影响因子：
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作者：
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2016-01-01
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影响因子：
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作者：
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Davatzikos, Christos
影响因子：
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通讯作者：
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影响因子：
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作者：
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通讯作者：
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