Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
Association of genomic subtypes of lower-grade gliomas with shape features automatically extracted by a deep learning algorithm
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DOI：
10.1016/j.compbiomed.2019.05.002
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发表时间：
2019-06-01
影响因子：
7.7
通讯作者：
Mazurowski, Maciej A.
中科院分区：
文献类型：
--
作者：
Buda, Mateusz;Saha, Ashirbani;Mazurowski, Maciej A.
最近的分析确定了与形状特征相关的低级别胶质瘤肿瘤的不同基因组亚型。在这项研究中，我们提出了一种全自动的方法，使用基于深度学习的分割来量化肿瘤成像特征，并测试这些特征是否可以预测肿瘤基因组亚型。我们使用了来自癌症基因组图谱中5家机构的110名低级别胶质瘤患者的术前成像和基因组数据。基于自动深度学习分割，我们提取了三个特征，这些特征量化了肿瘤的二维和三维特征。分析的患者队列的基因组数据由先前基于IDH突变和1 p/19 q共缺失、DNA甲基化、基因表达、DNA拷贝数和microRNA表达鉴定的基因组簇组成。为了分析成像特征和基因组聚类之间的关系，我们对每对成像特征和基因组亚型的10个假设进行了Fisher精确检验。为了解释多重假设检验，我们应用了Bonferroni校正。P值低于0.005被认为是统计学显著的，我们发现RNASeq簇和边界椭圆体体积比之间的关联最强（p < 0.0002），RNASeq簇和边缘波动之间的关联最强（p < 0.005）。此外，我们确定了边界椭圆体体积比与所有测试的分子亚型之间的关联（p < 0.02）以及角度标准偏差与RNASeq簇之间的关联（p < 0.02）。在用于生成定量图像特征的自动肿瘤分割方面，我们的深度学习算法实现了82%的平均Dice系数，与人类性能相当。
Recent analysis identified distinct genomic subtypes of lower-grade glioma tumors which are associated with shape features. In this study, we propose a fully automatic way to quantify tumor imaging characteristics using deep learning-based segmentation and test whether these characteristics are predictive of tumor genomic subtypes.We used preoperative imaging and genomic data of 110 patients from 5 institutions with lower-grade gliomas from The Cancer Genome Atlas. Based on automatic deep learning segmentations, we extracted three features which quantify two-dimensional and three-dimensional characteristics of the tumors. Genomic data for the analyzed cohort of patients consisted of previously identified genomic clusters based on IDH mutation and 1p/19q co-deletion, DNA methylation, gene expression, DNA copy number, and microRNA expression. To analyze the relationship between the imaging features and genomic clusters, we conducted the Fisher exact test for 10 hypotheses for each pair of imaging feature and genomic subtype. To account for multiple hypothesis testing, we applied a Bonferroni correction. P-values lower than 0.005 were considered statistically significant.We found the strongest association between RNASeq clusters and the bounding ellipsoid volume ratio (p < 0.0002) and between RNASeq clusters and margin fluctuation (p < 0.005). In addition, we identified associations between bounding ellipsoid volume ratio and all tested molecular subtypes (p < 0.02) as well as between angular standard deviation and RNASeq cluster (p < 0.02). In terms of automatic tumor segmentation that was used to generate the quantitative image characteristics, our deep learning algorithm achieved a mean Dice coefficient of 82% which is comparable to human performance.