Identification of Histological Correlates of Overall Survival in Lower Grade Gliomas Using a Bag-of-words Paradigm: A Preliminary Analysis Based on Hematoxylin & Eosin Stained Slides from the Lower Grade Glioma Cohort of The Cancer Genome Atlas.
Identification of Histological Correlates of Overall Survival in Lower Grade Gliomas Using a Bag-of-words Paradigm: A Preliminary Analysis Based on Hematoxylin & Eosin Stained Slides from the Lower Grade Glioma Cohort of The Cancer Genome Atlas.
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DOI：
10.4103/jpi.jpi_43_16
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发表时间：
2017
影响因子：
--
通讯作者：
Rao A
中科院分区：
文献类型：
--
作者：
Powell RT;Olar A;Narang S;Rao G;Sulman E;Fuller GN;Rao A
胶质瘤是最常见的原发性脑肿瘤，是一种具有多种组织学亚型和细胞起源的异质性肿瘤。在临床表现中，根据世界卫生组织（WHO）指南对神经胶质瘤进行分级，该指南反映了基于组织病理学和分子特征的肿瘤恶性特征。低级别弥漫性胶质瘤（LGG）（WHO II-III级）的恶性特征比高级别胶质瘤（WHO IV级）少，临床预后更好，但是，准确区分总生存期（OS）仍然是一个挑战。在这项研究中，我们的目的是使用机器学习方法来识别组织来源的图像特征，以预测低级别胶质瘤患者的混合组织学和级别队列中的OS。为了实现这一目标，我们使用来自癌症基因组图谱（TCGA）的公共LGG队列的H和E染色切片来创建与OS相关的“图像衍生视觉词”的机器学习词典。然后，我们通过训练广义机器学习模型来评估使用这些视觉词预测短期与长期OS的综合功效。最后，我们将这些预测性视觉词映射回分子信号级联，以推断机器学习生存相关表型的潜在驱动因素。我们使用词袋法分析了从TCGA的LGG队列下载的数字化组织学切片。该方法分别使用考克斯回归、方差分析和斯皮尔曼相关性鉴定了与OS、组织学和分子信号传导活性进一步相关的一组不同的组织学模式。构建支持向量机（SVM）模型，将患者分为24个月时二分的短OS组和长OS组。该方法识别了与OS相关的疾病相关表型，其中一些与疾病相关的分子途径相关。从这些图像衍生的表型中，获得了可以区分24个月OS（曲线下面积，0.76）的广义SVM模型。在这里，我们展示了一种潜在的策略，将来自H和E染色的载玻片的图像特征纳入OS的预测模型。此外，我们还展示了这些图像衍生的表型特征如何与LGG的病因或行为相关的分子信号传导活性相关。
Glioma, the most common primary brain neoplasm, describes a heterogeneous tumor of multiple histologic subtypes and cellular origins. At clinical presentation, gliomas are graded according to the World Health Organization guidelines (WHO), which reflect the malignant characteristics of the tumor based on histopathological and molecular features. Lower grade diffuse gliomas (LGGs) (WHO Grade II–III) have fewer malignant characteristics than high-grade gliomas (WHO Grade IV), and a better clinical prognosis, however, accurate discrimination of overall survival (OS) remains a challenge. In this study, we aimed to identify tissue-derived image features using a machine learning approach to predict OS in a mixed histology and grade cohort of lower grade glioma patients. To achieve this aim, we used H and E stained slides from the public LGG cohort of The Cancer Genome Atlas (TCGA) to create a machine learned dictionary of “image-derived visual words” associated with OS. We then evaluated the combined efficacy of using these visual words in predicting short versus long OS by training a generalized machine learning model. Finally, we mapped these predictive visual words back to molecular signaling cascades to infer potential drivers of the machine learned survival-associated phenotypes. We analyzed digitized histological sections downloaded from the LGG cohort of TCGA using a bag-of-words approach. This method identified a diverse set of histological patterns that were further correlated with OS, histology, and molecular signaling activity using Cox regression, analysis of variance, and Spearman correlation, respectively. A support vector machine (SVM) model was constructed to discriminate patients into short and long OS groups dichotomized at 24-month. This method identified disease-relevant phenotypes associated with OS, some of which are correlated with disease-associated molecular pathways. From these image-derived phenotypes, a generalized SVM model which could discriminate 24-month OS (area under the curve, 0.76) was obtained. Here, we demonstrated one potential strategy to incorporate image features derived from H and E stained slides into predictive models of OS. In addition, we showed how these image-derived phenotypic characteristics correlate with molecular signaling activity underlying the etiology or behavior of LGG.