Thomas Roetzer-Pejrimovsky, Karl-Heinz Nenning, Barbara Kiesel, Johanna Klughammer, Martin Rajchl, Bernhard Baumann, Georg Langs, Adelheid Woehrer
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At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome.</p><p><strong>Results: </strong>We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). 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引用次数: 0
摘要
背景:深度学习彻底改变了癌症病理学中的医学图像分析,通过支持癌症的诊断和预后评级,对临床产生了重大影响。在脑癌领域,胶质母细胞瘤是首批可用的数字资源之一,它是最常见也是最致命的脑癌。在组织学层面,胶质母细胞瘤的特点是表型变化多端,与患者的预后关系不大。在转录水平上,有3种分子亚型,间质亚型肿瘤与免疫细胞浸润增加和预后较差有关:结果:我们将 Xception 卷积神经网络应用于包含分子亚型注释的 276 张数字化血液染色和伊红(H&E)切片的发现集以及基于癌症基因组图谱的 178 例独立验证队列,从而解决了基因型与表型之间的相关性问题。利用这种方法,我们在基于 H&E 的分子亚型图谱绘制方面取得了很高的准确度(经典、间充质和绒毛膜的曲线下面积分别为 0.84、0.81 和 0.71;P < 0.001),并绘制出了与较差预后相关的区域(单变量生存模型 P < 0.001,多变量 P = 0.01)。后者的特点是肿瘤细胞密度较高(P < 0.001)、肿瘤细胞表型可变(P < 0.001)和 T 细胞浸润减少(P = 0.017):我们针对胶质母细胞瘤数字切片修改了著名的卷积神经网络架构,以准确绘制转录亚型的空间分布图和预示较差预后的区域,从而展示了人工智能图像挖掘在脑癌中的相关性。
Deep learning links localized digital pathology phenotypes with transcriptional subtype and patient outcome in glioblastoma.
Background: Deep learning has revolutionized medical image analysis in cancer pathology, where it had a substantial clinical impact by supporting the diagnosis and prognostic rating of cancer. Among the first available digital resources in the field of brain cancer is glioblastoma, the most common and fatal brain cancer. At the histologic level, glioblastoma is characterized by abundant phenotypic variability that is poorly linked with patient prognosis. At the transcriptional level, 3 molecular subtypes are distinguished with mesenchymal-subtype tumors being associated with increased immune cell infiltration and worse outcome.
Results: We address genotype-phenotype correlations by applying an Xception convolutional neural network to a discovery set of 276 digital hematozylin and eosin (H&E) slides with molecular subtype annotation and an independent The Cancer Genome Atlas-based validation cohort of 178 cases. Using this approach, we achieve high accuracy in H&E-based mapping of molecular subtypes (area under the curve for classical, mesenchymal, and proneural = 0.84, 0.81, and 0.71, respectively; P < 0.001) and regions associated with worse outcome (univariable survival model P < 0.001, multivariable P = 0.01). The latter were characterized by higher tumor cell density (P < 0.001), phenotypic variability of tumor cells (P < 0.001), and decreased T-cell infiltration (P = 0.017).
Conclusions: We modify a well-known convolutional neural network architecture for glioblastoma digital slides to accurately map the spatial distribution of transcriptional subtypes and regions predictive of worse outcome, thereby showcasing the relevance of artificial intelligence-enabled image mining in brain cancer.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.