PDF(Pubmed)
Abstract：
UNASSIGNED: To automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC).
UNASSIGNED: A deep learning model based on VGG19 architecture and transfer learning strategy was trained to recognize nine different tissue types in whole slide images of patients with CRC. Seven of the nine tissue types were defined as TME components besides background and debris. Then 13 TME features were calculated based on the areas of TME components. A total of 562 patients with gene expression data, survival information and WSIs were collected from The Cancer Genome Atlas project for further analysis. A TME signature for prognostic prediction was developed and validated using Cox regression method. A prognostic prediction model combined the TME signature and clinical variables was also established. At last, gene-set enrichment analysis was performed to identify the significant TME signature associated pathways by querying Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database.
UNASSIGNED: The deep learning model achieved an accuracy of 94.2% for tissue type recognition. The developed TME signature was found significantly associated to progression-free survival. The clinical combined model achieved a concordance index of 0.714. Gene-set enrichment analysis revealed the TME signature associated genes were enriched in neuroactive ligand-receptor interaction pathway.
UNASSIGNED: The TME signature was proved to be a prognostic factor and the associated biologic pathways would be beneficial to a better understanding of TME in CRC patients.
UNASSIGNED: A deep learning model based on VGG19 architecture and transfer learning strategy was trained to recognize nine different tissue types in whole slide images of patients with CRC. Seven of the nine tissue types were defined as TME components besides background and debris. Then 13 TME features were calculated based on the areas of TME components. A total of 562 patients with gene expression data, survival information and WSIs were collected from The Cancer Genome Atlas project for further analysis. A TME signature for prognostic prediction was developed and validated using Cox regression method. A prognostic prediction model combined the TME signature and clinical variables was also established. At last, gene-set enrichment analysis was performed to identify the significant TME signature associated pathways by querying Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database.
UNASSIGNED: The deep learning model achieved an accuracy of 94.2% for tissue type recognition. The developed TME signature was found significantly associated to progression-free survival. The clinical combined model achieved a concordance index of 0.714. Gene-set enrichment analysis revealed the TME signature associated genes were enriched in neuroactive ligand-receptor interaction pathway.
UNASSIGNED: The TME signature was proved to be a prognostic factor and the associated biologic pathways would be beneficial to a better understanding of TME in CRC patients.
摘要：
■为了自动量化苏木精和曙红染色的整个载玻片图像（WSI）中的结直肠肿瘤微环境（TME），并开发用于结直肠癌（CRC）预后预测的TME特征。
■对基于VGG19架构和迁移学习策略的深度学习模型进行了训练，以识别CRC患者的整个幻灯片图像中的9种不同组织类型。除背景和碎片外，9种组织类型中的7种被定义为TME成分。然后基于TME组件的面积计算13个TME特征。共有562例患者的基因表达数据，我们从癌症基因组图谱项目中收集生存信息和WSI进行进一步分析.使用Cox回归方法开发并验证了用于预后预测的TME特征。还建立了结合TME特征和临床变量的预后预测模型。最后,通过查询基因本体论数据库和京都基因和基因组百科全书数据库，进行基因集富集分析以鉴定重要的TME特征相关途径。
■深度学习模型对组织类型识别的准确率为94.2%。发现开发的TME特征与无进展生存期显着相关。临床联合模型的一致性指数为0.714。基因集富集分析显示TME签名相关基因富集在神经活性配体-受体相互作用途径中。
■TME特征被证明是一个预后因素，相关的生物学通路将有助于更好地了解CRC患者的TME。
■对基于VGG19架构和迁移学习策略的深度学习模型进行了训练，以识别CRC患者的整个幻灯片图像中的9种不同组织类型。除背景和碎片外，9种组织类型中的7种被定义为TME成分。然后基于TME组件的面积计算13个TME特征。共有562例患者的基因表达数据，我们从癌症基因组图谱项目中收集生存信息和WSI进行进一步分析.使用Cox回归方法开发并验证了用于预后预测的TME特征。还建立了结合TME特征和临床变量的预后预测模型。最后,通过查询基因本体论数据库和京都基因和基因组百科全书数据库，进行基因集富集分析以鉴定重要的TME特征相关途径。
■深度学习模型对组织类型识别的准确率为94.2%。发现开发的TME特征与无进展生存期显着相关。临床联合模型的一致性指数为0.714。基因集富集分析显示TME签名相关基因富集在神经活性配体-受体相互作用途径中。
■TME特征被证明是一个预后因素，相关的生物学通路将有助于更好地了解CRC患者的TME。
