TransSurv: Transformer-Based Survival Analysis Model Integrating Histopathological Images and Genomic Data for Colorectal Cancer

doi:10.1109/TCBB.2022.3199244

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	TransSurv: Transformer-Based Survival Analysis Model Integrating Histopathological Images and Genomic Data for Colorectal Cancer
	Lv, Zhilong 1,2; Lin, Yuexiao 3; Yan, Rui 1,2; Wang, Ying 4; Zhang, Fa 1
	2023-11-01
发表期刊	IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
ISSN	1545-5963
卷号	20 期号:6 页码:3411-3420
摘要	Survival analysis is a significant study in cancer prognosis, and the multi-modal data, including histopathological images, genomic data, and clinical information, provides unprecedented opportunities for its development. However, because of the high dimensionality and the heterogeneity of histopathological images and genomic data, acquiring effective predictive characters from these multi-modal data has always been a challenge for survival analysis. In this article, we propose a transformer-based survival analysis model (TransSurv) for colorectal cancer that can effectively integrate intra-modality and inter-modality features of histopathological images, genomic data, and clinical information. Specifically, to integrate the intra-modality relationship of image patches, we develop a multi-scale histopathological features fusion transformer (MS-Trans). Furthermore, we provide a cross-modal fusion transformer based on cross attention for multi-scale pathological representation and multi-omics representation, which includes RNA-seq expression and copy number alteration (CNA). At the output layer of the TransSurv, we adopt the Cox layer to integrate multi-modal fusion representation with clinical information for end-to-end survival analysis. The experimental results on the Cancer Genome Atlas (TCGA) colorectal cancer cohort demonstrate that the proposed TransSurv outperforms the existing methods and improves the prognosis prediction of colorectal cancer.
关键词	Cancer Genomics Bioinformatics Transformers Tumors Feature extraction Prognostics and health management Survival analysis multi-modal learning transformer histopathological slides genomic data
DOI	10.1109/TCBB.2022.3199244
收录类别	SCI
语种	英语
资助项目	Chinese Academy of Sciences
WOS研究方向	Biochemistry & Molecular Biology ; Computer Science ; Mathematics
WOS类目	Biochemical Research Methods ; Computer Science, Interdisciplinary Applications ; Mathematics, Interdisciplinary Applications ; Statistics & Probability
WOS记录号	WOS:001133540000008
出版者	IEEE COMPUTER SOC
引用统计	被引频次：8[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/38867
专题	中国科学院计算技术研究所
通讯作者	Wang, Ying; Zhang, Fa
作者单位	1.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 101408, Peoples R China 3.Capital Med Univ, Beijing Chaoyang Hosp, Dept Gen Surg, Beijing 100020, Peoples R China 4.Capital Med Univ, Beijing Chaoyang Hosp, Dept Pathol, Beijing 100020, Peoples R China
推荐引用方式 GB/T 7714	Lv, Zhilong,Lin, Yuexiao,Yan, Rui,et al. TransSurv: Transformer-Based Survival Analysis Model Integrating Histopathological Images and Genomic Data for Colorectal Cancer[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2023,20(6):3411-3420.
APA	Lv, Zhilong,Lin, Yuexiao,Yan, Rui,Wang, Ying,&Zhang, Fa.(2023).TransSurv: Transformer-Based Survival Analysis Model Integrating Histopathological Images and Genomic Data for Colorectal Cancer.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,20(6),3411-3420.
MLA	Lv, Zhilong,et al."TransSurv: Transformer-Based Survival Analysis Model Integrating Histopathological Images and Genomic Data for Colorectal Cancer".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 20.6(2023):3411-3420.