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

文献类型：期刊论文

作者Lv, Zhilong3,4; Lin, Yuexiao2; Yan, Rui3,4; Wang, Ying1; Zhang, Fa4
刊名IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
出版日期2023-11-01
卷号20期号:6页码:3411-3420
关键词Cancer Genomics Bioinformatics Transformers Tumors Feature extraction Prognostics and health management Survival analysis multi-modal learning transformer histopathological slides genomic data
ISSN号1545-5963
DOI10.1109/TCBB.2022.3199244
英文摘要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.
资助项目Chinese Academy of Sciences
WOS研究方向Biochemistry & Molecular Biology ; Computer Science ; Mathematics
语种英语
WOS记录号WOS:001133540000008
出版者IEEE COMPUTER SOC
源URL[http://119.78.100.204/handle/2XEOYT63/38866]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Wang, Ying; Zhang, Fa
作者单位1.Capital Med Univ, Beijing Chaoyang Hosp, Dept Pathol, Beijing 100020, Peoples R China
2.Capital Med Univ, Beijing Chaoyang Hosp, Dept Gen Surg, Beijing 100020, Peoples R China
3.Univ Chinese Acad Sci, Beijing 101408, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, High Performance Comp Res Ctr, Beijing 100190, 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.

入库方式： OAI收割

来源：计算技术研究所

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