Deep learning with whole slide images can improve the prognostic risk stratification with III colorectal cancer

doi:10.1016/j.cmpb.2022.106914

	Deep learning with whole slide images can improve the prognostic risk stratification with III colorectal cancer
	Sun, Caixia 1; Li, Bingbing 3,4,5; Wei, Genxia 2,3,4,5; Qiu, Weihao 3,4,5; Li, Danyi 3,4,5; Li, Xiangzhao 3,4,5; Liu, Xiangyu 2; Wei, Wei2 ; Wang, Shuo1,2 ; Liu, Zhenyu2,6,7 ; Tian, Jie 1,2; Liang, Li 3,4,5
发表期刊	COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN	0169-2607
	2022-06-01
卷号	221 页码:11
通讯作者	Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Liang, Li(lli@smu.edu.cn)
摘要	Background and Objective: Adjuvant chemotherapy is recommended as standard treatment for colorectal cancer (CRC) with stage III according to TNM stage. However, outcomes are varied even among patients receiving similar treatments. We aimed to develop a prognostic signature to stratify outcomes and benefit from different chemotherapy regimens by analyzing whole slide images (WSI) using deep learning.Methods: We proposed an unsupervised deep learning network (variational autoencoder and generative adversarial network) in 180,819 image tiles from the training set (147 patients) to develop a WSI signature for predicting the disease-free survival (DFS) and overall survival (OS) of patients, and tested in validation set of 63 patients. An integrated nomogram was constructed to investigate the incremental value of deep learning signature (DLS) to TNM stage for individualized outcomes prediction.Results: The DLS was associated with DFS and OS in both training and validation sets and proved to be an independent prognostic factor. Integrating the DLS and clinicopathologic factors showed better perfor-mance (C-index: DFS, 0.748; OS, 0.794; in the validation set) than TNM stage. In patients whose DLS and clinical risk levels were inconsistent, their risk of relapse was reclassified. In the subgroup of patients treated with 3 months, high-DL S was associated with worse DFS (hazard ratio: 3.622-7.728).Conclusions: The proposed based-WSI DLS improved risk stratification and could help identify patients with stage III CRC who may benefit from the prolonged duration of chemotherapy.(c) 2022 Published by Elsevier B.V.
关键词	Chemotherapy duration Whole slide images Deep learning Colorectal cancer Prognosis
DOI	10.1016/j.cmpb.2022.106914
关键词[WOS]	COLON-CANCER ; ADJUVANT CHEMOTHERAPY ; STAGE-II ; MICROSATELLITE INSTABILITY ; SURVIVAL ; RECURRENCE ; PREDICTION ; DURATION ; DISEASES
收录类别	SCI
语种	英语
资助项目	National key R&D program of China[2021YFF1201004] ; National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[92059103] ; National Natural Science Foundation of China[81872041] ; Youth Innovation Promotion Association CAS[2019136] ; Guangzhou R & D plan in key areas[2020 07040 0 01]
项目资助者	National key R&D program of China ; National Natural Science Foundation of China ; Youth Innovation Promotion Association CAS ; Guangzhou R & D plan in key areas
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS记录号	WOS:000807580800004
出版者	ELSEVIER IRELAND LTD
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49594
专题	中国科学院分子影像重点实验室
通讯作者	Liu, Zhenyu; Tian, Jie; Liang, Li
作者单位	1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch & Engn Med, Beijing 100191, Peoples R China 2.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Manageme, Beijing 100190, Peoples R China 3.Southern Med Univ, Nanfang Hosp, Dept Pathol, Guangzhou 510515, Guangdong, Peoples R China 4.Southern Med Univ, Basic Med Coll, Guangzhou 510515, Guangdong, Peoples R China 5.Guangdong Prov Key Lab Mol Tumor Pathol, Guangzhou 510515, Guangdong, Peoples R China 6.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techno, Beijing 100190, Peoples R China 7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Sun, Caixia,Li, Bingbing,Wei, Genxia,et al. Deep learning with whole slide images can improve the prognostic risk stratification with III colorectal cancer[J]. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,2022,221:11.
APA	Sun, Caixia.,Li, Bingbing.,Wei, Genxia.,Qiu, Weihao.,Li, Danyi.,...&Liang, Li.(2022).Deep learning with whole slide images can improve the prognostic risk stratification with III colorectal cancer.COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE,221,11.
MLA	Sun, Caixia,et al."Deep learning with whole slide images can improve the prognostic risk stratification with III colorectal cancer".COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 221(2022):11.