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

文献类型：期刊论文

作者Sun, Caixia7; Li, Bingbing3,4,5; Wei, Genxia3,4,5,6; Qiu, Weihao3,4,5; Li, Danyi3,4,5; Li, Xiangzhao3,4,5; Liu, Xiangyu6; Wei, Wei6; Wang, Shuo6,7; Liu, Zhenyu1,2,6
刊名COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
出版日期2022-06-01
卷号221页码:11
ISSN号0169-2607
关键词Chemotherapy duration Whole slide images Deep learning Colorectal cancer Prognosis
DOI10.1016/j.cmpb.2022.106914
通讯作者Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Liang, Li(lli@smu.edu.cn)
英文摘要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.
WOS关键词COLON-CANCER ; ADJUVANT CHEMOTHERAPY ; STAGE-II ; MICROSATELLITE INSTABILITY ; SURVIVAL ; RECURRENCE ; PREDICTION ; DURATION ; DISEASES
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]
WOS研究方向Computer Science ; Engineering ; Medical Informatics
语种英语
出版者ELSEVIER IRELAND LTD
WOS记录号WOS:000807580800004
资助机构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
源URL[http://ir.ia.ac.cn/handle/173211/49593]  
专题自动化研究所_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.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techno, Beijing 100190, Peoples R China
3.Guangdong Prov Key Lab Mol Tumor Pathol, Guangzhou 510515, Guangdong, Peoples R China
4.Southern Med Univ, Basic Med Coll, Guangzhou 510515, Guangdong, Peoples R China
5.Southern Med Univ, Nanfang Hosp, Dept Pathol, Guangzhou 510515, Guangdong, Peoples R China
6.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Manageme, Beijing 100190, Peoples R China
7.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch & Engn Med, Beijing 100191, Peoples R China
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.

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来源：自动化研究所

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