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Deep learning with whole slide images can improve the prognostic risk stratification with III colorectal cancer
Sun, Caixia1; Li, Bingbing3,4,5; Wei, Genxia2,3,4,5; Qiu, Weihao3,4,5; Li, Danyi3,4,5; Li, Xiangzhao3,4,5; Liu, Xiangyu2; Wei, Wei2; Wang, Shuo1,2; Liu, Zhenyu2,6,7; Tian, Jie1,2; Liang, Li3,4,5
Source PublicationCOMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
ISSN0169-2607
2022-06-01
Volume221Pages:11
Corresponding AuthorLiu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Liang, Li(lli@smu.edu.cn)
AbstractBackground 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.
KeywordChemotherapy duration Whole slide images Deep learning Colorectal cancer Prognosis
DOI10.1016/j.cmpb.2022.106914
WOS KeywordCOLON-CANCER ; ADJUVANT CHEMOTHERAPY ; STAGE-II ; MICROSATELLITE INSTABILITY ; SURVIVAL ; RECURRENCE ; PREDICTION ; DURATION ; DISEASES
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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 Research AreaComputer Science ; Engineering ; Medical Informatics
WOS SubjectComputer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS IDWOS:000807580800004
PublisherELSEVIER IRELAND LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49594
Collection中国科学院分子影像重点实验室
Corresponding AuthorLiu, Zhenyu; Tian, Jie; Liang, Li
Affiliation1.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
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
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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