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Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study
Zhang, Song1,2; Cai, Guoxiang3,4; Xie, Peiyi5; Sun, Caixia1,6,7; Li, Bao1,8; Dai, Weixing3,4; Liu, Xiangyu1,9; Qiu, Qi1,2; Du, Yang1,2; Li, Zhenhui10,12; Liu, Zhenyu1,2,11; Tian, Jie1,6,7,11
Source PublicationRADIOTHERAPY AND ONCOLOGY
ISSN0167-8140
2023-11-01
Volume188Pages:9
Corresponding AuthorLi, Zhenhui(lizhenhui@kmmu.edu.cn) ; Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractPurpose: Adjuvant therapy is recommended to minimize the risk of distant metastasis (DM) and local recurrence (LR) in patients with locally advanced rectal cancer (LARC). However, its role is controversial. We aimed to develop a pretreatment MRI-based deep learning model to predict LR, DM, and overall survival (OS) over 5 years after surgery and to identify patients benefitting from adjuvant chemotherapy (AC).Materials and methods: The multi-survival tasks network (MuST) model was developed in a primary cohort (n = 308) and validated using two external cohorts (n = 247, 245). An AC decision tree integrating the MuST-DM score, perineural invasion (PNI), and preoperative carbohydrate antigen 19-9 (CA19-9) was constructed to assess chemotherapy benefits and aid personalized treatment of patients. We also quantified the prognostic improvement of the decision tree.Results: The MuST network demonstrated high prognostic accuracy in the primary and two external cohorts for the prediction of three different survival tasks. Within the stratified analysis and decision tree, patients with CA19-9 levels > 37 U/mL and high MuST-DM scores exhibited favorable chemotherapy efficacy. Similar results were observed in PNI-positive patients with low MuST-DM scores. PNI-negative patients with low MuST-DM scores exhibited poor chemotherapy efficacy. Based on the decision tree, 14 additional patients benefiting from AC and 391 patients who received overtreatment were identified in this retrospective study.Conclusion: The MuST model accurately and non-invasively predicted OS, DM, and LR. A specific and direct tool linking chemotherapy decisions and benefit quantification has also been provided.(c) 2023 Elsevier B.V. All rights reserved. Radiotherapy and Oncology 188 (2023) 1-9
KeywordLocally advanced rectal cancer Magnetic resonance imaging Prognosis Adjuvant chemotherapy Deep learning
DOI10.1016/j.radonc.2023.109899
WOS KeywordTOTAL MESORECTAL EXCISION ; MEDIAN FOLLOW-UP ; POSTOPERATIVE CHEMORADIOTHERAPY ; PREOPERATIVE RADIOTHERAPY ; PERSONALIZED APPROACH ; SURVIVAL ; RECURRENCE ; COLON ; CHEMORADIATION ; MULTICENTER
Indexed BySCI
Language英语
Funding ProjectNational Key R&D Program of China, China[2021YFF1201003] ; National Natural Science Foundation of China, China[62333022] ; National Natural Science Foundation of China, China[92059103] ; National Natural Science Foundation of China, China[92259301] ; National Natural Science Foundation of China, China[92159301] ; National Natural Science Foundation of China, China[82001986] ; National Natural Science Foundation of China, China[82360345] ; Beijing Natural Science Foundation, China[JQ23034] ; Yunnan Basic Research Project, China[202201AT070010]
Funding OrganizationNational Key R&D Program of China, China ; National Natural Science Foundation of China, China ; Beijing Natural Science Foundation, China ; Yunnan Basic Research Project, China
WOS Research AreaOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:001081328900001
PublisherELSEVIER IRELAND LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/52988
Collection中国科学院分子影像重点实验室
Corresponding AuthorLi, Zhenhui; Liu, Zhenyu; Tian, Jie
Affiliation1.Chinese Acad Sci, Inst Automation, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
3.Fudan Univ, Dept Colorectal Surg, Shanghai Canc Ctr, Shanghai, Peoples R China
4.Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China
5.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Radiol, Guangzhou, Guangdong, Peoples R China
6.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
7.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China
8.Univ Sci & Technol China, Ctr Biomed Imaging, Hefei, Anhui, Peoples R China
9.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China
10.Kunming Med Univ, Yunnan Canc Hosp, Yunnan Canc Ctr, Dept Radiol,Affiliated Hosp 3, Kunming, Yunnan, Peoples R China
11.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
12.Yunnan Canc Hosp, 519 Kunzhou Rd, Kunming 650118, Yunnan, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Zhang, Song,Cai, Guoxiang,Xie, Peiyi,et al. Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study[J]. RADIOTHERAPY AND ONCOLOGY,2023,188:9.
APA Zhang, Song.,Cai, Guoxiang.,Xie, Peiyi.,Sun, Caixia.,Li, Bao.,...&Tian, Jie.(2023).Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study.RADIOTHERAPY AND ONCOLOGY,188,9.
MLA Zhang, Song,et al."Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study".RADIOTHERAPY AND ONCOLOGY 188(2023):9.
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