Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study

doi:10.1016/j.radonc.2023.109899

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	Improving prognosis and assessing adjuvant chemotherapy benefit in locally advanced rectal cancer with deep learning for MRI: A retrospective, multi-cohort study
	Zhang, Song 11,12; Cai, Guoxiang 1,10; Xie, Peiyi 9; Sun, Caixia 7,8,12; Li, Bao 6,12; Dai, Weixing 1,10; Liu, Xiangyu 5,12; Qiu, Qi 11,12; Du, Yang 11,12; Li, Zhenhui 2,4
刊名	RADIOTHERAPY AND ONCOLOGY
	2023-11-01
卷号	188 页码:9
关键词	Locally advanced rectal cancer Magnetic resonance imaging Prognosis Adjuvant chemotherapy Deep learning
ISSN号	0167-8140
DOI	10.1016/j.radonc.2023.109899
通讯作者	Li, Zhenhui(lizhenhui@kmmu.edu.cn) ; Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Purpose: 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
资助项目	National 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]
WOS关键词	TOTAL MESORECTAL EXCISION ; MEDIAN FOLLOW-UP ; POSTOPERATIVE CHEMORADIOTHERAPY ; PREOPERATIVE RADIOTHERAPY ; PERSONALIZED APPROACH ; SURVIVAL ; RECURRENCE ; COLON ; CHEMORADIATION ; MULTICENTER
WOS研究方向	Oncology ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	ELSEVIER IRELAND LTD
WOS记录号	WOS:001081328900001
资助机构	National Key R&D Program of China, China ; National Natural Science Foundation of China, China ; Beijing Natural Science Foundation, China ; Yunnan Basic Research Project, China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/52987]
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	Li, Zhenhui; Liu, Zhenyu; Tian, Jie
作者单位	1.Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China 2.Yunnan Canc Hosp, 519 Kunzhou Rd, Kunming 650118, Yunnan, Peoples R China 3.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China 4.Kunming Med Univ, Yunnan Canc Hosp, Yunnan Canc Ctr, Dept Radiol,Affiliated Hosp 3, Kunming, Yunnan, Peoples R China 5.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China 6.Univ Sci & Technol China, Ctr Biomed Imaging, Hefei, Anhui, Peoples R China 7.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, Peoples R China 8.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China 9.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Radiol, Guangzhou, Guangdong, Peoples R China 10.Fudan Univ, Dept Colorectal Surg, Shanghai Canc Ctr, Shanghai, Peoples R China
推荐引用方式 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.