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

	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, Guoxiang 3,4; Xie, Peiyi 5; Sun, Caixia 1,6,7; Li, Bao 1,8; Dai, Weixing 3,4; Liu, Xiangyu 1,9; Qiu, Qi1,2 ; Du, Yang1,2 ; Li, Zhenhui 10,12; Liu, Zhenyu1,2,11 ; Tian, Jie 1,6,7,11
发表期刊	RADIOTHERAPY AND ONCOLOGY
ISSN	0167-8140
	2023-11-01
卷号	188 页码:9
通讯作者	Li, Zhenhui(lizhenhui@kmmu.edu.cn) ; Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
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
关键词	Locally advanced rectal cancer Magnetic resonance imaging Prognosis Adjuvant chemotherapy Deep learning
DOI	10.1016/j.radonc.2023.109899
关键词[WOS]	TOTAL MESORECTAL EXCISION ; MEDIAN FOLLOW-UP ; POSTOPERATIVE CHEMORADIOTHERAPY ; PREOPERATIVE RADIOTHERAPY ; PERSONALIZED APPROACH ; SURVIVAL ; RECURRENCE ; COLON ; CHEMORADIATION ; MULTICENTER
收录类别	SCI
语种	英语
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]
项目资助者	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
WOS研究方向	Oncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Oncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001081328900001
出版者	ELSEVIER IRELAND LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/52988
专题	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.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
第一作者单位	中国科学院自动化研究所
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	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.