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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

文献类型：期刊论文

作者Zhang, Song4,5; Cai, Guoxiang6,7; Xie, Peiyi8; Sun, Caixia4,9,10; Li, Bao4,11; Dai, Weixing6,7; Liu, Xiangyu4,12; Qiu, Qi4,5; Du, Yang4,5; Li, Zhenhui2,3
刊名RADIOTHERAPY AND ONCOLOGY
出版日期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
DOI10.1016/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
WOS关键词TOTAL MESORECTAL EXCISION ; MEDIAN FOLLOW-UP ; POSTOPERATIVE CHEMORADIOTHERAPY ; PREOPERATIVE RADIOTHERAPY ; PERSONALIZED APPROACH ; SURVIVAL ; RECURRENCE ; COLON ; CHEMORADIATION ; MULTICENTER
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研究方向Oncology ; Radiology, Nuclear Medicine & Medical Imaging
语种英语
WOS记录号WOS:001081328900001
出版者ELSEVIER IRELAND LTD
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
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.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Kunming Med Univ, Yunnan Canc Hosp, Yunnan Canc Ctr, Dept Radiol,Affiliated Hosp 3, Kunming, Yunnan, Peoples R China
3.Yunnan Canc Hosp, 519 Kunzhou Rd, Kunming 650118, Yunnan, Peoples R China
4.Chinese Acad Sci, Inst Automation, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging, Beijing, Peoples R China
5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China
6.Fudan Univ, Dept Colorectal Surg, Shanghai Canc Ctr, Shanghai, Peoples R China
7.Fudan Univ, Shanghai Med Coll, Dept Oncol, Shanghai, Peoples R China
8.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Radiol, Guangzhou, Guangdong, Peoples R China
9.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Engn Med, Beijing, Peoples R China
10.Beihang Univ, Key Lab Big Data Based Precis Med, Minist Ind & Informat Technol, Beijing, 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.

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