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Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer

文献类型：期刊论文

作者Liu, Zhenyu1,7,9; Meng, Xiaochun6; Zhang, Hongmei5; Li, Zhenhui4; Liu, Jiangang10; Sun, Kai9,11; Meng, Yankai5; Dai, Weixing8; Xie, Peiyi6; Ding, Yingying4
刊名NATURE COMMUNICATIONS
出版日期2020-08-27
卷号11期号:1页码:11
ISSN号2041-1723
DOI10.1038/s41467-020-18162-9
通讯作者Wang, Meiyun(mywang@ha.edu.cn) ; Cai, Guoxiang(gxcai@fudan.edu.cn) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Adjuvant chemotherapy is usually used for distant control. However, not all patients can benefit from adjuvant chemotherapy, and particularly, some patients may even get worse outcomes after the treatment. We develop and validate an MRI-based radiomic signature (RS) for prediction of DM within a multicenter dataset. The RS is proved to be an independent prognostic factor as it not only demonstrates good accuracy for discriminating patients into high and low risk of DM in all the four cohorts, but also outperforms clinical models. Within the stratified analysis, good chemotherapy efficacy is observed for patients with pN2 disease and low RS, whereas poor chemotherapy efficacy is detected in patients with pT1-2 or pN0 disease and high RS. The RS may help individualized treatment planning to select patients who may benefit from adjuvant chemotherapy for distant control. Distant metastasis (DM) is the main cause of treatment failure in locally advanced rectal cancer. Here, the authors developed and validated a radiomic signature (RS) for prediction of DM within a multicenter dataset, and suggest that it may help with stratification of patients who might benefit from adjuvant chemotherapy for DM.
WOS关键词PREOPERATIVE CHEMORADIOTHERAPY ; NEOADJUVANT CHEMORADIATION ; RADIOMIC SIGNATURE ; FOLLOW-UP ; SURVIVAL ; RADIOTHERAPY ; MULTICENTER ; BIOMARKER ; BRIDGE ; PET/CT
资助项目National Natural Science Foundation of China[81922040] ; National Natural Science Foundation of China[81930053] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81971589] ; National Natural Science Foundation of China[81720108021] ; National Natural Science Foundation of China[81527805] ; Beijing Natural Science Foundation[7182109] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2017YFE0103600] ; Youth Innovation Promotion Association CAS[2019136] ; Zhongyuan Thousand Talents Plan Project-Basic Research Leader Talent[ZYQR201810117]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000567932900007
出版者NATURE PUBLISHING GROUP
资助机构National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; Youth Innovation Promotion Association CAS ; Zhongyuan Thousand Talents Plan Project-Basic Research Leader Talent
源URL[http://ir.ia.ac.cn/handle/173211/41949]  
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Wang, Meiyun; Cai, Guoxiang; Tian, Jie
作者单位1.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing 100190, Peoples R China
2.Zhengzhou Univ, Henan Prov Peoples Hosp, Dept Radiol, Zhengzhou 450003, Peoples R China
3.Zhengzhou Univ, Peoples Hosp, Zhengzhou 450003, Peoples R China
4.Kunming Med Univ, Dept Radiol, Affiliated Hosp 3, Yunnan Canc Hosp, Kunming 650031, Yunnan, Peoples R China
5.Chinese Acad Med Sci & Peking Union Med Coll, Natl Clin Res Ctr Canc, Dept Diagnost Radiol, Natl Canc Ctr,Canc Hosp, Beijing 100021, Peoples R China
6.Sun Yat Sen Univ, Affiliated Hosp 6, Dept Radiol, Guangzhou 510655, Peoples R China
7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100080, Peoples R China
8.Fudan Univ, Dept Colorectal Surg, Shanghai Canc Ctr, Shanghai 200032, Peoples R China
9.Chinese Acad Sci, State Key Lab Management & Control Complex Syst, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,Inst Automat, Beijing 100190, Peoples R China
10.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714		 Liu, Zhenyu,Meng, Xiaochun,Zhang, Hongmei,et al. Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer[J]. NATURE COMMUNICATIONS,2020,11(1):11.
APA Liu, Zhenyu.,Meng, Xiaochun.,Zhang, Hongmei.,Li, Zhenhui.,Liu, Jiangang.,...&Tian, Jie.(2020).Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer.NATURE COMMUNICATIONS,11(1),11.
MLA Liu, Zhenyu,et al."Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer".NATURE COMMUNICATIONS 11.1(2020):11.

入库方式： OAI收割

来源：自动化研究所

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