Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer

doi:10.1016/j.radonc.2018.11.007

	Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer
	Tang, Zhenchao 1; Zhang, Xiao-Yan 3; Liu, Zhenyu 2,5; Li, Xiao-Ting 3; Shi, Yan-Jie 3; Wang, Shou 2; Fang, Mengjie 2; Shen, Chen 2; Dong, Enqing 1; Sun, Ying-Shi 3
刊名	RADIOTHERAPY AND ONCOLOGY
	2019-03-01
卷号	132 页码:100-108
关键词	Locally advanced rectal cancer Neoadjuvant chemoradiotherapy Organ-preserving strategies Diffusion weighted imaging Decision support
ISSN号	0167-8140
DOI	10.1016/j.radonc.2018.11.007
通讯作者	Dong, Enqing(enqdong@sdu.edu.cn) ; Sun, Ying-Shi(sys27@163.com) ; Tian, Jie(jie.tian@ia.ac.cn)
英文摘要	Background and purpose: Locally advanced rectal cancer (LARC) patients showing pathological good response (pGR) of down-staging to ypT0-1N0 after neoadjuvant chemoradiotherapy (nCRT) may receive organ-preserving treatment instead of total mesorectal excision (TME). In the current study, quantitative analysis of diffusion weighted imaging (DWI) is conducted to predict pGR patients in order to provide decision support for organ-preserving strategies. Materials and methods: 222 LARC patients receiving nCRT and TME are enrolled from Beijing Cancer Hospital and allocated into training (152) and validation (70) set. Three pGR prediction models are constructed in the training set, including DWI prediction model based on quantitative DWI features, clinical prediction model based on clinical characteristics, and combined prediction model integrating DWI and clinical predictors. Prediction performances are assessed by area under receiver operating characteristic curve (AUC), classification accuracy (ACC), positive and negative predictive values (PPV and NPV). Results: The DWI (AUC = 0.866, ACC = 91.43%) and combined (AUC = 0.890, ACC = 90%) prediction model obtains good prediction performance in the independent validation set. Nevertheless, the clinical prediction model performs worse than the other two models (AUC = 0.631, ACC = 75.71% in validation set). Calibration analysis indicates that the pGR probability predicted by the combined prediction model is close to perfect prediction. Decision curve analysis reveals that the LARC patients will acquire clinical benefit if receiving organ-preserving strategy according to combined prediction model. Conclusion: Combination of quantitative DWI analysis and clinical characteristics holds great potential in identifying the pGR patients and providing decision support for organ-preserving strategies after nCRT treatment. (C) 2018 Elsevier B.V. All rights reserved.
资助项目	National Natural Science Foundation of China[81471640] ; National Natural Science Foundation of China[81501621] ; National Natural Science Foundation of China[81671848] ; National Natural Science Foundation of China[81371635] ; National Natural Science Foundation of China[81501549] ; National Natural Science Foundation of China[81772012] ; 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[2017YFC1309101] ; National Key Research and Development Plan of China[2017YFC1309104] ; National Key Research and Development Plan of China[2016YFC0103001] ; International Innovation Team of CAS[20140491524] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support[ZYLX201803] ; Beijing million Talents Project[2017A13]
WOS关键词	LYMPH-NODE METASTASIS ; PHASE-III TRIAL ; PREOPERATIVE CHEMORADIOTHERAPY ; RADIOMICS ANALYSIS ; TEXTURE ANALYSIS ; MRI ; RADIATION ; CHEMOTHERAPY ; EXCISION ; NOMOGRAM
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:000460111700015
资助机构	National Natural Science Foundation of China ; Beijing Natural Science Foundation ; National Key Research and Development Plan of China ; International Innovation Team of CAS ; Beijing Municipal Science & Technology Commission ; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support ; Beijing million Talents Project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/24978]
专题	中国科学院自动化研究所
通讯作者	Dong, Enqing; Sun, Ying-Shi; Tian, Jie
作者单位	1.Shandong Univ, Sch Mech Elect & Informat Engn, Weihai 264209, Shandong, Peoples R China 2.CAS Key Lab Mol Imaging, Inst Automat, Beijing 100190, Peoples R China 3.Peking Univ Canc Hosp & Inst, Key Lab Carcinogenesis & Translat Res, Minist Educ, Dept Radiol, Beijing, Peoples R China 4.Univ Chinese Acad Sci, Beijing, Peoples R China 5.Beijing Key Lab Mol Imaging, Beijing, Peoples R China 6.CAS Ctr Excellence Brain Sci & Intelligence Techn, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Tang, Zhenchao,Zhang, Xiao-Yan,Liu, Zhenyu,et al. Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer[J]. RADIOTHERAPY AND ONCOLOGY,2019,132:100-108.
APA	Tang, Zhenchao.,Zhang, Xiao-Yan.,Liu, Zhenyu.,Li, Xiao-Ting.,Shi, Yan-Jie.,...&Tian, Jie.(2019).Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer.RADIOTHERAPY AND ONCOLOGY,132,100-108.
MLA	Tang, Zhenchao,et al."Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer".RADIOTHERAPY AND ONCOLOGY 132(2019):100-108.