CASIA OpenIR
Quantitative analysis of diffusion weighted imaging to predict pathological good response to neoadjuvant chemoradiation for locally advanced rectal cancer
Tang, Zhenchao1; Zhang, Xiao-Yan3; Liu, Zhenyu2,5; Li, Xiao-Ting3; Shi, Yan-Jie3; Wang, Shou2; Fang, Mengjie2; Shen, Chen2; Dong, Enqing1; Sun, Ying-Shi3; Tian, Jie2,4,6
Source PublicationRADIOTHERAPY AND ONCOLOGY
ISSN0167-8140
2019-03-01
Volume132Pages:100-108
Corresponding AuthorDong, Enqing(enqdong@sdu.edu.cn) ; Sun, Ying-Shi(sys27@163.com) ; Tian, Jie(jie.tian@ia.ac.cn)
AbstractBackground 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.
KeywordLocally advanced rectal cancer Neoadjuvant chemoradiotherapy Organ-preserving strategies Diffusion weighted imaging Decision support
DOI10.1016/j.radonc.2018.11.007
WOS KeywordLYMPH-NODE METASTASIS ; PHASE-III TRIAL ; PREOPERATIVE CHEMORADIOTHERAPY ; RADIOMICS ANALYSIS ; TEXTURE ANALYSIS ; MRI ; RADIATION ; CHEMOTHERAPY ; EXCISION ; NOMOGRAM
Indexed BySCI
Language英语
Funding ProjectNational 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]
Funding OrganizationNational 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
WOS Research AreaOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS SubjectOncology ; Radiology, Nuclear Medicine & Medical Imaging
WOS IDWOS:000460111700015
PublisherELSEVIER IRELAND LTD
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24979
Collection中国科学院自动化研究所
Corresponding AuthorDong, Enqing; Sun, Ying-Shi; Tian, Jie
Affiliation1.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
Recommended Citation
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.
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