Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
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 | |
发表期刊 | RADIOTHERAPY AND ONCOLOGY |
ISSN | 0167-8140 |
2019-03-01 | |
卷号 | 132页码:100-108 |
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. |
关键词 | Locally advanced rectal cancer Neoadjuvant chemoradiotherapy Organ-preserving strategies Diffusion weighted imaging Decision support |
DOI | 10.1016/j.radonc.2018.11.007 |
关键词[WOS] | LYMPH-NODE METASTASIS ; PHASE-III TRIAL ; PREOPERATIVE CHEMORADIOTHERAPY ; RADIOMICS ANALYSIS ; TEXTURE ANALYSIS ; MRI ; RADIATION ; CHEMOTHERAPY ; EXCISION ; NOMOGRAM |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Beijing million Talents Project[2017A13] ; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support[ZYLX201803] ; National Key Research and Development Plan of China[2017YFC1309104] ; National Key Research and Development Plan of China[2017YFC1309101] ; National Natural Science Foundation of China[81371635] ; National Natural Science Foundation of China[81671848] ; National Natural Science Foundation of China[81501621] ; National Natural Science Foundation of China[81471640] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; National Key Research and Development Plan of China[2016YFC0103001] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Natural Science Foundation of China[81772012] ; Beijing Natural Science Foundation[7182109] ; National Natural Science Foundation of China[81501549] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; International Innovation Team of CAS[20140491524] ; National Natural Science Foundation of China[81527805] ; National Natural Science Foundation of China[81527805] ; International Innovation Team of CAS[20140491524] ; Beijing Municipal Science & Technology Commission[Z161100002616022] ; National Natural Science Foundation of China[81501549] ; Beijing Natural Science Foundation[7182109] ; National Natural Science Foundation of China[81772012] ; National Key Research and Development Plan of China[2017YFA0205200] ; National Key Research and Development Plan of China[2016YFC0103001] ; Beijing Municipal Science & Technology Commission[Z171100000117023] ; 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 Key Research and Development Plan of China[2017YFC1309101] ; National Key Research and Development Plan of China[2017YFC1309104] ; Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support[ZYLX201803] ; Beijing million Talents Project[2017A13] |
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:000460111700015 |
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/173211/24979 |
专题 | 中国科学院分子影像重点实验室 |
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. |
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