3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279)

doi:10.1016/j.ijrobp.2021.06.033

CORC > 自动化研究所 > 中国科学院自动化研究所 > 中国科学院分子影像重点实验室

	3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279)
	Li, Xiaoqin 6,7; Gao, Han 4,5; Zhu, Jian 6,7; Huang, Yong 6,7; Zhu, Yongbei 4,5; Huang, Wei 6,7; Li, Zhenjiang 6,7; Sun, Kai 3,5; Liu, Zhenyu 1,2,4; Tian, Jie 3,4,5,8
刊名	INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
	2021-11-15
卷号	111 期号:4 页码:926-935
ISSN号	0360-3016
DOI	10.1016/j.ijrobp.2021.06.033
通讯作者	Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Li, Baosheng(bsli@sdfmu.edu.cn)
英文摘要	Purpose: To develop and validate a pretreatment computed tomography (CT)-based deep-learning (DL) model for predicting the treatment response to concurrent chemoradiation therapy (CCRT) among patients with locally advanced thoracic esophageal squamous cell carcinoma (TESCC). Methods and Materials: We conducted a prospective, multicenter study on the therapeutic efficacy of CCRT among TESCC patients across 9 hospitals in China (ChiCTR2000039279). A total of 306 patients with locally advanced TESCC diagnosed by histopathology from August 2015 to May 2020 were included in this study. A 3-dimensional DL radiomics model (3DDLRM) was developed and validated based on pretreatment CT images to predict the response to CCRT. Furthermore, the prediction performance of the newly developed 3D-DLRM was analyzed according to 3 categories: radiation therapy plan, radiation field, and prescription dose used. Results: The 3D-DLRM achieved good prediction performance, with areas under the receiver operating characteristic curve of 0.897 (95% confidence interval, 0.840-0.959) for the training cohort and 0.833 (95% confidence interval, 0.654-1.000) for the validation cohort. Specifically, the 3D-DLRM accurately predicted patients who would not respond to CCRT, with a positive predictive value (PPV) of 100% for the validation cohort. Moreover, the 3D-DLRM performed well in all 3 categories, each with areas under the receiver operating characteristic curve of >0.8 and positive predictive values of approximately 100%. Conclusion: The proposed pretreatment CT-based 3D-DLRM provides a potential tool for predicting the response to CCRT among patients with locally advanced TESCC. With the help of precise pretreatment prediction, we may guide the individualized treatment of patients and improve survival. (C) 2021 The Author(s). Published by Elsevier Inc.
资助项目	National Natural Science Foundation of China[81530060] ; National Natural Science Foundation of China[81773232] ; National Natural Science Foundation of China[81874224] ; National Natural Science Foundation of China[81922040] ; Key Technology Research and Development Program of Shandong[2017CXZC1206] ; Beijing Natural Science Foundation[7182109] ; Youth Innovation Promotion Association CAS[2019136] ; Foundation of Taishan Scholars[tsqn201909187] ; Foundation of Taishan Scholars[tsqn201909140] ; Foundation of Taishan Scholars[ts20120505]
WOS关键词	PREDICT TREATMENT RESPONSE ; SQUAMOUS-CELL CARCINOMA ; NEOADJUVANT CHEMORADIOTHERAPY ; CANCER ; RADIOMICS ; THERAPY
WOS研究方向	Oncology ; Radiology, Nuclear Medicine & Medical Imaging
语种	英语
出版者	ELSEVIER SCIENCE INC
WOS记录号	WOS:000709807000013
资助机构	National Natural Science Foundation of China ; Key Technology Research and Development Program of Shandong ; Beijing Natural Science Foundation ; Youth Innovation Promotion Association CAS ; Foundation of Taishan Scholars
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/46285]
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	Liu, Zhenyu; Tian, Jie; Li, Baosheng
作者单位	1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China 3.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China 4.Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing Key Lab Mol Imaging,State Key Lab Managem, Beijing, Peoples R China 5.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, Peoples R China 6.Shandong First Med Univ & Shandong Acad Med Sci, Shandong Canc Hosp & Inst, Shandong Med Imaging & Radiotherapy Engn Ctr SMIR, Jinan, Shandong, Peoples R China 7.Shandong First Med Univ & Shandong Acad Med Sci, Shandong Canc Hosp & Inst, Jinan, Peoples R China 8.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China
推荐引用方式 GB/T 7714	Li, Xiaoqin,Gao, Han,Zhu, Jian,et al. 3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279)[J]. INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS,2021,111(4):926-935.
APA	Li, Xiaoqin.,Gao, Han.,Zhu, Jian.,Huang, Yong.,Zhu, Yongbei.,...&Li, Baosheng.(2021).3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279).INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS,111(4),926-935.
MLA	Li, Xiaoqin,et al."3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279)".INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS 111.4(2021):926-935.