Institutional Repository of Chinese Acad Sci, Inst Automat, CAS Key Lab Mol Imaging, Beijing 100190, Peoples R China
3D Deep Learning Model for the Pretreatment Evaluation of Treatment Response in Esophageal Carcinoma: A Prospective Study (ChiCTR2000039279) | |
Li, Xiaoqin1,2; Gao, Han3,4![]() ![]() | |
发表期刊 | INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
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ISSN | 0360-3016 |
2021-11-15 | |
卷号 | 111期号:4页码:926-935 |
通讯作者 | Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Li, Baosheng(bsli@sdfmu.edu.cn) |
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. |
DOI | 10.1016/j.ijrobp.2021.06.033 |
关键词[WOS] | PREDICT TREATMENT RESPONSE ; SQUAMOUS-CELL CARCINOMA ; NEOADJUVANT CHEMORADIOTHERAPY ; CANCER ; RADIOMICS ; THERAPY |
收录类别 | SCI |
语种 | 英语 |
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] |
项目资助者 | 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 |
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:000709807000013 |
出版者 | ELSEVIER SCIENCE INC |
七大方向——子方向分类 | 医学影像处理与分析 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/46286 |
专题 | 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.Shandong First Med Univ & Shandong Acad Med Sci, Shandong Canc Hosp & Inst, Jinan, Peoples R China 2.Shandong First Med Univ & Shandong Acad Med Sci, Shandong Canc Hosp & Inst, Shandong Med Imaging & Radiotherapy Engn Ctr SMIR, Jinan, Shandong, Peoples R China 3.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med & Engn, Beijing, 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.Xidian Univ, Engn Res Ctr Mol & Neuro Imaging, Sch Life Sci & Technol, Minist Educ, Xian, Shaanxi, Peoples R China 6.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Inst Automat, Beijing, Peoples R China 7.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 8.Beihang Univ, Minist Ind & Informat Technol, Key Lab Big Data Based Precis Med, Beijing, Peoples R China |
通讯作者单位 | 中国科学院自动化研究所 |
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. |
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