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 ; Zhu, Jian1,2; Huang, Yong1,2; Zhu, Yongbei3,4; Huang, Wei1,2; Li, Zhenjiang1,2; Sun, Kai3,5; Liu, Zhenyu4,6,7; Tian, Jie3,4,5,8; Li, Baosheng1,2
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| Source Publication | INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
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| ISSN | 0360-3016 |
| 2021-11-15 | |
| Volume | 111Issue:4Pages:926-935 |
| Corresponding Author | Liu, Zhenyu(zhenyu.liu@ia.ac.cn) ; Tian, Jie(jie.tian@ia.ac.cn) ; Li, Baosheng(bsli@sdfmu.edu.cn) |
| Abstract | 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 |
| WOS Keyword | PREDICT TREATMENT RESPONSE ; SQUAMOUS-CELL CARCINOMA ; NEOADJUVANT CHEMORADIOTHERAPY ; CANCER ; RADIOMICS ; THERAPY |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | 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 Research Area | Oncology ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS Subject | Oncology ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS ID | WOS:000709807000013 |
| Publisher | ELSEVIER SCIENCE INC |
| Sub direction classification | 医学影像处理与分析 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/46286 |
| Collection | 中国科学院分子影像重点实验室 |
| Corresponding Author | Liu, Zhenyu; Tian, Jie; Li, Baosheng |
| Affiliation | 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 |
| Corresponding Author Affilication | Institute of Automation, Chinese Academy of Sciences |
| Recommended Citation 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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