中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 中国科学院分子影像重点实验室
Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases

文献类型：期刊论文

作者Wei, Jingwei3,4,5; Cheng, Jin2; Gu, Dongsheng3,4,5; Chai, Fan2; Hong, Nan2; Wang, Yi2; Tian, Jie1,3,4,5,6
刊名MEDICAL PHYSICS
出版日期2020-11-30
页码10
ISSN号0094-2405
关键词colorectal liver metastases chemotherapy radiomics contrast‐ enhanced multidetector computed tomography deep learning
DOI10.1002/mp.14563
通讯作者Wang, Yi(wangyi@pkuph.edu.cn) ; Tian, Jie(tian@ieee.org)
英文摘要Purpose The purpose of this study was to develop and validate a deep learning (DL)-based radiomics model to predict the response to chemotherapy in colorectal liver metastases (CRLM). Methods In this retrospective study, we enrolled 192 patients diagnosed with CRLM who received first-line chemotherapy and were followed by response assessment. Tumor response was identified according to the Response Evaluation Criteria in Solid Tumors (RECIST). Contrast-enhanced multidetector computed tomography (MDCT) images were fed as inputs of the ResNet10-based DL radiomics model, and the possibility of response was predicted as the output. The final combined DL radiomics model was constructed by integrating the response-related clinical factors and the developed DL radiomics signature. A time-independent validation cohort (n = 48) was extracted from the 192 patients to evaluate the DL model with area under the receiver operating characteristic curve (AUC), specificity, and sensitivity. Meanwhile, a traditional radiomics model was constructed using least absolute shrinkage and selection operator (lasso) as comparisons with the DL-based model. Results According to RECIST criteria, 131 patients were identified as responders with complete response, partial response, and stable disease, while 61 patients were nonresponders with progression disease. The selected predictive clinical factor turned out to be the carcinoembryonic antigen (CEA) level with AUC of 0.489 (95% confidence interval [CI], 0.380-0.599) and 0.558 (95% CI, 0.374-0.741) in the training and validation cohorts, respectively. The DL-based model provided better performance than the traditional classifier-based radiomics model with significantly higher AUC (training: 0.903 [95% CI, 0.851-0.955] vs 0.745 [95% CI, 0.659-0.831]; validation: 0.820 [95% CI, 0.681-0.959] vs 0.598 [95% CI, 0.422-0.774]). The combination of DL-based model with the CEA level provided slightly increased performance with AUC of 0.935 [95% CI, 0.897-0.973] in the training cohort and 0.830 [95% CI, 0.688-0.973] in the validation cohort. Conclusions The developed DL-based radiomics model could improve the efficiency to predict the response to chemotherapy in CRLM, which may assist in subsequent personalized treatment decision-making in CRLM management.
WOS关键词HEPATIC METASTASIS ; CANCER ; CT ; ENHANCEMENT ; BEVACIZUMAB ; CARCINOMA ; FEATURES ; CRITERIA ; FOLFOX
资助项目Ministry of Science and Technology of China[2017YFA0205200] ; National Natural Science Foundation of China[81227901] ; National Natural Science Foundation of China[81527805] ; Chinese Academy of Sciences[GJJSTD20170004] ; Chinese Academy of Sciences[QYZDJ-SSW-JSC005] ; Beijing Municipal Science & Technology Commission[Z16110000 2616022] ; Beijing Municipal Science & Technology Commission[Z171100000117023]
WOS研究方向Radiology, Nuclear Medicine & Medical Imaging
语种英语
出版者WILEY
WOS记录号WOS:000594055200001
资助机构Ministry of Science and Technology of China ; National Natural Science Foundation of China ; Chinese Academy of Sciences ; Beijing Municipal Science & Technology Commission
源URL[http://ir.ia.ac.cn/handle/173211/41651]  
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Wang, Yi; Tian, Jie
作者单位1.Beihang Univ, Beijing Adv Innovat Ctr Big Data Based Precis Med, Sch Med, Beijing 100191, Peoples R China
2.Peking Univ Peoples Hosp, Dept Radiol, Beijing 100044, Peoples R China
3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
4.Beijing Key Lab Mol Imaging, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Automat, Key Lab Mol Imaging, Beijing 100190, Peoples R China
6.Xidian Univ, Sch Life Sci & Technol, Engn Res Ctr Mol & Neuro Imaging, Minist Educ, Xian 710126, Shaanxi, Peoples R China
推荐引用方式
GB/T 7714		 Wei, Jingwei,Cheng, Jin,Gu, Dongsheng,et al. Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases[J]. MEDICAL PHYSICS,2020:10.
APA Wei, Jingwei.,Cheng, Jin.,Gu, Dongsheng.,Chai, Fan.,Hong, Nan.,...&Tian, Jie.(2020).Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases.MEDICAL PHYSICS,10.
MLA Wei, Jingwei,et al."Deep learning-based radiomics predicts response to chemotherapy in colorectal liver metastases".MEDICAL PHYSICS (2020):10.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。