Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy

doi:10.1016/j.cmpb.2024.108295

科研成果详情

题名	Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy
作者	Su, Wanyu1,2; Cheng, Dezhi3; Ni, Weihua1,2; Ai, Yao1; Yu, Xianwen1,2; Tan, Ninghang1,2; Wu, Jianping1,4; Fu, Wen1; Li, Chenyu1; Xie, Congying1; Shen, Meixiao5,6; Jin, Xiance1,7
发表日期	2024-09
发表期刊	Computer Methods and Programs in Biomedicine 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Deep learning Dosiomics Lung cancer Radiation pneumonitis Radiomics
其他关键词	RISK-FACTORS ; RADIOMICS ; RADIOTHERAPY ; TOXICITY
摘要	Background and objective: To evaluate the feasibility and accuracy of radiomics, dosiomics, and deep learning (DL) in predicting Radiation Pneumonitis (RP) in lung cancer patients underwent volumetric modulated arc therapy (VMAT) to improve radiotherapy safety and management. Methods: Total of 318 and 31 lung cancer patients underwent VMAT from First Affiliated Hospital of Wenzhou Medical University (WMU) and Quzhou Affiliated Hospital of WMU were enrolled for training and external validation, respectively. Models based on radiomics (R), dosiomics (D), and combined radiomics and dosiomics features (R+D) were constructed and validated using three machine learning (ML) methods. DL models trained with CT (DLR), dose distribution (DLD), and combined CT and dose distribution (DL(R+D)) images were constructed. DL features were then extracted from the fully connected layers of the best-performing DL model to combine with features of the ML model with the best performance to construct models of R+DLR, D+DLD, R+D+DL(R+D)) for RP prediction. Results: The R+D model achieved a best area under curve (AUC) of 0.84, 0.73, and 0.73 in the internal validation cohorts with Support Vector Machine (SVM), XGBoost, and Logistic Regression (LR), respectively. The DL(R+D) model achieved a best AUC of 0.89 and 0.86 using ResNet-34 in training and internal validation cohorts, respectively. The R+D+DL(R+D) model achieved a best performance in the external validation cohorts with an AUC, accuracy, sensitivity, and specificity of 0.81(0.62-0.99), 0.81, 0.84, and 0.67, respectively. Conclusions: The integration of radiomics, dosiomics, and DL features is feasible and accurate for the RP prediction to improve the management of lung cancer patients underwent VMAT.
资助项目	Zhejiang Natural Science Foundation[Z24A050009];Zhejiang Provincial Health Science and Technology Program[WKJZJ-2437];Wenzhou Science and Technology Bureau[ZY2022016,ZY2020011]
出版者	Elsevier Ireland Ltd
ISSN	0169-2607
EISSN	1872-7565
卷号	254
DOI	10.1016/j.cmpb.2024.108295
页数	8
WOS类目	Computer Science, Interdisciplinary Applications ; Computer Science, Theory & Methods ; Engineering, Biomedical ; Medical Informatics
WOS研究方向	Computer Science ; Engineering ; Medical Informatics
WOS记录号	WOS:001294661200001
收录类别	SCOPUS ; EI ; PUBMED ; SCIE
EI入藏号	20242516289635
EI主题词	Hospitals
EI分类号	461.2 Biological Materials and Tissue Engineering ; 461.4 Ergonomics and Human Factors Engineering ; 461.6 Medicine and Pharmacology ; 462.2 Hospitals, Equipment and Supplies ; 622.3 Radioactive Material Applications ; 723 Computer Software, Data Handling and Applications ; 723.5 Computer Applications
URL	查看原文
PubMed ID	38905987
SCOPUSEID	2-s2.0-85196267956
通讯作者地址	[Jin, Xiance]Department of Radiotherapy Center,1st Affiliated Hospital of Wenzhou Medical University,Wenzhou,325000,China
Scopus学科分类	Software;Computer Science Applications;Health Informatics
SCOPUS_ID	SCOPUS_ID:85196267956
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/215533
专题	附属第一医院温州医科大学基础医学院（机能实验教学中心）眼视光学院（生物医学工程学院）、附属眼视光医院其他_温州医科大学附属衢州医院
通讯作者	Jin, Xiance
作者单位	1.Department of Radiotherapy Center,1st Affiliated Hospital of Wenzhou Medical University,Wenzhou,325000,China; 2.Cixi Biomedical Research Institute,Wenzhou Medical University,Zhejiang,315000,China; 3.Department of Thoracic Surgery,1st Affiliated Hospital of Wenzhou Medical University,Wenzhou,325000,China; 4.Department of Radiotherapy,The Quzhou Affiliated Hospital of Wenzhou Medical University,Quzhou People’ s Hospital,Quzhou,324000,China; 5.School of Eye,Wenzhou Medical University,Wenzhou,325000,China; 6.The Eye Hospital of Wenzhou Medical University,Wenzhou,325000,China; 7.School of Basic Medical Science,Wenzhou Medical University,Wenzhou,325000,China
第一作者单位	附属第一医院; 温州医科大学
通讯作者单位	附属第一医院
第一作者的第一单位	附属第一医院
推荐引用方式 GB/T 7714	Su, Wanyu,Cheng, Dezhi,Ni, Weihua,et al. Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy[J]. Computer Methods and Programs in Biomedicine,2024,254.
APA	Su, Wanyu., Cheng, Dezhi., Ni, Weihua., Ai, Yao., Yu, Xianwen., ... & Jin, Xiance. (2024). Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy. Computer Methods and Programs in Biomedicine, 254.
MLA	Su, Wanyu,et al."Multi-omics deep learning for radiation pneumonitis prediction in lung cancer patients underwent volumetric modulated arc therapy".Computer Methods and Programs in Biomedicine 254(2024).