Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients

doi:10.1007/s00262-024-03724-3

科研成果详情

题名	Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients
作者	Caii, Weimin 1; Wu, Xiao2; Guo, Kun2; Chen, Yongxian 3; Shi, Yubo 4; Chen, Junkai 1
发表日期	2024-06-04
发表期刊	CANCER IMMUNOLOGY IMMUNOTHERAPY 影响因子和分区
语种	英语
原始文献类型	Article
关键词	Immunotherapy Lung cancer Clinical durable benefit Deep learning Habitat radiomics
其他关键词	CELL LUNG-CANCER ; IMAGING BIOMARKERS ; HETEROGENEITY ; SURVIVAL ; CRITERIA
摘要	Background The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and ineffective extension. This study aimed to construct a non-invasive model for predicting immunotherapy response, based on the integration of deep learning and habitat radiomics in patients with advanced non-small cell lung cancer (NSCLC).Methods Independent patient cohorts from three medical centers were enrolled for training (n = 164) and test (n = 82). Habitat imaging radiomics features were derived from sub-regions clustered from individual's tumor by K-means method. The deep learning features were extracted based on 3D ResNet algorithm. Pearson correlation coefficient, T test and least absolute shrinkage and selection operator regression were used to select features. Support vector machine was applied to implement deep learning and habitat radiomics, respectively. Then, a combination model was developed integrating both sources of data.Results The combination model obtained a strong well-performance, achieving area under receiver operating characteristics curve of 0.865 (95% CI 0.772-0.931). The model significantly discerned high and low-risk patients, and exhibited a significant benefit in the clinical use.Conclusion The integration of deep-leaning and habitat radiomics contributed to predicting response to immunotherapy in patients with NSCLC. The developed integration model may be used as potential tool for individual immunotherapy management.
资助项目	National Natural Science Foundation of China
出版者	SPRINGER
ISSN	0340-7004
EISSN	1432-0851
卷号	73 期号:8
DOI	10.1007/s00262-024-03724-3
页数	10
WOS类目	Oncology ; Immunology
WOS研究方向	Oncology ; Immunology
WOS记录号	WOS:001239361300015
收录类别	SCIE ; SCOPUS ; PUBMED
URL	查看原文
PubMed ID	38833187
SCOPUSEID	2-s2.0-85194994501
通讯作者地址	[Chen, Junkai]Wenzhou Hosp Integrated Tradit Chinese & Western M, Dept Emergency, Wenzhou 325000, Peoples R China.
Scopus学科分类	Immunology and Allergy;Immunology;Oncology;Cancer Research
引用统计
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/214639
专题	附属第一医院附属第一医院_消化内科
通讯作者	Chen, Junkai
作者单位	1.Wenzhou Hosp Integrated Tradit Chinese & Western M, Dept Emergency, Wenzhou 325000, Peoples R China; 2.Wenzhou Med Univ, Affiliated Hosp 1, Dept Gastroenterol & Hepatol, Wenzhou 325000, Peoples R China; 3.Xiamen Second Peoples Hosp, Dept Chest Canc, Xiamen 36100, Peoples R China; 4.Yueqing Peoples Hosp, Dept Pulm, Wenzhou 325000, Peoples R China
推荐引用方式 GB/T 7714	Caii, Weimin,Wu, Xiao,Guo, Kun,et al. Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients[J]. CANCER IMMUNOLOGY IMMUNOTHERAPY,2024,73(8).
APA	Caii, Weimin, Wu, Xiao, Guo, Kun, Chen, Yongxian, Shi, Yubo, & Chen, Junkai. (2024). Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients. CANCER IMMUNOLOGY IMMUNOTHERAPY, 73(8).
MLA	Caii, Weimin,et al."Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients".CANCER IMMUNOLOGY IMMUNOTHERAPY 73.8(2024).