Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features

doi:10.3390/diagnostics11101875

	Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features
	Tian, Yuchi 1; Komolafe, Temitope Emmanuel2 ; Zheng, Jian 3; Zhou, Guofeng 4; Chen, Tao 5; Zhou, Bo 6,7; Yang, Xiaodong 1,3
	2021-10
发表期刊	DIAGNOSTICS (IF:3.0[JCR-2023],3.1[5-Year])
EISSN	2075-4418
卷号	11 期号:10
DOI	10.3390/diagnostics11101875
摘要	To assess if quantitative integrated deep learning and radiomics features can predict the PD-L1 expression level in preoperative MRI of hepatocellular carcinoma (HCC) patients. The data in this study consist of 103 hepatocellular carcinoma patients who received immunotherapy in a single center. These patients were divided into a high PD-L1 expression group (30 patients) and a low PD-L1 expression group (73 patients). Both radiomics and deep learning features were extracted from their MRI sequence of T2-WI, which were merged into an integrative feature space for machine learning for the prediction of PD-L1 expression. The five-fold cross-validation was adopted to validate the performance of the model, while the AUC was used to assess the predictive ability of the model. Based on the five-fold cross-validation, the integrated model achieved the best prediction performance, with an AUC score of 0.897 & PLUSMN; 0.084, followed by the deep learning-based model with an AUC of 0.852 & PLUSMN; 0.043 then the radiomics-based model with AUC of 0.794 & PLUSMN; 0.035. The feature set integrating radiomics and deep learning features is more effective in predicting PD-L1 expression level than only one feature type. The integrated model can achieve fast and accurate prediction of PD-L1 expression status in preoperative MRI of HCC patients.
关键词	radiomics deep learning hepatocellular carcinoma PD-L1 immunotherapy
URL	查看原文
收录类别	SCIE
语种	英语
WOS研究方向	General & Internal Medicine
WOS类目	Medicine, General & Internal
WOS记录号	WOS:000716258900001
出版者	MDPI
原始文献类型	Article
引用统计	正在获取...
文献类型	期刊论文
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/130304
专题	生物医学工程学院_PI研究组_沈定刚组
通讯作者	Zhou, Bo; Yang, Xiaodong
作者单位	1.Fudan Univ, Acad Engn & Technol, Shanghai 200433, Peoples R China; 2.Shanghai Tech Univ, Sch Biomed Engn, Shanghai 201210, Peoples R China; 3.Chinese Acad Sci, Suzhou Inst Biomed Engn & Technol, Dept Med Imaging, Suzhou 215163, Peoples R China; 4.Zhongshan Hosp, Dept Radiol, Shanghai 200032, Peoples R China; 5.Fudan Univ, Sch Informat Sci & Technol, Shanghai 200433, Peoples R China; 6.Zhongshan Hosp, Dept Intervent Radiol, Shanghai 200032, Peoples R China; 7.Natl Clin Res Ctr Intervent Med, Shanghai 200032, Peoples R China
推荐引用方式 GB/T 7714	Tian, Yuchi,Komolafe, Temitope Emmanuel,Zheng, Jian,et al. Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features[J]. DIAGNOSTICS,2021,11(10).
APA	Tian, Yuchi.,Komolafe, Temitope Emmanuel.,Zheng, Jian.,Zhou, Guofeng.,Chen, Tao.,...&Yang, Xiaodong.(2021).Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features.DIAGNOSTICS,11(10).
MLA	Tian, Yuchi,et al."Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features".DIAGNOSTICS 11.10(2021).