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| Assessing PD-L1 Expression Level via Preoperative MRI in HCC Based on Integrating Deep Learning and Radiomics Features | |
| 2021-10 | |
| Source Publication | DIAGNOSTICS
 |
| EISSN | 2075-4418 |
| Volume | 11Issue:10 |
| DOI | 10.3390/diagnostics11101875 |
| Abstract | 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. |
| Keyword | radiomics deep learning hepatocellular carcinoma PD-L1 immunotherapy |
| URL | 查看原文 |
| Indexed By | SCIE |
| Language | 英语 |
| WOS Research Area | General & Internal Medicine |
| WOS Subject | Medicine, General & Internal |
| WOS ID | WOS:000716258900001 |
| Publisher | MDPI |
| Original Document Type | Article |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/130304 |
| Collection | 生物医学工程学院_PI研究组_沈定刚组 |
| Corresponding Author | Zhou, Bo; Yang, Xiaodong |
| Affiliation | 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 |
| Recommended Citation 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). |
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