Article: CDPNet: a radiomic feature learning method with epigenetic application to estimating MGMT promoter methylation status in glioblastoma.
Proceedings of SPIE--the International Society for Optical Engineering
2024 Volume 12930
Abstract: Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains ...
Abstract | Radiomics has been widely recognized for its effectiveness in decoding tumor phenotypes through the extraction of quantitative imaging features. However, the robustness of radiomic methods to estimate clinically relevant biomarkers non-invasively remains largely untested. In this study, we propose Cascaded Data Processing Network (CDPNet), a radiomic feature learning method to predict tumor molecular status from medical images. We apply CDPNet to an epigenetic case, specifically targeting the estimation of |
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Language | English |
Publishing date | 2024-04-02 |
Publishing country | United States |
Document type | Journal Article |
ISSN | 0277-786X |
ISSN | 0277-786X |
DOI | 10.1117/12.3009716 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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