Identifying Robust Radiomics Features for Lung Cancer by Using In-Vivo and Phantom Lung Lesions

We propose a novel framework for determining radiomics feature robustness by considering the effects of both biological and noise signals. This framework is preliminarily tested in a study predicting the epidermal growth factor receptor (EGFR) mutation status in non-small cell lung cancer (NSCLC) pa...

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Detalles Bibliográficos
Autores principales: Lu, Lin, Sun, Shawn H., Afran, Aaron, Yang, Hao, Lu, Zheng Feng, So, James, Schwartz, Lawrence H., Zhao, Binsheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934702/
https://www.ncbi.nlm.nih.gov/pubmed/33681463
http://dx.doi.org/10.3390/tomography7010005

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7934702/
https://www.ncbi.nlm.nih.gov/pubmed/33681463
http://dx.doi.org/10.3390/tomography7010005

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