Cargando…
Feature selection methods and predictive models in CT lung cancer radiomics
Radiomics is a technique that extracts quantitative features from medical images using data‐characterization algorithms. Radiomic features can be used to identify tissue characteristics and radiologic phenotyping that is not observable by clinicians. A typical workflow for a radiomics study includes...
Autores principales: | Ge, Gary, Zhang, Jie |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860004/ https://www.ncbi.nlm.nih.gov/pubmed/36527376 http://dx.doi.org/10.1002/acm2.13869 |
Ejemplares similares
-
Uniqueness of radiomic features in non‐small cell lung cancer
por: Ge, Gary, et al.
Publicado: (2022) -
Clinical impact of variability on CT radiomics and suggestions for suitable feature selection: a focus on lung cancer
por: Lee, Seung-Hak, et al.
Publicado: (2019) -
Voxel size and gray level normalization of CT radiomic features in lung cancer
por: Shafiq-ul-Hassan, Muhammad, et al.
Publicado: (2018) -
Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients
por: Sugai, Yuto, et al.
Publicado: (2021) -
CT Radiomics Combined With Clinicopathological Features to Predict Invasive Mucinous Adenocarcinoma in Patients With Lung Adenocarcinoma
por: Zhang, Junjie, et al.
Publicado: (2023)