Cargando…
A Multi-Center, Multi-Vendor Study to Evaluate the Generalizability of a Radiomics Model for Classifying Prostate cancer: High Grade vs. Low Grade
Radiomics applied in MRI has shown promising results in classifying prostate cancer lesions. However, many papers describe single-center studies without external validation. The issues of using radiomics models on unseen data have not yet been sufficiently addressed. The aim of this study is to eval...
Autores principales: | Castillo T., Jose M., Starmans, Martijn P. A., Arif, Muhammad, Niessen, Wiro J., Klein, Stefan, Bangma, Chris H., Schoots, Ivo G., Veenland, Jifke F. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7926758/ https://www.ncbi.nlm.nih.gov/pubmed/33671533 http://dx.doi.org/10.3390/diagnostics11020369 |
Ejemplares similares
-
Classification of Clinically Significant Prostate Cancer on Multi-Parametric MRI: A Validation Study Comparing Deep Learning and Radiomics
por: Castillo T., Jose M., et al.
Publicado: (2021) -
Clinically significant prostate cancer detection and segmentation in low-risk patients using a convolutional neural network on multi-parametric MRI
por: Arif, Muhammad, et al.
Publicado: (2020) -
Automated Classification of Significant Prostate Cancer on MRI: A Systematic Review on the Performance of Machine Learning Applications
por: Castillo T., Jose M., et al.
Publicado: (2020) -
Quantification of Heterogeneity as a Biomarker in Tumor Imaging: A Systematic Review
por: Alic, Lejla, et al.
Publicado: (2014) -
Multi-classifier-based identification of COVID-19 from chest computed tomography using generalizable and interpretable radiomics features
por: Wang, Lu, et al.
Publicado: (2021)