Cargando…
Prediction of Prostate Cancer Disease Aggressiveness Using Bi-Parametric Mri Radiomics
SIMPLE SUMMARY: The use of radiomics has been studied to predict Gleason Score from bi-parametric prostate MRI examinations. However, different combinations of type of input data (whole prostate gland/lesion features), sampling strategy, feature selection method and machine learning algorithm can be...
Autores principales: | Rodrigues, Ana, Santinha, João, Galvão, Bernardo, Matos, Celso, Couto, Francisco M., Papanikolaou, Nickolas |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8657292/ https://www.ncbi.nlm.nih.gov/pubmed/34885175 http://dx.doi.org/10.3390/cancers13236065 |
Ejemplares similares
-
Value of handcrafted and deep radiomic features towards training robust machine learning classifiers for prediction of prostate cancer disease aggressiveness
por: Rodrigues, Ana, et al.
Publicado: (2023) -
Improving performance and generalizability in radiogenomics: a pilot study for prediction of IDH1/2 mutation status in gliomas with multicentric data
por: Santinha, João, et al.
Publicado: (2021) -
Application of Spectral Algorithm Applied to Spatially Registered Bi-Parametric MRI to Predict Prostate Tumor Aggressiveness: A Pilot Study
por: Mayer, Rulon, et al.
Publicado: (2023) -
Stability of Multi-Parametric Prostate MRI Radiomic Features to Variations in Segmentation
por: Thulasi Seetha, Sithin, et al.
Publicado: (2023) -
CT-Based Radiomics Analysis to Predict Malignancy in Patients with Intraductal Papillary Mucinous Neoplasm (IPMN) of the Pancreas
por: Tobaly, David, et al.
Publicado: (2020)