Cargando…
Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors
Prostate cancer diagnosis and treatment continues to be a major public health challenge. The heterogeneity of the disease is one of the major factors leading to imprecise diagnosis and suboptimal disease management. The improved resolution of functional multi-parametric magnetic resonance imaging (m...
Autores principales: | Parra, Andres N., Lu, Hong, Li, Qian, Stoyanova, Radka, Pollack, Alan, Punnen, Sanoj, Choi, Jung, Abdalah, Mahmoud, Lopez, Christopher, Gage, Kenneth, Park, Jong Y., Kosj, Yamoah, Pow-Sang, Julio M., Gillies, Robert J., Balagurunathan, Yoganand |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Impact Journals LLC
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6324677/ https://www.ncbi.nlm.nih.gov/pubmed/30647849 http://dx.doi.org/10.18632/oncotarget.26437 |
Ejemplares similares
-
Erratum: Predicting clinically significant prostate cancer using DCE-MRI habitat descriptors
por: Parra, N. Andres, et al.
Publicado: (2019) -
Habitats in DCE-MRI to Predict Clinically Significant Prostate Cancers
por: Parra, Nestor Andres, et al.
Publicado: (2019) -
Automatic Detection and Quantitative DCE-MRI Scoring of Prostate Cancer Aggressiveness
por: Parra, Nestor Andres, et al.
Publicado: (2017) -
Classifying Malignancy in Prostate Glandular Structures from Biopsy Scans with Deep Learning
por: Fogarty, Ryan, et al.
Publicado: (2023) -
Delineation of Tumor Habitats based on Dynamic Contrast Enhanced MRI
por: Chang, Yu-Cherng Channing, et al.
Publicado: (2017)