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Can texture features computed from the joint intensity distribution of different MRI sequences accurately predict prostate cancer grade?

   
@article{JMAI4593,
	author = {Vasilis Stavrinides and Lina Carmona Echeverria and Hayley C. Whitaker},
	title = {Can texture features computed from the joint intensity distribution of different MRI sequences accurately predict prostate cancer grade?},
	journal = {Journal of Medical Artificial Intelligence},
	volume = {1},
	number = {0},
	year = {2018},
	keywords = {},
	abstract = {The diagnostic landscape of prostate cancer has evolved rapidly, from prostate-specific antigen (PSA) testing to exciting new technologies that allow visualization of the disease, moving away from random sampling to targeted biopsies. Multiparametric magnetic resonance imaging (mpMRI) is a new modality that combines T2-weighted (T2W), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) sequences, each designed to reveal specific microstructural features typically associated with malignancy such as increased vascularity and cellularity.},
	issn = {2617-2496},	url = {https://jmai.amegroups.org/article/view/4593}
}