Dynamic Contrast-Enhanced Study in the mpMRI of the Prostate-Unnecessary or Underutilised? A Narrative Review.
Silva GuljašZdravka Krivdić DupanMaja Drežnjak MadunićMirela Šambić PencOliver PavlovicVinko KrajinaDeni PavokovićPetra Šmit TakačMarin ŠtefančićTamer SalhaPublished in: Diagnostics (Basel, Switzerland) (2023)
The aim of this review is to summarise recent scientific literature regarding the clinical use of DCE-MRI as a component of multiparametric resonance imaging of the prostate. This review presents the principles of DCE-MRI acquisition and analysis, the current role of DCE-MRI in clinical practice with special regard to its role in presently available categorisation systems, and an overview of the advantages and disadvantages of DCE-MRI described in the current literature. DCE-MRI is an important functional sequence that requires intravenous administration of a gadolinium-based contrast agent and gives information regarding the vascularity and capillary permeability of the lesion. Although numerous studies have confirmed that DCE-MRI has great potential in the diagnosis and monitoring of prostate cancer, its role is still inadequate in the PI-RADS categorisation. Moreover, there have been numerous scientific discussions about abandoning the intravenous application of gadolinium-based contrast as a routine part of MRI examination of the prostate. In this review, we summarised the recent literature on the advantages and disadvantages of DCE-MRI, focusing on an overview of currently available data on bpMRI and mpMRI, as well as on studies providing information on the potential better usability of DCE-MRI in improving the sensitivity and specificity of mpMRI examinations of the prostate.
Keyphrases
- contrast enhanced
- magnetic resonance imaging
- prostate cancer
- magnetic resonance
- computed tomography
- diffusion weighted imaging
- clinical practice
- systematic review
- radical prostatectomy
- high resolution
- high dose
- machine learning
- health information
- climate change
- deep learning
- social media
- big data
- fluorescence imaging
- human health
- data analysis