Quantitative Prostate MRI, From the


Journal

AJR. American journal of roentgenology
ISSN: 1546-3141
Titre abrégé: AJR Am J Roentgenol
Pays: United States
ID NLM: 7708173

Informations de publication

Date de publication:
02 Oct 2024
Historique:
medline: 2 10 2024
pubmed: 2 10 2024
entrez: 2 10 2024
Statut: aheadofprint

Résumé

Prostate MRI has traditionally relied on qualitative interpretation. However, quantitative components hold the potential to markedly improve performance. The ADC from DWI is probably the most widely recognized quantitative MRI biomarker and has shown strong discriminatory value for clinically significant prostate cancer (csPCa) as well as for recurrent cancer after treatment. Advanced diffusion techniques, including intravoxel incoherent motion, diffusion kurtosis, diffusion tensor imaging, and specific implementations such as restriction spectrum imaging, purport even better discrimination, but are more technically challenging. The inherent T1 and T2 of tissue also provide diagnostic value, with more advanced techniques deriving luminal water imaging and hybrid-multidimensional MRI. Dynamic contrast-enhanced imaging, primarily using a modified Tofts model, also shows independent discriminatory value. Finally, quantitative size and shape features can be combined with the aforementioned techniques and be further refined using radiomics, texture analysis, and artificial intelligence. Which technique will ultimately find widespread clinical use will depend on validation across a myriad of platforms use-cases.

Identifiants

pubmed: 39356481
doi: 10.2214/AJR.24.31715
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Daniel J A Margolis (DJA)

Department of Radiology, Weill Cornell Medical College, New York, NY.

Aritrick Chatterjee (A)

Department of Radiology, University of Chicago, Chicago, IL.

Nandita M deSouza (NM)

The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.

Andriy Fedorov (A)

Department of Radiology, Brigham and Women's Hospital, Boston, MA.

Fiona M Fennessy (FM)

Department of Radiology, Brigham and Women's Hospital, Boston, MA.

Stephan E Maier (SE)

Department of Radiology, Brigham and Women's Hospital, Boston, MA.

Nancy Obuchowski (N)

Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH.

Shonit Punwani (S)

Centre for Medical Imaging, University College London, London, UK.

Andrei Purysko (A)

Department of Radiology, Cleveland Clinic, Cleveland, OH.

Rebecca Rakow-Penner (R)

Department of Radiology, University of California, San Diego, CA.

Amita Shukla-Dave (A)

Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center, New York, NY.

Clare M Tempany (CM)

Department of Radiology, Brigham and Women's Hospital, Boston, MA.

Dariya Malyarenko (D)

Department of Radiology, University of Michigan, Ann Arbor, MI.

Classifications MeSH