Quantitative Prostate MRI, From the <i>AJR</i> Special Series on Quantitative Imaging.
Authors
Affiliations (11)
Affiliations (11)
- Department of Radiology, Weill Cornell Medical College, 525 E 68th St, New York, NY 10021.
- Department of Radiology, University of Chicago, Chicago, IL.
- The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
- Department of Radiology, Brigham and Women's Hospital, Boston, MA.
- Quantitative Health Sciences, Cleveland Clinic, Cleveland, OH.
- Centre for Medical Imaging, University College London, London, UK.
- Department of Radiology, Cleveland Clinic, Cleveland, OH.
- Department of Radiology, University of California, San Diego, CA.
- Departments of Medical Physics and Radiology, Memorial Sloan Kettering Cancer Center, New York, NY.
- GSK, London, England.
- Department of Radiology, University of Michigan, Ann Arbor, MI.
Abstract
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 as well as for recurrent cancer after treatment. Advanced diffusion techniques, including intravoxel incoherent motion imaging, diffusion kurtosis imaging, 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 fraction and hybrid multidimensional MRI metrics. Dynamic contrast-enhanced imaging, primarily using a modified Tofts model, also shows independent discriminatory value. Finally, quantitative lesion size and shape features can be combined with the aforementioned techniques and can 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 and use cases.