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Interpretation and reporting multiparametric prostate MRI: a primer for residents and novices

Overview of attention for article published in Abdominal Radiology, February 2014
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Title
Interpretation and reporting multiparametric prostate MRI: a primer for residents and novices
Published in
Abdominal Radiology, February 2014
DOI 10.1007/s00261-014-0097-x
Pubmed ID
Authors

Sandeep S. Hedgire, Steven C. Eberhardt, Rachel Borczuk, Shaunagh McDermott, Mukesh G. Harisinghani

Abstract

Multiparametric MRI has developed as a tool for prostate cancer lesion detection, characterization, staging, surveillance, and imaging of local recurrence. Given the disease frequency and the growing importance of imaging, as reliance on PSA declines, radiologists involved in prostate MRI imaging must become proficient with the fundamentals of multiparametric prostate MRI (T2WI, DWI, DCE-MRI, and MR spectroscopy). Interpretation and reporting must yield accuracy, consistency, and add value to clinical care. This review provides a primer to novices and trainees learning about multiparametric prostate MRI. MRI technique is presented along with the use of particular MRI sequences. Relevant prostate anatomy is outlined and imaging features of prostate cancer with staging are discussed. Finally structured reporting is introduced, and some limitations of prostate MRI are discussed.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 51 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Spain 1 2%
Canada 1 2%
Unknown 49 96%

Demographic breakdown

Readers by professional status Count As %
Other 8 16%
Student > Doctoral Student 8 16%
Professor > Associate Professor 8 16%
Student > Bachelor 5 10%
Researcher 5 10%
Other 13 25%
Unknown 4 8%
Readers by discipline Count As %
Medicine and Dentistry 35 69%
Computer Science 3 6%
Mathematics 2 4%
Nursing and Health Professions 1 2%
Agricultural and Biological Sciences 1 2%
Other 2 4%
Unknown 7 14%