Abstract #2752

Logistic Regression Models May Predict Gleason Grade of Prostate Cancer in the Peripheral Zone but Not the Transition Zone

Edward William Johnston¹, Kenneth Cheung¹, Nikolas Dikaios¹, Harbir Singh Sidhu¹, Mrishta Brizmohun Appayya¹, Lucy Simmons², Alex Freeman³, Hashim Ahmed², David Atkinson¹, and Shonit Punwani¹

¹UCL Centre for Medical Imaging, London, United Kingdom, ²Department of Urology, University College Hospital, London, United Kingdom, ³Cellular Pathology, University College Hospital, London, United Kingdom

Quantitative imaging metrics forming multiparametric prostate MRI have been shown to correlate with Gleason grade. We therefore aimed to develop logistic regression models which predict aggressive prostate cancer in focal lesions using quantitative MRI parameters. Models were constructed separately for the transition and peripheral zones, using data from 176 examinations.

In the peripheral zone, a combination of 3 simple parameters were found to predict a Gleason 4/5 component with a similar sensitivity and specificity to experienced radiologists. However, performance was relatively poor in the transition zone. Logistic regression models may therefore prove useful when training radiologists to characterise prostate cancer.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.