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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.

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