Abstract #2070

An mpMRI derived Logistic Regression Model for Gleason 4 Pattern Prediction in Peripheral Zone Prostate Cancer

Michela Antonelli¹, Edward W Johnston², Manuel Jorge Cardoso¹, Sebastien Ourselin*^1,3, and Shonit Punwani*⁴

¹Translational Imaging Group, CMIC, University College London, London, UK, London, United Kingdom, ²Centre for Medical Imaging, University College London, London, UK, London, United Kingdom, ³Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK, ⁴Academic Radiology, University College London Centre for Medical Imaging, London, UK

Gleason grade is the most important determinant of prognosis and survival in prostate cancer, and is determined using prostate biopsy. Here we investigate whether multi-parametric MRI can be used to classify Gleason grade non-invasively with logistic regression (LR) models, classifying tumours into 3+3 and those containing a 4 component. A selection of clinical and quantitative MRI metrics were used. The LR model was trained in ninety-nine patients and tested following a Leave-One-Out (LOO) analysis on a temporal separated cohort of nineteen patients. LR models were shown to predict the presence of Gleason 4 component in cancer lesions both before and after LOO analysis.

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