Keywords: Cancer, Prostate, rostate cancer diagnosis, Gleason score prediction, Magnetic Resonance Imaging (MRI), Convolutional Neural Networks (CNN), Pathological upgrading detection, Lesion characterization, Clinical significance determination, Artificial Intelligence (AI) in medical imaging, Precision medicine, Patient management, Predictive modeling in oncology, Diagnostic precision, Risk assessment in prostate cancer, Biopsy accuracy, Medical imaging technologies, Machine learning in healthcare
Motivation: Patients with low risk (Gleason 6) MR visible prostate cancer on initial biopsy are frequently upgraded to aggressive higher risk (Gleason 7 or higher) cancer. Identifying this progression early is difficult.
Goal(s): To address this using a neural network trained with radiologist labels and whole mount histology of Gleason ≥7 cases to predict pathological upgrading in our cohort.
Approach: DecNet was applied to the Gleason 6 initial MRIs to assess if the model could retrospectively identify patients with higher grade disease.
Results: Our model had a sensitivity of 84.6% for lesions upgraded to Gleason 7, outperforming PSA density, lesion size and ADC values.
Impact: These results showcase the potential of our model in unveiling higher-grade prostate cancer within lesions initially diagnosed as lower grade on pathology.
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