Abstract

One of the most common types of cancer in men is prostate cancer (PCa). Biopsies guided by bi-parametric magnetic resonance imaging (MRI) can aid PCa diagnosis. Previous works have mostly focused on either detection or classification of PCa from MRI. In this work, however, we present a neural network that simultaneously detects and grades cancer tissue in an end-to-end fashion. This is more clinically relevant than the classification goal of the ProstateX-2 challenge. We used the dataset of this challenge for training and testing. We use a 2D U-Net with MRI slices as input and lesion segmentation maps that encode the Gleason Grade Group (GGG), a measure for cancer aggressiveness, as output. We propose a method for encoding the GGG in the model target that takes advantage of the fact that the classes are ordinal. Furthermore, we evaluate methods for incorporating prostate zone segmentations as prior information, and ensembling techniques. The model scored a voxel-wise weighted kappa of 0.446 ± 0.082 and a Dice similarity coefficient for segmenting clinically significant cancer of 0.370 ± 0.046, obtained using 5-fold cross-validation. The lesion-wise weighted kappa on the ProstateX-2 challenge test set was 0.13 ± 0.27. We show that our proposed model target outperforms standard multiclass classification and multi-label ordinal regression. Additionally, we present a comparison of methods for further improvement of the model performance.

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