A multimodal convolutional neural network for prostate cancer diagnosis using volumetric multiparametric MRI and clinical features.

Aim: To build a deep convolutional neural network (CNN) for prostate cancer diagnosis that utilises volumetric multiparametric MRI (mpMRI) and clinical features.
Motivation: Prostate cancer is the most common cancer amongst men in the UK, with incidence rates projected to rise by 12% in the period 2014-2035 [1]. 4-6 in 10 prostate cancer cases in the UK are diagnosed at a late stage [1]. By combining anatomical and functional information, studies like PROMIS ("Prostate MRI Imaging Study") [2] have shown that mpMRI of the prostate can radically improve prostate cancer diagnosis, non-invasively. Computer-aided diagnosis techniques can be used to obtain a fast, cost effective and reproducible diagnosis out of complex mpMRI data and other clinical data available [7]. In computer vision, deep CNNs are increasingly becoming the technique of choice for computer- aided diagnosis [4]. CNN architectures that can effectively combine mpMRI data is an understudied problem, as is combining imaging data with clinical data e.g. age, prostate specific antigen (PSA) level and gland volume. Furthermore, a CNN based approach would favour some of the applications of mpMRI highlighted in PROMIS. For example, a CNN that can assign a score to patients, between 0 (healthy) and 1 (unhealthy), may be used to triage patients according to the degree of abnormality reflected by the score.

Background / Previous Work: Computer-aided diagnosis of prostate cancer has been an active area of research over the past decade. In [5], a novel two-stage (voxel classification / candidate classification) computer-aided detection workflow is developed based on handcrafted features. Alternatively, CNN based approaches "learn" the most discriminative features rather than requiring feature engineering. A comprehensive survey of over 300 contributions of deep learning in medical imaging can be found in [4]. A recent state of the art contribution uses a co-trained CNN for automated detection of prostate cancer in 2D images: T2-weighted (T2w) images and apparent diffusion coefficient (ADC) maps [8].

Novelty: Most CNN approaches are only able to process 2D images whilst most medical data used in clinical practice consists of 3D volumes [6]. The main novelty of this project will be the use of volumetric T2w images, ADC maps and dynamic contrast enhanced (DCE) images. A further novelty will lie in the treatment of the DCE volumes. As these are collected over a series of timesteps, a voxel-wise statistical approach may be required to combine the DCE volumes time series into a single volume. Another novel aspect of this work will be an investigation of the effectiveness of combining imaging data with clinical data. A final novelty will be the application of our CNN output to triage, which to the best of our knowledge, is an area that is unexplored to date and can have a substantial clinical impact.