Prostate cancer detection using lasers, machine learning and multiparametric MRI

Abstract

Worldwide, prostate cancer is the second most prevalent cancer among men. Currently, invasive prostate biopsies are the only method of diagnosing prostate cancer. To provide a better and less invasive diagnostic method, a Raman Spectroscopy probe has recently been developed at the University of Auckland. The probe has shown its ability to differentiate between malignant and benign prostate tissue and identify the Gleason Grade of the tumour. Recent literature has shown artificial intelligence (AI) based models have promise for automatically segmenting prostate tumours. This thesis aimed to apply AI-based models to identify tumours on multiparametric magnetic resonance imaging (mpMRI) to help guide where the Raman Spectroscopy probe should acquire measurements within the prostate. To achieve this, three methods were applied to segment tumours. These methods utilised T2-weighted (T2W), apparent diffusion coefficient (ADC) maps from diffusion weighted imaging (DWI), and a high b-value DWI image from three different prostate MRI datasets. The first method involved training an open-source deep learning model, nnU-Net. An iterative methodology was applied to train four different nnU-Net models, differing in their inputs and outputs. After the iterative development process, the best nnU-Net model achieved a dice similarity coefficient, sensitivity, and specificity of 0.552, 0.509, and 0.997, respectively. The second method involved running a pre-trained convolutional neural network. This model achieved a dice similarity coefficient, sensitivity, and specificity of 0.176, 0.818, and 0.801, respectively. The final method involved thresholding the ADC maps. This model achieved a dice similarity coefficient, sensitivity, and specificity of 0.372, 0.449, and 0.987, respectively. Therefore, the best tumour segmentation method investigated in this thesis was the nnU-Net model. In addition to segmenting the tumour, a machine learning model was developed to determine if a region where a biopsy was completed was likely to contain tumour. To complete this, a random forest classifier was trained with mpMRI radiomic features that were extracted from biopsy regions. This classifier achieved an accuracy, sensitivity, and specificity of 0.664, 0.652, and 0.667 respectively. Both the classifier and tumour segmentation methods provide an important first step towards developing AI-based models to help guide the Raman Spectroscopy probe.