Machine learning-based quantitative image, tissue, and clinical data analysis for lesion detection and characterization on prostate cancer imaging

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Prostate cancer is often treated by whole-prostate radiation treatment or surgery, with side effects including erectile dysfunction and urinary incontinence. However, not all men need such invasive treatment; some prostate cancers are sufficiently slow-growing to never need treatment, and some are concentrated in one spot and treatment can be targeted to the tumours only. One of the major problems in prostate cancer is how to determine the right treatment for each man. This is currently done using a blood test and a needle biopsy. Because the blood test has limited accuracy and the small biopsy needles may miss tumours, physicians need to select treatments using incomplete information. To solve this problem, this research proposes three-dimensional (3D) magnetic resonance imaging (MRI) and positron emission tomography (PET) to completely map the prostate. A new form of PET imaging, called prostate specific membrane antigen (PSMA) PET imaging, is showing tremendous promise in its ability to help physicians detect cancer within the prostate. Although MRI and PSMA PET are the best imaging technologies for prostate imaging, seeing prostate tumours on MRI can still be extremely for the physician difficult because of the complexity of the images, and there are no current guidelines for physicians to follow in interpreting PSMA PET images of the prostate. We propose the development of an artificially intelligent computer system to help doctors to translate the complex images into a simple 3D cancer map that will enable guiding of biopsies to targets and choosing the right treatment for each patient. This system will be based on artificial neural networks, which are software programs that mimic aspects of the visual systems in human brains. A new type of artificial neural network with a "deep learning" architecture has shown tremendous promise in machine vision tasks for everyday objects; for instance, many mobile phone cameras use deep learning for face recognition. We will, for the first time, develop a deep learning-based artificial intelligence system that will integrate mpMRI and PSMA PET images with all of the clinical parameters (e.g. blood test results) that the physician would use to assist in cancer detection. In addition, we will form a more robust interface between the physician and the machine by eye tracking technology to get a window into the physician's mind. Eye gaze locations suggest regions of suspicion or uncertainty, and the deep learning system will exploit these eye gaze data to sharpen its assessment of image regions where the physician's attention dwells. This will result in a hybrid human-machine vision system drawing on the combined strengths of human and artificial intelligence to finally develop an inexpensive software tool that will avoid overtreatment of prostate cancer in men for whom this would compromise quality life with no added benefit, and detect aggressive prostate cancer early while it is still curable.