Towards non-invasive Gleason grading of prostate cancer using diffusion weighted MRI
Abstract: Prostate cancer is one of the most common cancer diagnosis in men. This project aimed to help in characterization and treatment planning of prostate cancer by producing a Gleason grading probability based on apparent diffusion coefficient (ADC). In a study, from which this project received the patient data, the patients were first imaged using magnetic resonance imaging (MRI) in a 3T positron emission tomography MRI (PET/MRI) scanner. The prostates were surgically removed and placed in a patient specific mold. While inside the mold, the prostates were imaged using the same scanner, producing ex-vivo images of the prostates. Lastly the prostates were cut in histopathology slices and Gleason graded by a pathologist. To get correlation between ADC and Gleason grade all images needed to be correctly related to each other. This was done by three image registrations, which was the main part of this project. The histopathology slices were first registered to the ex-vivo images of the prostate, and then to the in-vivo T2-weighted images. The in-vivo T2w images were matched to images depicting the diffusion of water in the prostates, known as ADC-maps. The ADC-values were collected and matched to their possible Gleason grade. Information from 149 images were used, which came from 22 different patients. 3D pixels, known as voxels, with a corresponding Gleason grade annotation measured a lower average ADC-value. These voxels also showed more variation with a larger standard deviation. Furthermore, these voxels measured a larger range of ADC-values compared to voxels without a corresponding Gleason grade, but the probability of a Gleason grade was mainly seen for ADC-values below 1200 mm2/s. Filtering the ADC-map before collecting the information showed less spread in measurements, and larger total probability of Gleason grade annotation for lower ADC-values. To test the validity of the result a movement of the Gleason grade map was used to simulate registration errors. No large impact was observed for small movements but more obvious change for large. The results indicate this method as promising in predicting regions with a probability for Gleason grade of 3 or 4, however it was less accurate in separating the two. Gleason 5 showed very low probability, mainly as a result of the low sample size since only two patients had such tumors. Further research with better optimized filtering is recommended in the future.
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