Prostate cancer is the most common cancer in Sweden. Patients with the condition have a good prognosis in general and most cases can be treated. Localized prostate cancer is primarily treated via surgery or radiation therapy and is diagnosed with the help of different imaging modalities, such as magnetic resonance imaging, MRI, and positron emission tomography, PET. The diagnosis is confirmed and the aggressiveness of the cancer is determined through biopsies. Samples from a small part of the prostate are extracted and then examined. This could mean that parts of higher aggressiveness may be missed, which in turn could lead to under-treatment of the cancer. The aggressiveness of a lesion can be described by Gleason Score, GS, which is determined by an visual assessment of the shape, size and arrangement of the cells. The aim of this study was to correlate GS with in-vivo images using MRI and PET. This was accomplished by investigating image data from PSMA PET, Acetate PET, Ktrans MRI and T2-weighted MRI from a cohort of 26 prostate cancer patients containing 74 lesions. Regions of interests, ROI:s, were created and applied on all images. Statistics such as median and max value were extracted from each ROI. The statistics were combined to get a wide range of descriptive variables for each respective imaging modality. These were normalised against a certain zone of the prostate or only the absolute value. The results indicated that PSMA PET, Acetate PET and Ktrans MRI were correlated to GS, while T2-weighted MRI was not. Data also indicated that PSMA PET, Acetate PET and Ktrans MRI give complementary information to each other, which could indicate that a combination of the modalities would better predict GS. The implications of these findings could affect both the diagnostics and the treatment of prostate cancer.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-184885 |
Date | January 2021 |
Creators | Lindahl, Jens |
Publisher | Umeå universitet, Institutionen för fysik |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0016 seconds