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The current risk assessment of prostate cancer (PC) relies on histopathological samples from biopsies and clinical variables such as prostate-specific antigen (PSA). However, this comes with uncertainties and in some cases it can be challenging to separate patients who would benefit from radical treatment and those who would not. The risk assessment tools for PC need to be improved and preferably developed into predictive markers. Medical imaging using positron emission tomography (PET) and magnetic resonance imaging (MRI) are potential diagnostic modalities for achieving such improvements. Both PET and MRI have several clinical applications in PC already and are increasingly being incorporated at different steps in the clinical management. For example, MRI is used to guide targeted biopsies, and also as a guide during planning of external beam radiotherapy treatments with focal boosting of the macroscopic visible tumour. However, more precise and individual treatment strategies demand verification of both the characterisation regarding aggressiveness and spatial distribution of the disease. 

To evaluate the performance of PET and MRI in detection of biochemical recurrent PC after radical prostatectomy, a systematic literature review was conducted (study I). The results of this systematic review indicated that there is a large variety of available imaging methods for PC being used for detecting local and/or locoregional recurrence. Many of the included studies were based on evaluation of patients with high PSA levels yielding high sensitivities and specificities. A pooled mean sensitivity was calculated to 84% for multiparametric MRI (mpMRI) and Choline-PET/CT. Methodological variations between and within studies were observed which limited the possibility of performing a meaningful meta-analysis. No publications evaluating radiotracers binding to prostate-specific membrane antigen (PSMA) were included in the review, although the early literature of using PSMA-PET showed much promise. 

To introduce a PSMA-binding radiotracer to the clinical management of PC at Umeå University Hospital a clinical trial was performed with the aim to investigate the clinical performance of the radiotracer [⁶⁸Ga]PSMA-11. In this clinical trial we aimed to both evaluate the diagnostic performance and the safety of the radiotracer. To evaluate the safety, regarding radiation-exposure, absorbed organ doses as well as the effective dose were calculated in a cohort of six low-risk PC patients (study II). The results showed that the effective dose for [⁶⁸Ga]PSMA-11 was 0.022 mSv/MBq, and that the kidneys and lacrimal glands were the organs receiving the highest organ doses. Based on these results, which were in line with other clinically used radiotracers, we could conclude that [⁶⁸Ga]PSMA-11 is, from a radiation dosimetry perspective, a safe radiotracer to inject into patients. 

The diagnostic performance, specifically regarding detection of intraprostatic tumours using [⁶⁸Ga]PSMA-11 (PSMA)-PET, mpMRI and [11C]Acetate (ACE)-PET was evaluated in a cohort of 55 intermediate and high-risk PC patients planned for radical prostatectomy with the whole mount histopathology as the reference test (study IV). The imaging modalities were radiologically reviewed and compared. Sensitivity regarding detection of intraprostatic lesions was calculated for each imaging modality. Regarding detection of lesions with a volume >0.5 cc and with a ISUP grade ≥2, PSMA-PET and mpMRI showed similar performance with sensitivities of 69% and 73%, respectively while ACE-PET had a sensitivity of 36%. 

In this clinical study, a registration procedure between histopathology and in vivo images was developed and performed in all patients. This procedure included both a 3D printed patient-specific prostate-mould, an ex vivo MRI of the specimen and image registrations (study III). The uncertainty of the precision of the registration between histopathology data and in vivo data was evaluated by comparing positions of landmarks visible in the corresponding images. The uncertainty of the method was estimated to a median in-plane error of 1.7 mm [interquartile range: 1.0, 2.5] for the entire registration procedure. 

To conclude, the tools for risk assessment of PC need to be improved and developed into predictive markers. When in vivo data is correlated with histopathology data, such as the data set collected within this thesis, it is possible to identify new predictive markers that can be used to improve the clinical management of PC. 

Background: Medical imaging is increasingly used to inform on the clinical decision-making in prostate cancer (PCa). However, the ways in which tumour pathology is reflected in imaging remains poorly understood. The aim of this thesis was to provide insights into the associations between image characteristics and histopathological features that can be leveraged for improving radiotherapy. 

Methods: A pipeline of registration algorithms were developed to align a gold standard histopathological reference and in vivo imaging. We investigated the ability of image summary measures to discriminate between histological grades of PCa, and examined how detectability varied across lesions characterized by grades and by combined markers of cellular proliferation and differentiation. Finally, we conducted an in silico evaluation of a radiotherapy treatment protocol, based on the ongoing HYPO-RT-PC-boost phase II trial (NCT06220435). 

Results: The registration pipeline provided the means to investigate associations between imaging characteristics and histopathological features. We demonstrated that image measures derived from in vivo imaging can distinguish between lower- and higher-grade PCa, using partially discriminative cut-off values. Further, we showed that many detected lesions were both high-grade and had a higher-risk profile, characterized by high proliferation and low differentiation. Undetected lesions were more often lower-grade, but did not predominantly exhibit the low-risk combination of low proliferation and high differentiation. Furthermore, we showed that image summary measures can distinguish between higher- and lower-risk lesions, suggesting further prognostic potential of the imaging modalities. By incorporating multiple observer delineations of the visible tumour, the results of the in silico evaluation indicate that radiation oncologists delineate different tumour volumes, but they could all still obtain good coverage in sites containing more aggressive disease. These results provide a rationale for prioritizing sensitive structures during treatment planning.

Conclusion: Medical imaging modalities hold untapped potential to inform the clinical decision making. However, the inability to identify tumour on medical imaging does not necessarily translate to inadequate dose coverage in radiotherapy. The inherent complexity of generating large-scale datasets with co-registered imaging and histopathology limits generalizability and underscores the importance of interstudy harmonization in imaging protocols and histopathological evaluation.