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Sixty patients with a new diagnosis of prostate cancer that meet eligibility criteria. The group will have two standard MRI-P's completed. The first MRI-P will be acquired as standard of care and the second will be an additional investigation for the purposes of this study. The efficacy of the MRT will be compared at both time points, evaluating if the MRT demonstrates clinically sufficient stability in its findings (i.e., does the MRT report an accurate and similar result at both time points).

Predicted Grade Group (GG) by the MRI-based Radiomics Tool (MRT) at each Magnetic Resonance Imaging of the Prostate (MRI-P)

MRT Accuracy

MRT Stability

Beverly A Lieuwen, BSc

Dr. Michael Kucharczyk

Michael J Kucharczyk, MB BCh BAO MPH FRCPC

Beverly Lieuwen

Prostate cancer is the most common cancer diagnosed in men in Canada. Magnetic resonance imaging (MRI) may become a valuable tool to non-invasively identify prostate cancer and assess its biological aggressiveness, which in turn will help doctors make better decisions about how to treat an individual patient's prostate cancer.

Despite the promise of MRI for detecting and characterizing prostate cancer, there are several recognized limitations and challenges. These include lack of standardized interpretation and reporting of prostate MRI exams.

The investigators propose to validate and improve a computer program computerized prediction tool that will use information from MR images to inform us how aggressive a prostate cancer is. The hypothesis is that this computer-aided approach will increase the reproducibility and accuracy of MRI in predicting the tumor biology information about the imaged prostate cancer.

Prostate biopsies are the gold standard assessment of how prostate cancer is diagnosed and how low risk prostate cancers are surveilled. The investigators have produced a machine-learning based algorithm which uses MRI characteristics (radiomic features or textures) to predict the results of a prostate biopsy. The field has numerous concerns that such radiomic based predictions will not be reproducible, as there as so many subtle changes between MRI scans of different patients.

The interventions are the use of the MRT and the use of a second MRI of the prostate (MRI-P).

Two primary outcomes will be investigated. First, the existing radiomics predictive model, labeled as the MRI-P based Radiomics Tool (MRT) will predict the Grade Group (GG) and compare it to the gold standard, pathologist's evaluation of the Grade Group (GG). Second, the stability of the predicted GG between two shortly spaced MRI-Ps will be compared.

Patients with a detectable prostate nodule on MRI-P which localizes to a biopsy confirmed prostate cancer will be approached for enrollment. If enrolled, participants will attend for a subsequent MRI-P in a brief time frame relative to the acquisition of the first MRI-P. Attempts will be made to obtain participants that allow for even distribution among all GGs.

Can MRI of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour

Can Magnetic Resonance Imaging of the Prostate Combined With a Radiomics Evaluation Determine the Invasive Capacity of a Tumour (Can MRI-PREDICT)

Stability of participants' MRT classification (each of the five GG groups) between two shortly spaced MRIs.

The accuracy of the GG classification from the MRT. Will be compared to the Gold Standard - prostate biopsy results. The percentage of MRT classifications that show agreement between the two methods (i.e. Gold Standard and MRT) in terms of GG classification will be reported.

Gwet's first order agreement coefficient; McNemar's test to test agreement across the two time points, regarding GG classification agreement.

Intra-class correlation coefficient (ICC) will to test the reliability of individual radiomic features at time points 1 and 2. Stability will be defined as an ICC ≥0.85.

Ordinal logistic regression with a cumulative logic link will be used to model GG classification. Clinical covariates, PIRADS scores, and exclusively "reliable" radiomic features will be explored in secondary analyses.

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