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Clinical Trial Summary

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.


Clinical Trial Description

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. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05024162
Study type Interventional
Source Nova Scotia Health Authority
Contact Beverly A Lieuwen, BSc
Phone 9024735315
Email [email protected]
Status Not yet recruiting
Phase N/A
Start date September 1, 2021
Completion date September 1, 2022

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