PSMA-PET: Deep Radiomic Biomarkers of Progression and Response Prediction in Prostate Cancer

Study Overview
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Brief Summary
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Prostate cancer (PCa) is the most common non-skin malignancy and the third leading cause of cancer death in North American men. The accurately mapped metastatic state is a necessary prerequisite to guiding treatment in practice and in clinical trials. Imaging biomarkers (BMs) can provide information on disease volume and distribution, prognosis, changes in biologic behavior, therapy-induced changes (both responders and non-responders), durations of response, emergence of treatment resistance, and the host reaction to the therapies. Of particular relevance to metastatic prostate cancer is the emergence of a promising imaging technique involving new prostate specific membrane antigen (PSMA) positron emission tomography (PET) tracers. This approach has demonstrated higher sensitivity in detecting metastases, prior to and during therapy, than current imaging standard of care (CT and bone scan), and is not widely clinically available outside of the research realm in North America. Positron emission tomography / computer tomography (PET/CT) is a nuclear medicine diagnostic imaging procedure based on the measurement of positron emission from radiolabeled tracer molecules in vivo. PSMA is a homodimeric type II membrane metalloenzyme that functions as a glutamate carboxypeptidase/folate hydrolase and is overexpressed in PCa. PSMA is expressed in the vast majority of PCa tissue specimens and its degree of expression correlates with a number of important metrics of PCa tumor aggressiveness including Gleason score, propensity to metastasize and the development of castration resistance. [18F]DCFPyL is a promising high-sensitivity second generation PSMA-targeted urea-based PET probe. Studies employing second-generation PSMA PET/CT imaging in men with biochemical progression after definitive therapy suggest detection of metastases in over 60% of men imaged. Deep learning is defined as a variant of artificial neural networks, using multiple layers of 'neurons'. Deep learning has been investigated in medical imaging in numerous applications across organ systems. In oncology, basic artificial neural networks to support decision-making have previously been developed retrospectively in breast cancer and prostate cancer, but have not been validated or integrated prospectively. Novel data-driven methods are needed to predict outcomes as early as possible in order to guide the duration and the aggressiveness of a particular therapy. They are also needed for optimal patient selection based on the patient's response to a given therapy. Here the investigators hypothesize that the combination of a highly performing prostate cancer imaging technique combined with machine learning has high potential. The main objective of this study is to acquire PSMA-PET data in patients with prostate cancer who receive treatment and follow-up in order to enable the discovery of predictive imaging biomarkers through deep learning techniques.

Official Title
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PSMA-PET: Deep Radiomic Biomarkers of Progression and Response Prediction in Prostate Cancer

Conditions
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Prostate Cancer

Intervention / Treatment
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* Diagnostic Test: 18F-DCFPyL IV injection


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients with prostate cancer, being followed and treated at CHUM, whose treating physician at CHUM has requested a PSMA-PET scan. Exclusion Criteria: Claustrophobia/inability to complete imaging procedure.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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Male

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Diagnostic
Intervention Model: Single Group Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Main arm<br>PET-CT imaging following 18F-DCFPyL injection, 1 injection, IV, 10 mCi | Diagnostic Test: 18F-DCFPyL IV injection<br>* Patient will receive one injection of 18F-DCFPyL and undergo PET-CT imaging<br>|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Overall survival | Images from the 18F-DCFPyL PET-CT scans will be combined with patient follow-up data in a deep learning algorithm to discover radiomics features predicting outcomes (overall survival). | 10 years | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Progression free survival | Images from the 18F-DCFPyL PET-CT scans will be combined with patient follow-up data in a deep learning algorithm to discover radiomics features predicting outcomes (progression free survival). | 10 years |