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

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.

Study Overview

• Status: Recruiting
• Conditions: Prostate Cancer
• Intervention / Treatment: Diagnostic test: 18F-DCFPyL IV injection

• Study Type: Interventional
• Enrollment (Estimated): 1000
• Phase: Phase 3

Contacts and Locations

• Study Contact: Name: Daniel Juneau, MD; Phone Number: 1-514-890-8180; Email: daniel.juneau@umontreal.ca

Study Locations

• Canada
  • Quebec
    • Montreal, Quebec, Canada, H2X 0C1
      • Recruiting
      • Centre Hospitalier de l'Université de Montréal

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (Adult, Older Adult)
• Accepts Healthy Volunteers: No

Description

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.

Study Plan

How is the study designed?

Design Details

• Primary Purpose: Diagnostic
• Allocation: N/A
• Interventional Model: Single Group Assignment
• Masking: None (Open Label)

Number of Arms

1

Arms and Interventions

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

What is the study measuring?

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

Collaborators and Investigators

• Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
• Investigators: Principal Investigator: Daniel Juneau, MD, Centre Hospitalier de l'Universite de Montreal (CHUM)

Study record dates

Study Major Dates

• Study Start (Actual): December 1, 2018
• Primary Completion (Estimated): December 1, 2028
• Study Completion (Estimated): December 1, 2029

Study Registration Dates

• First Submitted: July 9, 2018
• First Submitted That Met QC Criteria: July 19, 2018
• First Posted (Actual): July 20, 2018

Study Record Updates

• Last Update Posted (Actual): March 19, 2026
• Last Update Submitted That Met QC Criteria: March 17, 2026
• Last Verified: March 1, 2026

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Urogenital Diseases; Genital Diseases; Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Prostatic Diseases; Male Urogenital Diseases; Prostatic Neoplasms
• Other Study ID Numbers: 18.068

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No