PSMA-PET: Deep Radiomic Biomarkers of Progression and Response Prediction in Prostate Cancer
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|ClinicalTrials.gov Identifier: NCT03594760|
Recruitment Status : Recruiting
First Posted : July 20, 2018
Last Update Posted : January 31, 2019
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.
|Condition or disease||Intervention/treatment||Phase|
|Prostate Cancer||Diagnostic Test: 18F-DCFPyL IV injection||Phase 3|
|Study Type :||Interventional (Clinical Trial)|
|Estimated Enrollment :||1000 participants|
|Intervention Model:||Single Group Assignment|
|Masking:||None (Open Label)|
|Official Title:||PSMA-PET: Deep Radiomic Biomarkers of Progression and Response Prediction in Prostate Cancer|
|Actual Study Start Date :||December 1, 2018|
|Estimated Primary Completion Date :||December 2023|
|Estimated Study Completion Date :||December 2024|
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
- Overall survival [ Time Frame: 5 years ]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).
- Progression free survival [ Time Frame: 5 years ]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).
To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.
Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03594760
|Contact: Daniel Juneau, MDemail@example.com|
|Centre Hospitalier de l'université de Montréal||Recruiting|
|Montréal, Quebec, Canada, H2X 0C1|
|Principal Investigator:||Daniel Juneau, MD||Centre hospitalier de l'Université de Montréal (CHUM)|