Clinical trial · Observational
Validation of ClarityDX Prostate as a Reflex Test to Refine the Prediction of Clinically-significant Prostate Cancer
Clinical Validation of ClarityDX Prostate as a Reflex Test to Prostate Specific Antigen (PSA) to Refine the Prediction of Clinically-significant Prostate Cancer
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
This ambispective observational cohort study will evaluate the clinical performance of the ClarityDX Prostate blood test in men with a prostate-specific antigen (PSA) level of 3 ng/mL or higher who are undergoing prostate biopsy due to suspicion of prostate cancer. The study will compare ClarityDX Prostate test results with biopsy outcomes to determine the test's ability to identify the presence of clinically significant prostate cancer.
Conditions
Conditions (3)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Performance Assessment of Novel Test | — | UNRESOLVED | — |
| Prostate Cancer | Malignant Prostate Neoplasm | CURATED_EXACT | 0.92 |
| Prostate Cancer Screening | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (3)
- measure
- Diagnostic Clinical Performance: prediction of clinically significant prostate cancer at biopsy
- timeFrame
- 3 years
- description
- Training Phase: Processed clinical features of each patient will be analyzed using machine learning to predict clinically significant prostate cancer, with the output being the ClarityDX Prostate Risk Score. Validation Phase: The models created during the Training Phase will be locked down and then used to determine the probability of approximately 1,400 patients in the investigational Validation Phase having clinically significant prostate cancer.
- measure
- Active Surveillance: prediction of Gleason Grade Group on confirmatory/follow-up biopsies for participants on Active Surveillance
- timeFrame
- 4 years
- measure
- MRI: prediction of PI-RADS pre-diagnostic biopsy
- timeFrame
- 4 years
Secondary outcomes (3)
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 40 Years
- Maximum age
- 75 Years
Show eligibility criteria text
Inclusion Criteria: 1. Males between 40-75 (inclusive) years of age; 2. With and without family history of prostate cancer; 3. No prior prostate cancer diagnosis and who are referred to have a prostate biopsy; 4. Total PSA results \>/= 3ng/mL collected within 6m of enrollment; 5. Willing to permit provincial agencies (e.g. Alberta Health Services, Alberta Health, Netcare, Service Alberta or other based on recruitment jurisdiction) to disclose health-related information to study; 6. Undergoing a diagnostic prostate biopsy; and 7. Provided informed consent to participate in the study. Exclusion Criteria: 1. Unwilling to participate in the study; 2. Unavailable for biopsy procedure in recruitment areas; 3. Not undergoing a prostate biopsy; 4. Prior diagnosis of cancer excluding non-melanoma skin cancer; and/or 5. Under the age of 40 years of age or over the age of 75 years of age.
References
Publications (2)
- RESULTPaproski RJ, Kinnaird A, Hyndman ME, Fairey A, Marks L, Pavlovich CP, Fletcher SA, Zachoval R, Adamcova V, Stejskal J, Aprikian A, Wallis CJD, Pink D, Vasquez C, Beatty PH, Lewis JD. Predicting clinically significant prostate cancer with or without digital rectal exam and MRI data using ClarityDX Prostate models. NPJ Digit Med. 2026 Apr 17;9(1):467. doi: 10.1038/s41746-026-02642-1. PMID 41998222
- RESULTHyndman ME, Paproski RJ, Kinnaird A, Fairey A, Marks L, Pavlovich CP, Fletcher SA, Zachoval R, Adamcova V, Stejskal J, Aprikian A, Wallis CJD, Pink D, Vasquez C, Beatty PH, Lewis JD. Development of an effective predictive screening tool for prostate cancer using the ClarityDX machine learning platform. NPJ Digit Med. 2024 Jun 20;7(1):163. doi: 10.1038/s41746-024-01167-9. PMID 38902526