Clinical trial · Observational
Automatic Detection in MRI of Prostate Cancer: DAICAP
- 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)
Prostate cancer is the most common cancer in France and the 3rd most common cancer death in humans. The introduction of pre-biopsy MRI has considerably improved the quality of prostate cancer (PCa) diagnosis by increasing the detection of clinically significant PCa , and by reducing the number of unnecessary biopsies.However the diagnostic performance of Prostate MRI is highly dependent on reader experience that limits the population based delivery of high quality multiparametricMRI (mpMRI) driven PCa diagnosis. The main objective of this study is the development and the test of diagnostic accuracy of an AI algorithm for the detection of cancerous prostatic lesions from mpMRI images. The secondary objective is the development and the test of diagnostic accuracy of an AI algorithm to predict tumor aggressiveness from mpMRI images.
Conditions
Conditions (1)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Detection and Characterization of Prostate Cancer Based on Artificial Intelligence | — | UNRESOLVED | — |
Interventions
Interventions (0)
Data not yet available
Design
Arms and outcomes
Arms (2)
- label
- Retrospective group
- description
- Retrospective group: 700 patients from the databases of the AP-HP, the Lyon University Hospital and the Lille University Hospital for training and validation of the algorithms.
- label
- Prospective group
- description
- Prospective group: 550 patients (test-set) from AP-HP (CHU Pitié, Tenon, Bicêtre, Necker), CHU Lille, CHU Lyon, CHU Bordeaux and CHU Strasbourg to tes the performance of the algorithms.
Primary outcomes (1)
- measure
- Performance (sensitivity and specificity) of the algorithm to predict the standardized radiological PI-RADS
- timeFrame
- Inclusion
- description
- The primary endpoint will be the performance (sensitivity and specificity) of the algorithm to predict the standardized radiological PI-RADS score for each patient: presence of at least one lesion considered significant (internationally standardized score between 1 and 5 and with a threshold of positivity at 3 or more).
Secondary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- Male
- Minimum age
- 18 Years
Show eligibility criteria text
Retrospective substudy Inclusion Criteria: * Patients with clinical suspicion of prostate cancer (increased PSA and/or abnormality on digital rectal examination) who underwent a diagnostic workup including mpMRI and prostate biopsies according to national recommendations: in case of normal mpMRI (PI-RADS \< 3) 12 systematic samples; in case of pathological mpMRI (PI-RADS ≥3) 12 systematic samples associated with targeted samples (n= 2 to 4) by cognitive fusion, or image fusion software. Exclusion Criteria: * Patients with histologically proven prostate cancer and/or treatment for prostate cancer prior to the diagnostic workup Prospective substudy Inclusion Criteria: * Patients with clinical suspicion of prostate cancer (increased PSA level and/or abnormality on digital rectal examination) who should receive a diagnostic workup including mpMRI and prostate biopsies according to national recommendations: in case of normal mpMRI (PI-RADS \< 3) 12 systematic samples; in case of pathological mpMRI (PI-RADS ≥3) 12 systematic samples associated with targeted samples (n= 2 to 4) by cognitive fusion, or image fusion software. Exclusion Criteria: * Patients with already histologically proven cancer, patients who have received treatment for prostate cancer, patients who cannot benefit from prostate biopsies, or patients with a contraindication to performing mpMRI.
References
Publications (0)
Data not yet available