Automatic Detection in MRI of Prostate Cancer: DAICAP

NCT05513820 | Recruiting

• 1 other identifier: APHP201101
• Study Type: observational
• Target: 1,250
• Locations: 1 country
• Sites: 1

Brief Summary

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

Detection and Characterization of Prostate Cancer Based on Artificial Intelligence

Keywords

Prostate Cancer; PI-RADS; PSA; Artificial Intelligence; Multiparametric Magnetic Resonance Imaging

Outcome Measures

Primary Outcomes (1)

• Performance (sensitivity and specificity) of the algorithm to predict the standardized radiological PI-RADS

  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).

  Inclusion

Secondary Outcomes (1)

• Performance (sensitivity and specificity) of the algorithm in predicting tumor aggressiveness

  Inclusion

Study Arms (2)

Retrospective group

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.

Prospective group

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: male
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with clinical suspicion of prostate cancer

You may qualify if:

• 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.

You may not qualify if:

• Patients with histologically proven prostate cancer and/or treatment for prostate cancer prior to the diagnostic workup
• Prospective substudy
• 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.
• 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.

Sponsors & Collaborators

• Assistance Publique - Hôpitaux de Paris: lead
• University Hospital, Strasbourg, France: collaborator
• University Hospital, Bordeaux: collaborator
• University Hospital, Lille: collaborator
• The Civil Hospitals, Lyon: collaborator
• Institut National de Recherche en Informatique et en Automatique: collaborator
• INCEPTO: collaborator

Study Sites (1)

La Pitié Salpétrière Hospital

Paris, 75013, France

RECRUITING

MeSH Terms

Conditions

Prostatic Neoplasms

Condition Hierarchy (Ancestors)

Genital Neoplasms, Male; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Genital Diseases, Male; Genital Diseases; Urogenital Diseases; Prostatic Diseases; Male Urogenital Diseases

Central Study Contacts

Raphaële Renard-Penna, MD, PhD

CONTACT

01 42 17 82 25; Raphaele.renardpenna@aphp.fr

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: August 22, 2022
• First Posted: August 24, 2022
• Study Start: June 21, 2022
• Primary Completion: December 1, 2024
• Study Completion: December 1, 2024
• Last Updated: May 20, 2024

Record last verified: 2024-05

Locations

FR (1)