Automatic Detection in MRI of Prostate Cancer: DAICAP

NCT ID: NCT05513820

Last Updated: 2024-05-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Total Enrollment

1250 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-06-21

Study Completion Date

2024-12-31

Brief Summary

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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.

Detailed Description

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This is a study combining :

1. Firstly a sub-study with a multicentric retrospective sample of 700 patients from the databases of AP-HP, CHU de Lyon and CHU de Lille for training and validation of algorithms. The historical depth may be up to 96 months (8 years)
2. A second sub-study with a multicentric prospective sample of 550 patients (test-set) associating AP-HP (CHU Pitié, Tenon, Bicêtre, Necker), CHU Lille, CHU Lyon, CHU Bordeaux and CHU Strasbourg to test the performance of algorithms Data will be collected retrospectively (training phase - validation of the algorithm) and prospectively (testing phase of the algorithm) from the medical records of each of the centres for patients corresponding to the inclusion and exclusion criteria mentioned above.

Methodology :

1. Retrospective phase mpMRI images chained to histological (prostate biopsy data), biological (PSA) and demographic (age) data will be used for supervised learning during the training and validation phases. Thus, the aggressiveness scores will rely on a matching between mpMRI images and the results of targeted biopsies in addition to standard biopsies
2. Prospective phase For the performance measurement, a test set of 550 prospectively collected images will be used, of which 150 will be from the same centers, and 400 from 3 other clinical centers (CHU Strasbourg, APHP Bicêtre and Necker-HEGP and CHU Bordeaux).

The algorithms developed in the retrospective phase will be applied by Inria to the prospective data, without knowledge of the PI-RADS score or the aggressiveness. The performance of each algorithm will then be evaluated, under the responsibility of an independent unit,by its sensitivity and specificity with their IC95%. The main analysis will be conducted by patient (presence of at least one lesion with a PI-RADS score ≥3; presence of at least one lesion considered aggressive (defined by the presence of a histological Gleason score grade 4 up to 6 months after the mpMRI). Secondary analyses will be conducted by lesion and by prostate lobe.

Conditions

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Detection and Characterization of Prostate Cancer Based on Artificial Intelligence

Study Design

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Observational Model Type

OTHER

Study Time Perspective

OTHER

Study Groups

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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.

No interventions assigned to this group

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.

No interventions assigned to this group

Eligibility Criteria

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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.


* 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 histologically proven prostate cancer and/or treatment for prostate cancer prior to the diagnostic workup

Prospective substudy


* 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.
Minimum Eligible Age

18 Years

Eligible Sex

MALE

Accepts Healthy Volunteers

No

Sponsors

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University Hospital, Strasbourg, France

OTHER

Sponsor Role collaborator

University Hospital, Bordeaux

OTHER

Sponsor Role collaborator

University Hospital, Lille

OTHER

Sponsor Role collaborator

The Civil Hospitals, Lyon

UNKNOWN

Sponsor Role collaborator

Institut National de Recherche en Informatique et en Automatique

OTHER

Sponsor Role collaborator

INCEPTO

UNKNOWN

Sponsor Role collaborator

Assistance Publique - Hôpitaux de Paris

OTHER

Sponsor Role lead

Responsible Party

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Responsibility Role SPONSOR

Locations

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La Pitié Salpétrière Hospital

Paris, , France

Site Status RECRUITING

Countries

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France

Central Contacts

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Raphaële Renard-Penna, MD, PhD

Role: CONTACT

01 42 17 82 25

Facility Contacts

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Sofia ZEMOURI

Role: primary

33 1 42 16 75 75

Other Identifiers

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APHP201101

Identifier Type: -

Identifier Source: org_study_id

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