Detection and Characterization of Prostate Cancer Based on Artificial IntelligenceDAICAPNCT05513820
Automatic Detection in MRI of Prostate Cancer: DAICAP
An observational study of Detection and Characterization of Prostate Cancer Based on Artificial Intelligence, run by Assistance Publique - Hôpitaux de Paris at 1 site in France.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 years and older, men only
- Conditions the study is for
- Detection and Characterization of Prostate Cancer Based on Artificial Intelligence
- How many people
- 1,250 participants
- Where
- 1 siteFrance
- Study dates
- 21 Jun 2022to Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- Men only
- No healthy volunteers
- Adult, Older adult
Study population: Patients with clinical suspicion of prostate cancer
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
France1 site, 1 recruiting
- Recruiting
La Pitié Salpétrière Hospital
Paris, 75013
Sofia ZEMOURI, 33 1 42 16 75 75, sofia.zemouri@aphp.fr
About this study
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.
Read the detailed description
This is a study combining :
- 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)
- 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 :
- 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
- 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.
- Keywords
- Prostate Cancer, PI-RADS, PSA, Artificial Intelligence, Multiparametric Magnetic Resonance Imaging
- Medical subject headings
- Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: Inclusion
Secondary outcomes (1)
Performance (sensitivity and specificity) of the algorithm in predicting tumor aggressiveness
The secondary endpoint will be the performance (sensitivity and specificity) of the algorithm in predicting tumor aggressiveness.Gold standard is defined, for each patient, on the histological analysis of the first series of biopsy samples taken in the course of care,up to 6 months from the mpMRI, as the presence of at least one lesion with grade 4 cells according to the characterization by the international histoprognostic score ISUP.Patients without biopsy in the 6 months will be considered as having non aggressive tumor.
Measured: Inclusion
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Aug 2022
- Study start21 Jun 2022
- Primary completionDec 2024 (estimated)
- Study completionDec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Detection and Characterization of Prostate Cancer Based on Artificial IntelligenceStudies of the same condition, open and under way.
- More trials run by Assistance Publique - Hôpitaux de ParisThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Other
- Time perspective
- Other
- Enrolment
- 1,250 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Assistance Publique - Hôpitaux de Paris
- Sponsor type
- University, hospital or other
- Collaborators
- University Hospital, Strasbourg, France, University Hospital, Bordeaux, University Hospital, Lille, The Civil Hospitals, Lyon, Institut National de Recherche en Informatique et en Automatique, INCEPTO
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05513820
- Sponsor's protocol ID
- APHP201101
- Acronym
- DAICAP
- Status last verified
- May 2024
- First submitted
- 22 Aug 2022
- Last updated
- 20 May 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 20 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Automatic Detection in MRI of Prostate Cancer: DAICAP
- ClinicalTrials.gov ID
- NCT05513820
- Acronym / sponsor ID
- DAICAP / APHP201101
- Keywords
- Prostate Cancer, PI-RADS, PSA, Artificial Intelligence, Multiparametric Magnetic Resonance Imaging
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 1,250 participants (estimated)
- Lead sponsor
- Assistance Publique - Hôpitaux de ParisUniversity, hospital or other
- Collaborators
- University Hospital, Strasbourg, France, University Hospital, Bordeaux, University Hospital, Lille, The Civil Hospitals, Lyon, Institut National de Recherche en Informatique et en Automatique, INCEPTO
- Study start
- 21 Jun 2022 (actual)
- Primary completion
- Dec 2024 (estimated)
- Study completion
- Dec 2024 (estimated)
- First posted
- 24 Aug 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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
This is a study combining :
- 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)
- 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 :
- 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
- 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.
Study design
- Observational model
- Other
- Time perspective
- Other
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with clinical suspicion of prostate cancer
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Performance (sensitivity and specificity) of the algorithm to predict the standardized radiological PI-RADS
Time frame: Inclusion
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).
1 secondary outcome
Performance (sensitivity and specificity) of the algorithm in predicting tumor aggressiveness
Time frame: Inclusion
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 May 2024.
- Individual participant dataSharing planned: Undecided
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Detection and Characterization of Prostate Cancer Based on Artificial Intelligence, run by Assistance Publique - Hôpitaux de Paris at 1 site in France.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 20 May 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in France. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Raphaële Renard-Penna, MD, PhD (01 42 17 82 25, Raphaele.renardpenna@aphp.fr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance (sensitivity and specificity) of the algorithm to predict the standardized radiological PI-RADS (time frame: Inclusion). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 20 May 2024.