Prostate CancerNCT06773598
Validation of a Machine Learning Model Based on MR for the Prediction of Prostate Cancer
An observational study of Prostate Cancer, run by IRCCS Azienda Ospedaliero-Universitaria di Bologna at 1 site in Italy.
Trial summary
The goal of this observational study is to validate a clinically significant predictive machine learning model based on the processing of images RMmp (Multiparametric Magnetic Resonance Imaging). Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Prostate Cancer
- How many people
- 1,100 participants
- Where
- 1 siteItaly
- Study dates
- 21 May 2024to Nov 2028
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: The study will include participants who have performed an MMR at the U.O.C. Radiology Addomino-Pelvic Diagnostic and Interventional of the IRCCS University Hospital of Bologna, Sant'Orsola Polyclinic and a TRUS biopsy with fusion technique with RMmp of the prostate. The retrospective enrollment refers to the period between 15 September 2015 (date start of TRUS biopsy with fusion technique with RMmp at the Policlinico di Sant'Orsola) and the date of approval of this study. The prospective enrollment refers to the period of time from the study approval until the following 3 years.
Who may be able to join
- Participants aged 18 at the time of examination
- Obtaining informed consent
- Presence of one or more lesions classified as PI-RADSv2.1 ≥ 1 at a prostate RMmp at the IRCCS Azienda Ospedaliero-Universitaria in Bologna
- Indication for TRUS biopsy by fusion technique integrated with systematic biopsy at the IRCCS Azienda Ospedaliero-Universitaria in Bologna
Who cannot join
- Previous prostate surgery or hormone therapy
- Technically sub-optimal investigations for the presence of artifacts (hip prosthesis, movement of the endorectal probe, etc.)
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Italy1 site, 1 recruiting
- Recruiting
IRCCS Azienda Ospedaliero-Universitaria di Bologna
Bologna, 40138
Caterina Gaudiano, MD, +39 0512142307, caterina.gaudiano@aosp.bo.it
About this study
Official title: Validation of a Machine Learning Model Based on Multiparametric MR for the Prediction of Clinically Significant Prostate Cancer
The goal of this observational study is to validate a clinically significant predictive machine learning model based on the processing of images RMmp (Multiparametric Magnetic Resonance Imaging). To be validated the model should be evaluated on:
- Specificity (SP): is the probability of a negative test result, conditioned on the individual truly being negative
- Sensitivity (SN): is the probability of a positive test result, conditioned on the individual truly being positive
Read the detailed description
This is a transversal, non-interventional observation study that involves the analysis of data collected both retrospectively and prospectively.
Patients will be enrolled by the radiologist medical staff designated by the Principal Investigator. The Principal Investigator and his delegates will be responsible for the acquisition and pseudo-anonymization of images of patients enrolled (both retrospectively and prospectively) from the RIS-PACS of the IRCCS Bologna University Hospital, Policlinico di Sant'Orsola. When available, for the participants undergoing prostate surgery, the O.U. of Pathological Anatomy will provide digital scans of the prostatic macrosections.
For the validation of the machine learning model of clinically significant CaP, a tool will be made available to radiologists. This tool is developed by the Department of Informatics - Science and Engineering, DISI, University of Bologna and Alma Mater Research Institute on Global Challenges and Climate Change, Alma Climate, University of Bologna, and integrated in an open-source Dicom viewer, Aliza MS Dicom Viewer. The doctors will then have the possibility to carry out independently the segmentation of the ADC sequences (previously pseudo-anonymized) and start the radiomic process to obtain a predictive value of the probability that the segmented lesion is clinically significant. This probability value will be calculated by the software on the basis of the machine learning model developed in PROSTATE_01. The tool also allows data collection (organized and pseudo-anonymous). The validation of the clinically significant predictive model of CaP will be carried out on all patients enrolled both retrospectively and prospectively, excluding those used in the model training phase in PROSTATE_01. After having evaluated the performance of the validated model and the effects of selection bias, once the recruitment is completed both retrospectively and prospectively, a refinement of the previously developed model will be carried out. This will be a "re-training" of the model itself, performed on training and test datasets randomly selected from the entire enrolled population from which it will be obtained randomly, by difference, also the subset for a new validation.
- Conditions
- Prostate Cancer
- Keywords
- Radiomics, mpMRI
- Medical subject headings
- Prostatic Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
Specificity (SP)
Specificity is the probability of obtaining a negative classification or that the disease is indeed absent.
Measured: From enrollment to the end of treatment at 5 years
Sensitivity (SN)
Sensitivity is the probability of a positive classification or that the disease is actually present.
Measured: From enrollment to the end of treatment at 5 years
Positive Predictive Value (PPV)
It is the ratio of patients truly diagnosed as positive to all those who had positive test results (including healthy subjects who were incorrectly diagnosed as patient).
Measured: From enrollment to the end of treatment at 5 years
Negative Predictive Value (NPV)
It is the ratio of subjects truly diagnosed as negative to all those who had negative test results (including patients who were incorrectly diagnosed as healthy).
Measured: From enrollment to the end of treatment at 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jan 2025
- Study start21 May 2024
- Primary completionOct 2028 (estimated)
- Study completionNov 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Prostate CancerStudies of the same condition, open and under way.
- 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
- Cohort
- Time perspective
- Other
- Enrolment
- 1,100 participants (estimated)
Sponsor and investigators
- Lead sponsor
- IRCCS Azienda Ospedaliero-Universitaria di Bologna
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Caterina Gaudiano, MD, Principal investigator, IRCCS Azienda Ospedaliero-Universitaria di Bologna
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06773598
- Sponsor's protocol ID
- PROSTATE_02
- Status last verified
- Nov 2024
- First submitted
- 9 Jan 2025
- Last updated
- 14 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of a Machine Learning Model Based on Multiparametric MR for the Prediction of Clinically Significant Prostate Cancer
- ClinicalTrials.gov ID
- NCT06773598
- Acronym / sponsor ID
- PROSTATE_02
- Conditions
- Prostate Cancer
- Keywords
- Radiomics, mpMRI
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,100 participants (estimated)
- Lead sponsor
- IRCCS Azienda Ospedaliero-Universitaria di BolognaUniversity, hospital or other
- Study start
- 21 May 2024 (actual)
- Primary completion
- Oct 2028 (estimated)
- Study completion
- Nov 2028 (estimated)
- First posted
- 14 Jan 2025 (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.
The goal of this observational study is to validate a clinically significant predictive machine learning model based on the processing of images RMmp (Multiparametric Magnetic Resonance Imaging). To be validated the model should be evaluated on:
- Specificity (SP): is the probability of a negative test result, conditioned on the individual truly being negative
- Sensitivity (SN): is the probability of a positive test result, conditioned on the individual truly being positive
Detailed description
This is a transversal, non-interventional observation study that involves the analysis of data collected both retrospectively and prospectively.
Patients will be enrolled by the radiologist medical staff designated by the Principal Investigator. The Principal Investigator and his delegates will be responsible for the acquisition and pseudo-anonymization of images of patients enrolled (both retrospectively and prospectively) from the RIS-PACS of the IRCCS Bologna University Hospital, Policlinico di Sant'Orsola. When available, for the participants undergoing prostate surgery, the O.U. of Pathological Anatomy will provide digital scans of the prostatic macrosections.
For the validation of the machine learning model of clinically significant CaP, a tool will be made available to radiologists. This tool is developed by the Department of Informatics - Science and Engineering, DISI, University of Bologna and Alma Mater Research Institute on Global Challenges and Climate Change, Alma Climate, University of Bologna, and integrated in an open-source Dicom viewer, Aliza MS Dicom Viewer. The doctors will then have the possibility to carry out independently the segmentation of the ADC sequences (previously pseudo-anonymized) and start the radiomic process to obtain a predictive value of the probability that the segmented lesion is clinically significant. This probability value will be calculated by the software on the basis of the machine learning model developed in PROSTATE_01. The tool also allows data collection (organized and pseudo-anonymous). The validation of the clinically significant predictive model of CaP will be carried out on all patients enrolled both retrospectively and prospectively, excluding those used in the model training phase in PROSTATE_01. After having evaluated the performance of the validated model and the effects of selection bias, once the recruitment is completed both retrospectively and prospectively, a refinement of the previously developed model will be carried out. This will be a "re-training" of the model itself, performed on training and test datasets randomly selected from the entire enrolled population from which it will be obtained randomly, by difference, also the subset for a new validation.
Study design
- Observational model
- Cohort
- Time perspective
- Other
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The study will include participants who have performed an MMR at the U.O.C. Radiology Addomino-Pelvic Diagnostic and Interventional of the IRCCS University Hospital of Bologna, Sant'Orsola Polyclinic and a TRUS biopsy with fusion technique with RMmp of the prostate. The retrospective enrollment refers to the period between 15 September 2015 (date start of TRUS biopsy with fusion technique with RMmp at the Policlinico di Sant'Orsola) and the date of approval of this study. The prospective enrollment refers to the period of time from the study approval until the following 3 years.
Key inclusion criteria (4 listed)
- Participants aged 18 at the time of examination
- Obtaining informed consent
- Presence of one or more lesions classified as PI-RADSv2.1 ≥ 1 at a prostate RMmp at the IRCCS Azienda Ospedaliero-Universitaria in Bologna
- Indication for TRUS biopsy by fusion technique integrated with systematic biopsy at the IRCCS Azienda Ospedaliero-Universitaria in Bologna
Key exclusion criteria (2 listed)
- Previous prostate surgery or hormone therapy
- Technically sub-optimal investigations for the presence of artifacts (hip prosthesis, movement of the endorectal probe, etc.)
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
Specificity (SP)
Time frame: From enrollment to the end of treatment at 5 years
Specificity is the probability of obtaining a negative classification or that the disease is indeed absent.
Sensitivity (SN)
Time frame: From enrollment to the end of treatment at 5 years
Sensitivity is the probability of a positive classification or that the disease is actually present.
Positive Predictive Value (PPV)
Time frame: From enrollment to the end of treatment at 5 years
It is the ratio of patients truly diagnosed as positive to all those who had positive test results (including healthy subjects who were incorrectly diagnosed as patient).
Negative Predictive Value (NPV)
Time frame: From enrollment to the end of treatment at 5 years
It is the ratio of subjects truly diagnosed as negative to all those who had negative test results (including patients who were incorrectly diagnosed as healthy).
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 14 Jan 2025.
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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.
Caterina Gaudiano, MD principal investigator
IRCCS Azienda Ospedaliero-Universitaria di Bologna
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 Prostate Cancer, run by IRCCS Azienda Ospedaliero-Universitaria di Bologna at 1 site in Italy.
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 4 inclusion and 2 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: looking for participants now, as of the sponsor's last update on 14 Jan 2025. 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 Italy. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Caterina Gaudiano, MD (+39 0515142307, caterina.gaudiano@aosp.bo.it), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Specificity (SP) (time frame: From enrollment to the end of treatment at 5 years). The study lists 4 primary outcomes in all. 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 14 Jan 2025.