Prostate CancerNCT06116344
Improving Prostate Lesion Classification and Development of a PI-RADS 3 Classifier
An observational study of Prostate Cancer, run by Paracelsus Medical University at 1 site in Germany.
Trial summary
The investigators propose an AI methodology combining machine learning, histological results and expert image interpretation for the development of a PI-RADS 3 classifier. Read more about this study
- Who is it for?
- Ages 18 to 90 years, men only
- Conditions the study is for
- Prostate Cancer
- How many people
- 173 participants
- Where
- 1 siteGermany
- Study dates
- 1 Jan 2018to 24 Aug 2023
- 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 to 90 years
- Men only
- No healthy volunteers
- Adult, Older adult
Study population: the study population will be patients of a primary care clinic
Who may be able to join
- Only patients with a clinical indication for mp prostate MRI will be included in this prospective study.
- No allergies to GBCA
Who cannot join
- 1. Contraindications for MRI
Trial locations and contacts
1 site in 1 country.
Germany1 site
Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany
Nuremberg
About this study
Official title: Improving Prostate Lesion Classification and Diagnostic Accuracy Using Machine Learning: A Comprehensive Evaluation and Development of a PI-RADS 3 Classifier
The investigators propose an AI methodology combining machine learning, histological results and expert image interpretation for the development of a PI-RADS 3 classifier.
Read the detailed description
Prostate cancer is the most common carcinoma in male patients in Western industrialized countries. Multiparametric prostate MRI (mpMRI) can select patients who may be potential candidates for biopsy. In this study, the investigators present a comprehensive methodology that evaluates a multitude of AI algorithms and assesses their performance on a large and high-quality dataset, aiming to generate an efficient model and develop a PI-RADS 3 classifier. By combining the power of machine learning with the information provided by mpMRI, histopathological results as well as expert image interpretation, the investigators attempt to improve the diagnostic accuracy, which in the future my lead to more informed clinical decisions and reduce unnecessary biopsies.
- Conditions
- Prostate Cancer
- Keywords
- prostate cancer, artificial intelligence
- 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 |
|---|---|---|
| experimental | experimental: patients with a condition | |
| control group | control group: patients without condition |
What is being measured
The primary outcome is the main question the study is designed to answer.
Normalized Quantitative Signal - Intensity - Measurements with Region of Interest drawn in specific T2-weighted axial MRI Images
Regions of interest for quantitative signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Image analysis will be performed on a PACS workstation. Signal intensity will be measured and normalized, therefore no units needed.
Measured: through study completion, an average of 3 years
Quantitative Signal - Intensity - Measurements with Region of Interest in specific in Apparent diffusion coefficient (ADC) axial MRI Images
Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized in mm2/s
Measured: through study completion, an average of 3 years
Quantitative Signal - Intensity - Measurements with Region of Interest in specific in high b-value (800, 1500, 4000) axial MRI Images
Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized in mm2/s
Measured: through study completion, an average of 3 years
Signal - Intensity - Measurements with Region of Interest in specific dynamic contrast enhanced (DCE) MRI Images
Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized. Image analysis will be performed on a PACS workstation. The original Time inteisity curves are transformed in relative enhancement curves. Thus, they are normalized with respect to first point in time and represent the percentage increase compared to the time before contrast arrival, no units needed.
Measured: through study completion, an average of 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted3 Nov 2023
- Study start1 Jan 2018
- Primary completion31 Dec 2020
- Study completion24 Aug 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- 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
- Case-control
- Time perspective
- Retrospective
- Enrolment
- 173 participants (actual)
Sponsor and investigators
- Lead sponsor
- Paracelsus Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Dr. Panagiota Manava, Paracelsus Medical University
- Investigators
- Michael M. Lell, Prof. Dr. med., Study director, Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06116344
- Sponsor's protocol ID
- AI_Prostate_1_KNN
- Status last verified
- Nov 2023
- First submitted
- 24 Aug 2023
- Last updated
- 3 Nov 2023
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, Margolis D, Schnall MD, Shtern F, Tempany CM, Thoeny HC, Verma S. PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. Eur Urol. 2016 Jan;69(1):16-40. doi: 10.1016/j.eururo.2015.08.052. Epub 2015 Oct 1.
PubMed 26427566Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, Tempany CM, Choyke PL, Cornud F, Margolis DJ, Thoeny HC, Verma S, Barentsz J, Weinreb JC. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol. 2019 Sep;76(3):340-351. doi: 10.1016/j.eururo.2019.02.033. Epub 2019 Mar 18.
PubMed 30898406Morash C. What do you do with PI-RADS-3? Can Urol Assoc J. 2021 Apr;15(4):122. doi: 10.5489/cuaj.7262. No abstract available.
PubMed 33830009
Study at a glance
From the registry record, last updated by the sponsor on 3 Nov 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Improving Prostate Lesion Classification and Diagnostic Accuracy Using Machine Learning: A Comprehensive Evaluation and Development of a PI-RADS 3 Classifier
- ClinicalTrials.gov ID
- NCT06116344
- Acronym / sponsor ID
- AI_Prostate_1_KNN
- Conditions
- Prostate Cancer
- Keywords
- prostate cancer, artificial intelligence
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 173 participants (actual)
- Lead sponsor
- Paracelsus Medical UniversityUniversity, hospital or other
- Study start
- 1 Jan 2018 (actual)
- Primary completion
- 31 Dec 2020 (actual)
- Study completion
- 24 Aug 2023 (actual)
- First posted
- 3 Nov 2023 (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 investigators propose an AI methodology combining machine learning, histological results and expert image interpretation for the development of a PI-RADS 3 classifier.
Detailed description
Prostate cancer is the most common carcinoma in male patients in Western industrialized countries. Multiparametric prostate MRI (mpMRI) can select patients who may be potential candidates for biopsy. In this study, the investigators present a comprehensive methodology that evaluates a multitude of AI algorithms and assesses their performance on a large and high-quality dataset, aiming to generate an efficient model and develop a PI-RADS 3 classifier. By combining the power of machine learning with the information provided by mpMRI, histopathological results as well as expert image interpretation, the investigators attempt to improve the diagnostic accuracy, which in the future my lead to more informed clinical decisions and reduce unnecessary biopsies.
Study design
- Observational model
- Case-control
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| experimental | – | –experimental: patients with a condition |
| control group | – | –control group: patients without condition |
Who may qualify
- Ages
- 18 to 90 years
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- the study population will be patients of a primary care clinic
Key inclusion criteria (2 listed)
- Only patients with a clinical indication for mp prostate MRI will be included in this prospective study.
- No allergies to GBCA
Key exclusion criteria (1 listed)
- 1. Contraindications for MRI
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
Normalized Quantitative Signal - Intensity - Measurements with Region of Interest drawn in specific T2-weighted axial MRI Images
Time frame: through study completion, an average of 3 years
Regions of interest for quantitative signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Image analysis will be performed on a PACS workstation. Signal intensity will be measured and normalized, therefore no units needed.
Quantitative Signal - Intensity - Measurements with Region of Interest in specific in Apparent diffusion coefficient (ADC) axial MRI Images
Time frame: through study completion, an average of 3 years
Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized in mm2/s
Quantitative Signal - Intensity - Measurements with Region of Interest in specific in high b-value (800, 1500, 4000) axial MRI Images
Time frame: through study completion, an average of 3 years
Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized in mm2/s
Signal - Intensity - Measurements with Region of Interest in specific dynamic contrast enhanced (DCE) MRI Images
Time frame: through study completion, an average of 3 years
Regions of interest for signal intensity measurements will be drawn in various prostate lesions, the size of the region of interest will depend on the target structure. Signal intensity will be measured and normalized. Image analysis will be performed on a PACS workstation. The original Time inteisity curves are transformed in relative enhancement curves. Thus, they are normalized with respect to first point in time and represent the percentage increase compared to the time before contrast arrival, no units needed.
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Nov 2023.
- Results publication: PubMed 26427566Weinreb JC, Barentsz JO, Choyke PL, Cornud F, Haider MA, Macura KJ, Margolis D, Schnall MD, Shtern F, Tempany CM, Thoeny HC, Verma S. PI-RADS Prostate Imaging - Reporting and Data System: 2015, Version 2. Eur Urol. 2016 Jan;69(1):16-40. doi: 10.1016/j.eururo.2015.08.052. Epub 2015 Oct 1.
- Results publication: PubMed 30898406Turkbey B, Rosenkrantz AB, Haider MA, Padhani AR, Villeirs G, Macura KJ, Tempany CM, Choyke PL, Cornud F, Margolis DJ, Thoeny HC, Verma S, Barentsz J, Weinreb JC. Prostate Imaging Reporting and Data System Version 2.1: 2019 Update of Prostate Imaging Reporting and Data System Version 2. Eur Urol. 2019 Sep;76(3):340-351. doi: 10.1016/j.eururo.2019.02.033. Epub 2019 Mar 18.
- Results publication: PubMed 33830009Morash C. What do you do with PI-RADS-3? Can Urol Assoc J. 2021 Apr;15(4):122. doi: 10.5489/cuaj.7262. No abstract available.
- Individual participant dataSharing planned: No
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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. This study is not recruiting at the moment.
Michael M. Lell, Prof. Dr. med. study director
Department of Radiology and Nuclear Medicine, Klinikum Nuernberg, Paracelsus Medical University, Germany
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 Paracelsus Medical University at 1 site in Germany.
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 1 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: finished, as of the sponsor's last update on 3 Nov 2023. 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 Germany. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Normalized Quantitative Signal - Intensity - Measurements with Region of Interest drawn in specific T2-weighted axial MRI Images (time frame: through study completion, an average of 3 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?
On the ClinicalTrials.gov record for NCT06116344, which also lists 3 linked publications.
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 3 Nov 2023.