Prostate CancerNCT06168864
Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.
An observational study of Prostate Cancer, run by IRCCS San Raffaele at 1 site in Italy.
Trial summary
Prostate cancer is the second most common cancer in the male population. This pathology represents an oncological and public health problem especially in developed countries, due to a greater presence of elderly men in the population. Read more about this study
- Who is it for?
- Ages 18 years and older, men only
- Conditions the study is for
- Prostate Cancer
- What is being tested?
- Artificial intelligence models for segmentation and characterization of prostate cancer
- How many people
- 350 participants
- Where
- 1 siteItaly
- Study dates
- 6 Jan 2020to 1 Jun 2022
- 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
- Adult, Older adult
Study population: Patients with prostate cancer undergoing PET examination with 68 Ga-PMSA (PET/CT or PET/MRI), since 01/06/2020, at the U.O. of Nuclear Medicine at the San Raffaele Hospital on the clinical indication of the specialist.
Who may be able to join
- Patients with histological diagnosis of prostate cancer;
- Patients who performed a PET exam with 68 Ga-PMSA.
Who cannot join
- CT and MR images with artifacts that preclude interpretation of results.
Trial locations and contacts
1 site in 1 country.
About this study
Prostate cancer is the second most common cancer in the male population. This pathology represents an oncological and public health problem especially in developed countries, due to a greater presence of elderly men in the population.
Medical imaging plays a central role in the staging and restaging of prostate disease. Magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are among the methods commonly used in normal clinical practice for the characterization of prostate cancer. To date, the study of these images is limited to a qualitative visual analysis, however there is increasing evidence relating to the usefulness of introducing a quantitative (or semi-quantitative) analysis of biomedical images.
Read more
The current increase in available imaging data, and their quality, allows the application of artificial intelligence methods also in the medical field for the automation of tasks (e.g. automatic segmentation) and classification (e.g. tumor aggressiveness).
The extraction of quantitative data, and more generally the study of tumor lesions, requires manual segmentation by one or more doctors. This process requires very long times as each image must be processed individually; furthermore, the result also depends on the level of experience of the doctor carrying out the segmentation and this could create a source of heterogeneity, affecting the reproducibility of the segmentation.
AI-based automatic segmentation methods can be applied to medical images for the localization of tumor lesions, thus exceeding the limits of manual segmentation.
- Conditions
- Prostate Cancer
- Being tested
- Artificial intelligence models for segmentation and characterization of prostate cancer
- Medical subject headings
- Prostatic Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligence models for segmentation and characterization of prostate cancerDiagnostic test
rtificial intelligence algorithms for the automatic segmentation of prostate cancer lesions on medical images.
What is being measured
The primary outcome is the main question the study is designed to answer.
Artificial intelligence algorithms for the classification of prostate cancer lesions on medical images.
PET images from enrolled patients will be used to create models that investigate the ability of artificial intelligence to automate tumor segmentation tasks.
Measured: 2 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted13 Dec 2023
- Study start6 Jan 2020
- Primary completion1 Jun 2022
- Study completion1 Jun 2022
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.
- More trials run by IRCCS San RaffaeleThe 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 350 participants (actual)
Sponsor and investigators
- Lead sponsor
- IRCCS San Raffaele
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Chiti Arturo, IRCCS San Raffaele
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06168864
- Sponsor's protocol ID
- AI_Pca
- Status last verified
- Dec 2023
- First submitted
- 5 Dec 2023
- Last updated
- 13 Dec 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 13 Dec 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of Artificial Intelligence Models for Segmentation and Characterization of Prostate Cancer: a Single-center Retrospective Observational Study.
- ClinicalTrials.gov ID
- NCT06168864
- Acronym / sponsor ID
- AI_Pca
- Conditions
- Prostate Cancer
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 350 participants (actual)
- Lead sponsor
- IRCCS San RaffaeleUniversity, hospital or other
- Study start
- 6 Jan 2020 (actual)
- Primary completion
- 1 Jun 2022 (actual)
- Study completion
- 1 Jun 2022 (actual)
- First posted
- 13 Dec 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.
Prostate cancer is the second most common cancer in the male population. This pathology represents an oncological and public health problem especially in developed countries, due to a greater presence of elderly men in the population.
Medical imaging plays a central role in the staging and restaging of prostate disease. Magnetic resonance imaging (MRI), computed tomography (CT) and positron emission tomography (PET) are among the methods commonly used in normal clinical practice for the characterization of prostate cancer. To date, the study of these images is limited to a qualitative visual analysis, however there is increasing evidence relating to the usefulness of introducing a quantitative (or semi-quantitative) analysis of biomedical images.
The current increase in available imaging data, and their quality, allows the application of artificial intelligence methods also in the medical field for the automation of tasks (e.g. automatic segmentation) and classification (e.g. tumor aggressiveness).
The extraction of quantitative data, and more generally the study of tumor lesions, requires manual segmentation by one or more doctors. This process requires very long times as each image must be processed individually; furthermore, the result also depends on the level of experience of the doctor carrying out the segmentation and this could create a source of heterogeneity, affecting the reproducibility of the segmentation.
AI-based automatic segmentation methods can be applied to medical images for the localization of tumor lesions, thus exceeding the limits of manual segmentation.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- Male only
- Age groups
- Adult, Older adult
- Study population
- Patients with prostate cancer undergoing PET examination with 68 Ga-PMSA (PET/CT or PET/MRI), since 01/06/2020, at the U.O. of Nuclear Medicine at the San Raffaele Hospital on the clinical indication of the specialist.
Key inclusion criteria (2 listed)
- Patients with histological diagnosis of prostate cancer;
- Patients who performed a PET exam with 68 Ga-PMSA.
Key exclusion criteria (1 listed)
- CT and MR images with artifacts that preclude interpretation of results.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial intelligence models for segmentation and characterization of prostate cancerrtificial intelligence algorithms for the automatic segmentation of prostate cancer lesions on medical images. | Diagnostic test | – |
Primary outcome
Artificial intelligence algorithms for the classification of prostate cancer lesions on medical images.
Time frame: 2 years
PET images from enrolled patients will be used to create models that investigate the ability of artificial intelligence to automate tumor segmentation tasks.
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 13 Dec 2023.
- 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.
The record lists no contact. Use the official record to reach the sponsor.
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 San Raffaele 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 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 13 Dec 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 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?
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?
Artificial intelligence algorithms for the classification of prostate cancer lesions on medical images. (time frame: 2 years). 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 13 Dec 2023.