Radionucleide TherapyPADLNCT06268418
Pre-therapeutic 68Ga-PSMA PET AI Based Dose Prediction for 177Lu-PSMA Targeted Radionuclide Therapy
An observational study of Radionucleide Therapy, run by Central Hospital, Nancy, France at 2 sites in France.
Trial summary
Targeted Radionuclide Therapy (TRT) is a contemporary approach to radiation oncology, aiming to deliver the maximal destructive radiation dose via cancer-targeting radiopharmaceutical. Read more about this study
- Who is it for?
- Ages 18 to 99 years, men only
- Conditions the study is for
- Radionucleide Therapy
- What is being tested?
- Artificial intelligence
- How many people
- 46 participants
- Where
- 2 sitesFrance
- Study dates
- 30 Nov 2024to 30 Jun 2025
- 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 99 years
- Men only
- No healthy volunteers
- Adult, Older adult
Study population: Patients who received at least one dose of 177Lu-PSMA and for whom a 68Ga-PSMA PET/CT was performed as part of IVRT in the "pre-treatment" assessment between 1 2022 and 31 January 2024
Who may be able to join
- Patients who received at least one dose of 177Lu-PSMA and for whom a 68Ga-PSMA PET/CT was performed as part of IVRT in the "pre-treatment" assessment
Who cannot join
- Patient opposition to the use of their data as part of this research.
Trial locations and contacts
2 sites in 1 country.
France2 sites
CHRU de NANCY
Vandœuvre-lès-Nancy, 54511
Nuclear medicine Department CHRU de NANCY
Vandœuvre-lès-Nancy, 54511
About this study
Targeted Radionuclide Therapy (TRT) is a contemporary approach to radiation oncology, aiming to deliver the maximal destructive radiation dose via cancer-targeting radiopharmaceutical. Radioactive ligands for the prostate-specific membrane antigen (PSMA) have emerged for the treatment of metastatic castration-resistant prostate cancer (mCRPC).Normal organ and tumor dose can be assessed by a series of cross-sectional whole-body SPECT scans, however, these require a large amount imaging time and are often not feasible in routine clinical practice.
An alternative is to generate a 3D time integrated activity (TIA) map per patient based on the PBPK and the pre-therapy imaging
Read the detailed description
Despite the early success of TRT, concerns have been raised about the risks of inadequate trade-off between therapeutic dose and side effects. Currently, the protocols for administering the radiopharmaceuticals are assessed on a population basis, and the activity to administer was determined for a specific patient group based on preceding studies . However, the European Council Directive (2013/59 Euratom) mandates that TRT treatments should be planned according to the optimal radiation dose tailored for individual patients, as has long been the case for external beam radiotherapy (EBRT) or brachytherapy. An essential requirement of TRT treatment planning is to estimate the absorbed dose in advance of therapy.
Prior knowledge of the biodistribution of the therapeutic agent via the pre-therapy imaging assists to optimize the trade-off between tumor destruction and irradiation of healthy tissues. Concepts, such as physiologically based pharmacokinetic (PBPK) modeling, have been proposed to estimate the spatiotemporal pharmacokinetics of imaging agents and then extrapolate to the treatment agents.
An alternative is to generate a 3D time integrated activity (TIA) map per patient based on the PBPK and the pre-therapy imaging. The TIA gives the information about number of decays that take place in each voxel during the total duration of the therapy. PBPK is an organ-based model, then the calculation of the 3D TIA raises the issue of organ segmentations on the pre-therapy nuclear imaging, which must be robust, automatic, and accurate. The absorbed dose to the patient can be estimated before the treatment using the 3D TIA and the patient anatomy (CT image) using Monte Carlo (MC) simulation. . This project will address two main challenges: (a) the robust and accurate metabolic segmentation in nuclear medicine for the 3D TIA calculation, and (b) the fast dose prediction based on MC and deep-learning approach.
- Conditions
- Radionucleide Therapy
- Being tested
- Artificial intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial intelligenceOther
Segmentation
What is being measured
The primary outcome is the main question the study is designed to answer.
Evaluate the prediction of the absorbed dose by Deep Learning approaches for RLT with 177Lu-PSMA, from pre-treatment 68Ga-PSMA.PET/CT images
Difference between the dose prediction by the model and that calculated with a reference method (Monte Carlo)
Measured: 1 month
Secondary outcomes (1)
Automatically contour the total tumor metabolic volume on 68Ga-PSMA pretreatment PET images using Deep Learning approaches
Dice index between the reference contour and that given by the model
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Feb 2024
- Study start30 Nov 2024
- Primary completion30 Apr 2025
- Study completion30 Jun 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Radionucleide TherapyStudies of the same condition, open and under way.
- More trials run by Central Hospital, Nancy, FranceThe 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
- Retrospective
- Enrolment
- 46 participants (actual)
Sponsor and investigators
- Lead sponsor
- Central Hospital, Nancy, France
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, BOURSIER Caroline, Central Hospital, Nancy, France
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06268418
- Sponsor's protocol ID
- 2024PI020
- Acronym
- PADL
- Status last verified
- Jul 2025
- First submitted
- 13 Feb 2024
- Last updated
- 16 Jul 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 16 Jul 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pre-therapeutic 68Ga-PSMA PET AI Based Dose Prediction for 177Lu-PSMA Targeted Radionuclide Therapy
- ClinicalTrials.gov ID
- NCT06268418
- Acronym / sponsor ID
- PADL / 2024PI020
- Conditions
- Radionucleide Therapy
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 46 participants (actual)
- Lead sponsor
- Central Hospital, Nancy, FranceUniversity, hospital or other
- Study start
- 30 Nov 2024 (actual)
- Primary completion
- 30 Apr 2025 (actual)
- Study completion
- 30 Jun 2025 (actual)
- First posted
- 20 Feb 2024 (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.
Targeted Radionuclide Therapy (TRT) is a contemporary approach to radiation oncology, aiming to deliver the maximal destructive radiation dose via cancer-targeting radiopharmaceutical. Radioactive ligands for the prostate-specific membrane antigen (PSMA) have emerged for the treatment of metastatic castration-resistant prostate cancer (mCRPC).Normal organ and tumor dose can be assessed by a series of cross-sectional whole-body SPECT scans, however, these require a large amount imaging time and are often not feasible in routine clinical practice.
An alternative is to generate a 3D time integrated activity (TIA) map per patient based on the PBPK and the pre-therapy imaging
Detailed description
Despite the early success of TRT, concerns have been raised about the risks of inadequate trade-off between therapeutic dose and side effects. Currently, the protocols for administering the radiopharmaceuticals are assessed on a population basis, and the activity to administer was determined for a specific patient group based on preceding studies . However, the European Council Directive (2013/59 Euratom) mandates that TRT treatments should be planned according to the optimal radiation dose tailored for individual patients, as has long been the case for external beam radiotherapy (EBRT) or brachytherapy. An essential requirement of TRT treatment planning is to estimate the absorbed dose in advance of therapy.
Prior knowledge of the biodistribution of the therapeutic agent via the pre-therapy imaging assists to optimize the trade-off between tumor destruction and irradiation of healthy tissues. Concepts, such as physiologically based pharmacokinetic (PBPK) modeling, have been proposed to estimate the spatiotemporal pharmacokinetics of imaging agents and then extrapolate to the treatment agents.
An alternative is to generate a 3D time integrated activity (TIA) map per patient based on the PBPK and the pre-therapy imaging. The TIA gives the information about number of decays that take place in each voxel during the total duration of the therapy. PBPK is an organ-based model, then the calculation of the 3D TIA raises the issue of organ segmentations on the pre-therapy nuclear imaging, which must be robust, automatic, and accurate. The absorbed dose to the patient can be estimated before the treatment using the 3D TIA and the patient anatomy (CT image) using Monte Carlo (MC) simulation. . This project will address two main challenges: (a) the robust and accurate metabolic segmentation in nuclear medicine for the 3D TIA calculation, and (b) the fast dose prediction based on MC and deep-learning approach.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 99 years
- Sex
- Male only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients who received at least one dose of 177Lu-PSMA and for whom a 68Ga-PSMA PET/CT was performed as part of IVRT in the "pre-treatment" assessment between 1 2022 and 31 January 2024
Key inclusion criteria (1 listed)
- Patients who received at least one dose of 177Lu-PSMA and for whom a 68Ga-PSMA PET/CT was performed as part of IVRT in the "pre-treatment" assessment
Key exclusion criteria (1 listed)
- Patient opposition to the use of their data as part of this research.
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 intelligenceSegmentation | Other | – |
Primary outcome
Evaluate the prediction of the absorbed dose by Deep Learning approaches for RLT with 177Lu-PSMA, from pre-treatment 68Ga-PSMA.PET/CT images
Time frame: 1 month
Difference between the dose prediction by the model and that calculated with a reference method (Monte Carlo)
1 secondary outcome
Automatically contour the total tumor metabolic volume on 68Ga-PSMA pretreatment PET images using Deep Learning approaches
Time frame: 1 month
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 16 Jul 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. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
2 sites in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Radionucleide Therapy, run by Central Hospital, Nancy, France at 2 sites 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 1 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 16 Jul 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 2 sites 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?
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?
Evaluate the prediction of the absorbed dose by Deep Learning approaches for RLT with 177Lu-PSMA, from pre-treatment 68Ga-PSMA.PET/CT images (time frame: 1 month). 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 16 Jul 2025.