Clinical trial NCT06268418: Pre-therapeutic 68Ga-PSMA PET AI Based Dose Prediction for 177Lu-PSMA Targeted Radionuclide Therapy
Official title: Pre-therapeutic 68Ga-PSMA PET AI Based Dose Prediction for 177Lu-PSMA Targeted Radionuclide Therapy
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Radionucleide Therapy
Lead sponsor: Central Hospital, Nancy, France
Sponsor class: OTHER
First posted: 2024-02-20
Enrollment: 46
Sex: MALE
Minimum age (years): 18
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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

Brief summary: 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.

Eligibility criteria: Inclusion Criteria:

* 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

Exclusion Criteria:

* Patient opposition to the use of their data as part of this research.

Primary outcomes:
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
timeFrame: 1 month
Secondary outcomes:
measure: Automatically contour the total tumor metabolic volume on 68Ga-PSMA pretreatment PET images using Deep Learning approaches
timeFrame: 1 month
Other outcomes:
Locations:
city: Vandoeuvre les Nancy cedex
country: France
facility: CHRU de NANCY
city: Vandoeuvre les Nancy cedex
country: France
facility: Nuclear medicine Department CHRU de NANCY
Study officials:
References: