Pre-therapeutic 68Ga-PSMA PET AI Based Dose Prediction for 177Lu-PSMA Targeted Radionuclide Therapy (PADL)

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

Study Overview

• Status: Not yet recruiting
• Conditions: Radionucleide Therapy
• Intervention / Treatment: Other: Artificial intelligence

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 Type: Observational
• Enrollment (Estimated): 46

Contacts and Locations

• Study Contact: Name: VERONIQUE ROCH, MSc; Phone Number: +33 383154276; Email: v.roch@chru-nancy.fr
• Study Contact Backup: Name: CAROLINE BOURSIER, MD; Phone Number: +33 383154316; Email: c.boursier@chru-nancy.fr

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)	1 month

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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	1 month

Collaborators and Investigators

• Sponsor: Central Hospital, Nancy, France

Study record dates

Study Major Dates

• Study Start (Estimated): March 31, 2024
• Primary Completion (Estimated): June 30, 2024
• Study Completion (Estimated): August 30, 2024

Study Registration Dates

• First Submitted: February 13, 2024
• First Submitted That Met QC Criteria: February 13, 2024
• First Posted (Actual): February 20, 2024

Study Record Updates

• Last Update Posted (Estimated): February 21, 2024
• Last Update Submitted That Met QC Criteria: February 20, 2024
• Last Verified: February 1, 2024

More Information

Terms related to this study

• Other Study ID Numbers: 2024PI020

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No