Computer-implemented Method for Single Time Point Dosimetry

Patent Abstract

The present invention relates to a computer-implemented single time-point dosimetry method (200) comprising the following steps: a. Deriving for one or more organs at least one pre-therapy metrics from a pre-therapy scan of a patient (201); b. Deriving for the one or more organs the activity value at a specific time from a posttherapy scan of the patient (203); c. Computing the time-integrated activity value for the one or more organs (204); wherein the time-integrated value is computed based on the activity value from the posttherapy scan, the specific time of the posttherapy scan and a predicted effective half-life value, and wherein the predicted effective half-life value is determined by a machine learning-model trained according to the method of the present invention.

Simple SummaryContent extracted with AI.

This invention describes a computer-implemented method for performing dosimetry (measurement of absorbed radiation dose) from a single time point during radiopharmaceutical therapy, such as radioligand therapy. By using machine learning trained on pre-therapy and posttherapy scans and clinical data, it can predict patient-specific biokinetics (effective half-life of the radiopharmaceutical in organs or tissues) and thus estimate the total radiation dose absorbed in different organs or tissues from just one imaging scan after therapy instead of the standard, multiple time point approach.

Use CasesContent extracted with AI.

• Personalized dosimetry in radioligand therapy for cancer patients (e.g., prostate cancer), enabling tailored treatment to individual patient needs.
• Integration into nuclear medicine departments to streamline workflow and reduce the need for multiple posttherapy scans.
• Use in clinics or hospitals with limited imaging resources or scanner availability.
• Supporting outpatient procedures where lengthy or multiple imaging sessions are impractical.
• Dose estimation for regulatory or safety assessments in radiopharmaceutical therapy studies.

BenefitsContent extracted with AI.

• Reduces the number of scans required for effective dosimetry from multiple to a single posttherapy scan, minimizing the burden on patients and healthcare resources.
• Enables early and flexible scheduling of posttherapy imaging, accommodating real-world logistics better than traditional methods.
• Facilitates more personalized and accurate dose quantification by leveraging machine learning models trained on individual patient and organ data.
• Can lower costs associated with dosimetry, treatments, and scan acquisition times.
• Improves access to dosimetry-guided therapy in less specialized centers or clinics with fewer resources.
• Potentially enhances patient comfort, compliance, and throughput due to fewer and shorter imaging sessions.
• Supports faster and broader adoption of personalized radiopharmaceutical therapies.

Technical Classifications (CPCs)

Inventors & Applicants