Characterization of Effective Half-Life for Instant Single-Time-Point Dosimetry Using Machine Learning

Single-time-point (STP) image-based dosimetry offers a more convenient approach for clinical practice in radiopharmaceutical therapy (RPT) compared with conventional multiple-time-point image-based dosimetry. Despite numerous advancements, current STP methods are limited by the need for strict and late timing in data acquisition, posing challenges in routine clinical settings. This study introduces a new concept of instant STP (iSTP) dosimetry, achieved by predicting the effective half-life (T _eff) of organs using machine learning applied on pretherapy patient data (PET and clinical values). Methods: Data from 22 patients who underwent pretherapy ⁶⁸Ga-gallium N,N-bis[2-hydroxy-5-(carboxyethyl)benzyl]ethylenediamine-N,N-diacetic acid ([⁶⁸Ga]Ga-PSMA-11) imaging and subsequently [¹⁷⁷Lu]Lu-PSMA I&T RPT were analyzed. A machine learning model was developed for T _eff predictions for the left and right kidneys, liver, and spleen subsequently used to estimate time-integrated activity and absorbed dose. iSTP results were compared against multiple-time-point and previously proposed Hänscheid methods. Our method comprised 2 different prediction scenarios, using data before each therapy cycle and from the first cycle. Results: The iSTP method introduced early posttherapy time points (2, 20, 43, and 69 h) for the left kidney, right kidney, liver, and spleen. Dosimetry in the first scenario, aggregating 2 and 20 h, achieved mean differences in time-integrated activity below 27% for all organs. To assess the feasibility, these time points were compared with the best results from the Hänscheid method (kidneys, 69 h; liver and spleen, 20 h). At 2 h, a significant difference (P < 0.001) was found for almost all organs except for the spleen (P = 0.1370). However, at 20 h, no significant differences were found for the right kidney, liver, and spleen, apart from the left kidney (P < 0.01). In the scenario using only the initial PET/CT data to predict T _eff for subsequent cycles, iSTP dosimetry achieved no statistical significance (P > 0.05) for all cycles in comparison to results using PET data before each therapy cycle. Conclusion: Our preliminary results prove the concept for prediction of T _eff with pretherapy data and achieving STP shortly and flexibly after the RPT. The proposed method may expedite the application of dosimetry in broader contexts, such as outpatient or short-duration inpatient treatment.