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.