Peter Kletting1,2, Anne Thieme3, Nina Eberhardt4, Andreas Rinscheid4,2, Calogero D'Alessandria3, Jakob Allmann3, Hans-Jürgen Wester5, Robert Tauber6, Ambros J Beer4, Gerhard Glatting4,2, Matthias Eiber3. 1. Department of Nuclear Medicine, Ulm University, Ulm, Germany peter.kletting@uniklinik-ulm.de. 2. Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, Germany. 3. Department of Nuclear Medicine, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany. 4. Department of Nuclear Medicine, Ulm University, Ulm, Germany. 5. Pharmaceutical Radiochemistry, Technische Universität München, Garching, Germany; and. 6. Department of Urology, Klinikum Rechts der Isar, Technische Universität München, Munich, Germany.
Abstract
The aim of this work was to develop a theranostic method that allows prediction of prostate-specific membrane antigen (PSMA)-positive tumor volume after radioligand therapy (RLT) based on a pretherapeutic PET/CT measurement and physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling at the example of RLT using 177Lu-labeled PSMA for imaging and therapy (PSMA I&T). Methods: A recently developed PBPK model for 177Lu-PSMA I&T RLT was extended to account for tumor (exponential) growth and reduction due to irradiation (linear quadratic model). Data from 13 patients with metastatic castration-resistant prostate cancer were retrospectively analyzed. Pharmacokinetic/pharmacodynamic parameters were simultaneously fitted in a Bayesian framework to PET/CT activity concentrations, planar scintigraphy data, and tumor volumes before and after (6 wk) therapy. The method was validated using the leave-one-out Jackknife method. The tumor volume after therapy was predicted on the basis of pretherapy PET/CT imaging and PBPK/PD modeling. Results: The relative deviation of the predicted and measured tumor volume for PSMA-positive tumor cells (6 wk after therapy) was 1% ± 40%, excluding 1 patient (prostate-specific antigen-negative) from the population. The radiosensitivity for the prostate-specific antigen-positive patients was determined to be 0.0172 ± 0.0084 Gy-1 Conclusion: To our knowledge, the proposed method is the first attempt to solely use PET/CT and modeling methods to predict the PSMA-positive tumor volume after RLT. Internal validation shows that this is feasible with an acceptable accuracy. Improvement of the method and external validation of the model is ongoing.
The aim of this work was to develop a theranostic method that allows prediction of prostate-specific membrane antigen (PSMA)-positive tumor volume after radioligand therapy (RLT) based on a pretherapeutic PET/CT measurement and physiologically based pharmacokinetic/pharmacodynamic (PBPK/PD) modeling at the example of RLT using 177Lu-labeled PSMA for imaging and therapy (PSMA I&T). Methods: A recently developed PBPK model for 177Lu-PSMA I&T RLT was extended to account for tumor (exponential) growth and reduction due to irradiation (linear quadratic model). Data from 13 patients with metastatic castration-resistant prostate cancer were retrospectively analyzed. Pharmacokinetic/pharmacodynamic parameters were simultaneously fitted in a Bayesian framework to PET/CT activity concentrations, planar scintigraphy data, and tumor volumes before and after (6 wk) therapy. The method was validated using the leave-one-out Jackknife method. The tumor volume after therapy was predicted on the basis of pretherapy PET/CT imaging and PBPK/PD modeling. Results: The relative deviation of the predicted and measured tumor volume for PSMA-positive tumor cells (6 wk after therapy) was 1% ± 40%, excluding 1 patient (prostate-specific antigen-negative) from the population. The radiosensitivity for the prostate-specific antigen-positive patients was determined to be 0.0172 ± 0.0084 Gy-1 Conclusion: To our knowledge, the proposed method is the first attempt to solely use PET/CT and modeling methods to predict the PSMA-positive tumor volume after RLT. Internal validation shows that this is feasible with an acceptable accuracy. Improvement of the method and external validation of the model is ongoing.
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