RT Journal Article SR Electronic T1 Modeling and Predicting Tumor Response in Radioligand Therapy JF Journal of Nuclear Medicine JO J Nucl Med FD Society of Nuclear Medicine SP 65 OP 70 DO 10.2967/jnumed.118.210377 VO 60 IS 1 A1 Kletting, Peter A1 Thieme, Anne A1 Eberhardt, Nina A1 Rinscheid, Andreas A1 D’Alessandria, Calogero A1 Allmann, Jakob A1 Wester, Hans-Jürgen A1 Tauber, Robert A1 Beer, Ambros J. A1 Glatting, Gerhard A1 Eiber, Matthias YR 2019 UL http://jnm.snmjournals.org/content/60/1/65.abstract AB 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.