PT  - JOURNAL ARTICLE
AU  - Kletting, Peter
AU  - Thieme, Anne
AU  - Eberhardt, Nina
AU  - Rinscheid, Andreas
AU  - D’Alessandria, Calogero
AU  - Allmann, Jakob
AU  - Wester, Hans-Jürgen
AU  - Tauber, Robert
AU  - Beer, Ambros J.
AU  - Glatting, Gerhard
AU  - Eiber, Matthias
TI  - Modeling and Predicting Tumor Response in Radioligand Therapy
AID  - 10.2967/jnumed.118.210377
DP  - 2019 Jan 01
TA  - Journal of Nuclear Medicine
PG  - 65--70
VI  - 60
IP  - 1
4099  - http://jnm.snmjournals.org/content/60/1/65.short
4100  - http://jnm.snmjournals.org/content/60/1/65.full
SO  - J Nucl Med2019 Jan 01; 60
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.