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.