177Lu-DOTATATE Theranostics: Predicting Renal Dosimetry From Pretherapy 68Ga-DOTATATE PET and Clinical Biomarkers

Abstract

Purpose: Pretreatment predictions of absorbed doses can be especially valuable for patient selection and dosimetry-guided individualization of radiopharmaceutical therapy. Our goal was to build regression models using pretherapy 68Ga-DOTATATE PET uptake data and other baseline clinical factors/biomarkers to predict renal absorbed dose delivered by 177Lu-DOTATATE peptide receptor radionuclide therapy (177Lu-PRRT) for neuroendocrine tumors. We explore the combination of biomarkers and 68Ga PET uptake metrics, hypothesizing that they will improve predictive power over univariable regression.

Patients and methods: Pretherapy 68Ga-DOTATATE PET/CTs were analyzed for 25 patients (50 kidneys) who also underwent quantitative 177Lu SPECT/CT imaging at approximately 4, 24, 96, and 168 hours after cycle 1 of 177Lu-PRRT. Kidneys were contoured on the CT of the PET/CT and SPECT/CT using validated deep learning-based tools. Dosimetry was performed by coupling the multi-time point SPECT/CT images with an in-house Monte Carlo code. Pretherapy renal PET SUV metrics, activity concentration per injected activity (Bq/mL/MBq), and other baseline clinical factors/biomarkers were investigated as predictors of the 177Lu SPECT/CT-derived mean absorbed dose per injected activity to the kidneys using univariable and bivariable models. Leave-one-out cross-validation (LOOCV) was used to estimate model performance using root mean squared error and absolute percent error in predicted renal absorbed dose including mean absolute percent error (MAPE) and associated standard deviation (SD).

Results: The median therapy-delivered renal dose was 0.5 Gy/GBq (range, 0.2-1.0 Gy/GBq). In LOOCV of univariable models, PET uptake (Bq/mL/MBq) performs best with MAPE of 18.0% (SD = 13.3%), and estimated glomerular filtration rate (eGFR) gives an MAPE of 28.5% (SD = 19.2%). Bivariable regression with both PET uptake and eGFR gives LOOCV MAPE of 17.3% (SD = 11.8%), indicating minimal improvement over univariable models.

Conclusions: Pretherapy 68Ga-DOTATATE PET renal uptake can be used to predict post-177Lu-PRRT SPECT-derived mean absorbed dose to the kidneys with accuracy within 18%, on average. Compared with PET uptake alone, including eGFR in the same model to account for patient-specific kinetics did not improve predictive power. Following further validation of these preliminary findings in an independent cohort, predictions using renal PET uptake can be used in the clinic for patient selection and individualization of treatment before initiating the first cycle of PRRT.