Combined model-based and patient-specific dosimetry for ¹⁸F-DCFPyL, a PSMA-targeted PET agent

Prostate-specific membrane antigen (PSMA), a type-II integral membrane protein highly expressed in prostate cancer, has been extensively used as a target for imaging and therapy. Among the available PET radiotracers, the low molecular weight agents that bind to PSMA are proving particularly effective. We present the dosimetry results for ¹⁸F-DCFPyL in nine patients with metastatic prostate cancer.

Nine patients were imaged using sequential PET/CT scans at approximately 1, 12, 35 and 70 min, and a final PET/CT scan at approximately 120 min after intravenous administration of 321 ± 8 MBq (8.7 ± 0.2 mCi) of¹⁸F-DCFPyL. Time-integrated-activity coefficients were calculated and used as input in OLINDA/EXM software to obtain dose estimates for the majority of the major organs. The absorbed doses (AD) to the eye lens and lacrimal glands were calculated using Monte-Carlo models based on idealized anatomy combined with patient-specific volumes and activity from the PET/CT scans. Monte-Carlo based models were also developed for calculation of the dose to two major salivary glands (parotid and submandibular) using CT-based patient-specific gland volumes.

The highest calculated mean AD per unit administered activity of ¹⁸F was found in the lacrimal glands, followed by the submandibular glands, kidneys, urinary bladder wall, and parotid glands. The S-values for the lacrimal glands to the eye lens (0.42 mGy/MBq h), the tear film to the eye lens (1.78 mGy/MBq h) and the lacrimal gland self-dose (574.10 mGy/MBq h) were calculated. Average S-values for the salivary glands were 3.58 mGy/MBq h for the parotid self-dose and 6.78 mGy/MBq h for the submandibular self-dose. The resultant mean effective dose of ¹⁸F-DCFPyL was 0.017 ± 0.002 mSv/MBq.

¹⁸F-DCFPyL dosimetry in nine patients was obtained using novel models for the lacrimal and salivary glands, two organs with potentially dose-limiting uptake for therapy and diagnosis which lacked pre-existing models.