Treatment planning algorithm for peptide receptor radionuclide therapy considering multiple tumor lesions and organs at risk.

Autor: Jiménez-Franco LD; Medical Radiation Physics/Radiation Protection, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, D-68167, Germany.; Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, D-68167, Germany., Kletting P; Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, 89081, Germany.; Department of Nuclear Medicine, Ulm University, Ulm, 89081, Germany., Beer AJ; Department of Nuclear Medicine, Ulm University, Ulm, 89081, Germany., Glatting G; Medical Radiation Physics, Department of Nuclear Medicine, Ulm University, Ulm, 89081, Germany.; Department of Nuclear Medicine, Ulm University, Ulm, 89081, Germany.
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2018 Jun 15. Date of Electronic Publication: 2018 Jun 15.
DOI: 10.1002/mp.13049
Abstrakt: Purpose: Peptide receptor radionuclide therapy (PRRT) has shown promising results in the treatment of tumors with high expression of somatostatin receptors such as neuroendocrine tumors (NETs) and meningioma. However, PRRT potentially produces high renal and red marrow (RM) toxicity, the kidneys usually being the dose-limiting organ. Previously, it was shown that an improved therapeutic index can be achieved by choosing an optimal combination of injected activity and peptide molar amount. The aim of this work was to develop a clinically applicable algorithm for treatment planning in PRRT. To demonstrate the applicability and possible advantages of the algorithm thus developed, an in silico clinical trial applying the algorithm to 177 Lu-DOTATATE therapy in nine virtual patients was conducted.
Methods: An algorithm for treatment planning in PRRT was developed, which simultaneously considers multiple tumor lesions, maximum tolerated biologically effective doses (BEDs) for multiple organs at risk (OARs) and a maximum achievable molar activity. The algorithm, subject to the abovementioned constraints, aims at maximizing the total number of killed tumor cells in the considered lesions/metastases. An in silico clinical trial was conducted with nine virtual patients. For each virtual patient, simulations increasing the molar dose of 177 Lu-DOTATATE from 2 to 2048 nmol by factors of 25 were performed. Maximum tolerated BEDs per cycle for the kidneys (10 Gy 2.5 ) and for the RM (0.5 Gy 15 ) were defined based on a planned total treatment of four cycles. A maximum achievable molar activity of 420 MBq/nmol was assumed. Optimal combinations of molar dose and activity were determined by applying the developed algorithm. For comparison, simulations for a typical plan with 177 Lu-DOTATATE (7.4 GBq, 265 nmol) were performed and BEDs for the OARs and for individual tumor lesions were calculated. Furthermore, to determine treatment efficacy, overall tumor control probability (oTCP) values after a four-cycle treatment were estimated for the optimal and typical plans.
Results: The conducted in silico clinical trial yielded optimal molar doses and activities ranging from 24 to 512 nmol and from 6 to 30 GBq, respectively. Tumor BEDs ranged from 2 to 107 Gy 10 and from 1 to 65 Gy 10 for the optimal and typical plans, respectively. The estimated oTCP values showed that the optimal plans may produce adequate tumor control in six of the nine virtual patients after four cycles of 177 Lu-DOTATATE while the typical plan may be sufficient in only two virtual patients.
Conclusions: The algorithm presented can derive plans with higher tumor control than the typically delivered plan. Therefore, we propose this algorithm for clinical validation and possibly future implementation in treatment planning in molecular radiotherapy.
(© 2018 American Association of Physicists in Medicine.)
Databáze: MEDLINE
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