Personalized three-dimensional dose calculation method based on multi-modal images in dosimetry assessment of prostate cancer with skull metastasis: a Monte Carlo simulation study

Abstract Background The nuclear medicine treatment based on 177Lu-PSMA-617 has achieved excellent therapeutic effects on Prostate cancer (PCa), but there is a lack of three-dimensional dosimetry analysis of organs at risk (OARs) and distant metastatic lesions. Results This work establishes an accura...

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Bibliographic Details
Main Authors: Feng Tian, Huan Liu, Yingying Gu, Dandan Zhang, Lijun Tang
Format: Article
Language:English
Published: SpringerOpen 2025-07-01
Series:EJNMMI Physics
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Online Access:https://doi.org/10.1186/s40658-025-00781-0
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Summary:Abstract Background The nuclear medicine treatment based on 177Lu-PSMA-617 has achieved excellent therapeutic effects on Prostate cancer (PCa), but there is a lack of three-dimensional dosimetry analysis of organs at risk (OARs) and distant metastatic lesions. Results This work establishes an accurate and personalized three-dimensional dose calculation method based on Monte Carlo simulation and multi-modal images of PCa patients with skull metastasis, and analyzes the three-dimensional dose distribution of metastatic lesions and OARs in the brain. Results show that due to the targeting characteristics of 177Lu-PSMA-617, metastatic brain lesions of all patients can receive high radiation doses with the average dose of 0.05 mGy/MBq, and the dose distribution are relatively uniform with the average homogeneity index of 1.23. In addition, the radiation dose received by most OARs are much lower than that of metastatic lesions, but for parotid glands, the dose deposited are only 3.99 times less than that of metastatic lesions due to the high absorption of 177Lu-PSMA-617. Conclusion Therefore, it is necessary to clarify the three-dimensional dose distribution of OARs and metastatic lesions, so as to optimize the activity injected and achieve the precise killing of metastatic lesions and protection of OARs.
ISSN:2197-7364