Heterogeneity of dose distribution in normal tissues in case of radiopharmaceutical therapy with alpha-emitting radionuclides

Li, Wei Bo; Bouvier‐Capely, Céline; Vargas, Clarita Saldarriaga; Andersson, Michelle; Madas, Balázs G.

doi:10.1007/s00411-022-01000-5

Cited by 12 publications

(6 citation statements)

References 77 publications

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“…The MELODI Workshop 2020 on the “Spatial and temporal variation in dose delivery” brought together experts in clinical/epidemiological/dosimetric and biological/mechanistic aspects of research. It was organized in four sessions, addressing (1) dose-rate effects, (2) radiation quality, (3) internal exposure and (4) partial body exposure, each generating at least one thematic manuscript (Lowe et al 2022 ; Baiocco et al 2022 ; Boei et al 2022 ; Li et al 2022 ). Due to COVID19 outbreak, the workshop was successfully held on-line.…”

Section: Introductionmentioning

confidence: 99%

Out-of-field effects: lessons learned from partial body exposure

Pazzaglia

Eidemüller

Lumniczky³

et al. 2022

Radiat Environ Biophys

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Partial body exposure and inhomogeneous dose delivery are features of the majority of medical and occupational exposure situations. However, mounting evidence indicates that the effects of partial body exposure are not limited to the irradiated area but also have systemic effects that are propagated outside the irradiated field. It was the aim of the “Partial body exposure” session within the MELODI workshop 2020 to discuss recent developments and insights into this field by covering clinical, epidemiological, dosimetric as well as mechanistic aspects. Especially the impact of out-of-field effects on dysfunctions of immune cells, cardiovascular diseases and effects on the brain were debated. The presentations at the workshop acknowledged the relevance of out-of-field effects as components of the cellular and organismal radiation response. Furthermore, their importance for the understanding of radiation-induced pathologies, for the discovery of early disease biomarkers and for the identification of high-risk organs after inhomogeneous exposure was emphasized. With the rapid advancement of clinical treatment modalities, including new dose rates and distributions a better understanding of individual health risk is urgently needed. To achieve this, a deeper mechanistic understanding of out-of-field effects in close connection to improved modelling was suggested as priorities for future research. This will support the amelioration of risk models and the personalization of risk assessments for cancer and non-cancer effects after partial body irradiation.

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Section: Introductionmentioning

confidence: 99%

Out-of-field effects: lessons learned from partial body exposure

Pazzaglia

Eidemüller

Lumniczky³

et al. 2022

Radiat Environ Biophys

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“…Evidence has demonstrated the spatial heterogeneity of dose distribution influences the response of RPT [42], [43]. Although voxel-wise dosimetry monitoring has been established for RPT [44], [45], the pre-therapy prediction of voxel-wise dosimetry is technically more challenging due the increased complexity of the inference.…”

Section: Discussionmentioning

confidence: 99%

PBPK-Adapted Deep Learning for Pretherapy Prediction of Voxelwise Dosimetry: In-Silico Proof of Concept

Kassar,

Drobnjakovic,

Birindelli

et al. 2024

IEEE Trans. Radiat. Plasma Med. Sci.

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Pre-therapy dosimetry prediction is a prerequisite for treatment planning and personalized optimization of the emerging radiopharmaceutical therapy (RPT). Physiologically based pharmacokinetic (PBPK) model, describing the intrinsic pharmacokinetics of radiopharmaceuticals, have been proposed for pre-therapy prediction of dosimetry. However, it is restricted with organ-wise prediction and the customization based on pretherapy measurements is still challenging. On the other side, artificial intelligence (AI) has demonstrated the potential in pretherapy dosimetry prediction. Nevertheless, it is still challenging for pure data-driven model to achieve voxel-wise prediction due to huge gap between the pre-therapy imaging and post-therapy dosimetry. This study aims to integrate the PBPK model into deep learning for voxel-wise pre-therapy dosimetry prediction. A conditional generative adversarial network (cGAN) integrated with the PBPK model as regularization was developed. For proof of concept, 120 virtual patients with 68 Ga-PSMA-11 PET imaging and 177 Lu-PSMA-I&T dosimetry were generated using realistic in silico simulations. In kidneys, spleen, liver and salivary glands, the proposed method achieved better accuracy and dose volume histogram than pure deep learning. The preliminary results confirmed that the proposed PBPK-adapted deep learning can improve the pre-therapy voxel-wise dosimetry prediction and may provide a practical solution to support treatment planning of heterogeneous dose distribution for personalized RPT.

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“…Having expertise in both imaging and radionuclide dosimetry becomes extremely important for a medical physicist because the delivery of radionuclides and the distribution of radiation are critical for the successful treatment of BC. In addition, dosimetry calculations for the medical radioisotopes and daughters are still challenging given the many factors that need to be considered (160)(161)(162)(163)(164). It is necessary to accurately determine radiochemical purity and dosage as well as to account for both the parent radionuclide and associated decay daughters, as their relocation from the tumor site potentially places healthy tissues at risk.…”

Section: The Challenges Of Radionuclide Therapy For Breast Cancermentioning

confidence: 99%

The application of radionuclide therapy for breast cancer

Musket,

Davern,

Elam

et al. 2024

Front. Nucl. Med.

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Radionuclide-mediated diagnosis and therapy have emerged as effective and low-risk approaches to treating breast cancer. Compared to traditional anatomic imaging techniques, diagnostic radionuclide-based molecular imaging systems exhibit much greater sensitivity and ability to precisely illustrate the biodistribution and metabolic processes from a functional perspective in breast cancer; this transitions diagnosis from an invasive visualization to a noninvasive visualization, potentially ensuring earlier diagnosis and on-time treatment. Radionuclide therapy is a newly developed modality for the treatment of breast cancer in which radionuclides are delivered to tumors and/or tumor-associated targets either directly or using delivery vehicles. Radionuclide therapy has been proven to be eminently effective and to exhibit low toxicity in eliminating both primary tumors and metastases, and even undetected tumors. In addition, the specific interaction between the surface modules of the delivery vehicles and the targets on the surface of tumor cells enables radionuclide targeting therapy, and this represents an exceptional potential for this treatment in breast cancer. This article reviews the development of radionuclide molecular imaging techniques that are currently employed for early breast cancer diagnosis and both the progress and challenges of radionuclide therapy employed in breast cancer treatment.

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Heterogeneity of dose distribution in normal tissues in case of radiopharmaceutical therapy with alpha-emitting radionuclides

Cited by 12 publications

References 77 publications

Out-of-field effects: lessons learned from partial body exposure

Out-of-field effects: lessons learned from partial body exposure

PBPK-Adapted Deep Learning for Pretherapy Prediction of Voxelwise Dosimetry: In-Silico Proof of Concept

The application of radionuclide therapy for breast cancer

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