Preclinical Voxel-Based Dosimetry in Theranostics: a Review

Development of Alginate-Based Biodegradable Radioactive Microspheres Labeled with Positron Emitter through Click Chemistry Reaction: Stability and PET Imaging Study

Biodegradable radioactive microspheres labeled with positron emitters hold significant promise for diagnostic and therapeutic applications in cancers and other diseases, including arthritis. The alginate-based polymeric microspheres offer advantages such as biocompatibility, biodegradability, and improved stability, making them suitable for clinical applications. In this study, we developed novel positron emission tomography (PET) microspheres using alginate biopolymer radiolabeled with gallium-68 (68Ga) through a straightforward conjugation reaction. Polyethylenimine (PEI)-decorated calcium alginate microspheres (PEI-CAMSs) were fabricated and further modified using azadibenzocyclooctyne-N-hydroxysuccinimide ester (ADIBO-NHS). Subsequently, azide-functionalized NOTA chelator (N3-NOTA) was labeled with [68Ga]Ga to obtain [68Ga]Ga-NOTA-N3, which was then reacted with the surface-modified PEI-CAMSs using strain-promoted alkyne-azide cycloaddition (SPAAC) reaction to develop [68Ga]Ga-NOTA-PEI-CAMSs, a novel PET microsphere. The radiolabeling efficiency and radiochemical stability of [68Ga]Ga-NOTA-PEI-CAMSs were determined using the radio-instant thin-layer chromatography-silica gel (radio-ITLC-SG) method. The in vivo PET images were also acquired to study the in vivo stability of the radiolabeled microspheres in normal mice. The radiolabeling efficiency of [68Ga]Ga-NOTA-PEI-CAMSs was over 99%, and the microspheres exhibited high stability (92%) in human blood serum. PET images demonstrated the stability and biodistribution of the microspheres in mice for up to 2 h post injection. This study highlights the potential of biodegradable PET microspheres for preoperative imaging and targeted radionuclide therapy. Overall, the straightforward synthesis method and efficient radiolabeling technique provide a promising platform for the development of theranostic microspheres using other radionuclides such as 90Y, 177Lu, 188Re, and 64Cu.

Navigating the landscape of theranostics in nuclear medicine: current practice and future prospects

Theranostics refers to the combination of diagnostic biomarkers with therapeutic agents that share a specific target expressed by diseased cells and tissues. Nuclear medicine is an exciting component explored for its applicability in theranostic concepts in clinical and research investigations. Nuclear theranostics is based on the employment of radioactive compounds delivering ionizing radiation to diagnose and manage certain diseases employing binding with specifically expressed targets. In the realm of personalized medicine, nuclear theranostics stands as a beacon of potential, potentially revolutionizing disease management. Studies exploring the theranostic profile of radioactive compounds have been presented in this review along with a detailed explanation of radioactive compounds and their theranostic applicability in several diseases. It furnishes insights into their applicability across diverse diseases, elucidating the intricate interplay between these compounds and disease pathologies. Light is shed on the important milestones of nuclear theranostics beginning with radioiodine therapy in thyroid carcinomas, MIBG labelled with iodine in neuroblastoma, and several others. Our perspectives have been put forth regarding the most important theranostic agents along with emerging trends and prospects.

The heterobivalent (SSTR2/albumin) radioligand [67Cu]Cu-NODAGA-cLAB4-TATE enables efficient somatostatin receptor radionuclide theranostics

Somatostatin type 2 receptor (SSTR2) radionuclide therapy using β- particle-emitting radioligands has entered clinical practice for the treatment of neuroendocrine neoplasms (NENs). Despite the initial success of [177Lu]Lu‑DOTA-TATE, theranostic SSTR2 radioligands require improved pharmacokinetics and enhanced compatibility with alternative radionuclides. Consequently, this study evaluates the pharmacokinetic effects of the albumin-binding domain cLAB4 on theranostic performance of copper‑67-labeled NODAGA-TATE variants in an SSTR2-positive mouse pheochromocytoma (MPC) model. Methods: Binding, uptake, and release of radioligands as well as growth-inhibiting effects were characterized in cells grown as monolayers and spheroids. Tissue pharmacokinetics, absorbed tumor doses, and projected human organ doses were determined from quantitative SPECT imaging in a subcutaneous tumor allograft mouse model. Treatment effects on tumor growth, leukocyte numbers, and renal albumin excretion were assessed. Results: Both copper‑64- and copper‑67-labeled versions of NODAGA-TATE and NODAGA-cLAB4‑TATE showed similar SSTR2 binding affinity, but faster release from tumor cells compared to the clinical reference [177Lu]Lu‑DOTA-TATE. The bifunctional SSTR2/albumin-binding radioligand [67Cu]Cu‑NODAGA-cLAB4‑TATE showed both an improved uptake and prolonged residence time in tumors resulting in equivalent treatment efficacy to [177Lu]Lu‑DOTA-TATE. Absorbed doses were well tolerated in terms of leukocyte counts and kidney function. Conclusion: This preclinical study demonstrates therapeutic efficacy of [67Cu]Cu‑NODAGA-cLAB4‑TATE in SSTR2-positive tumors. As an intrinsic radionuclide theranostic agent, the radioligand provides stable radiocopper complexes and high sensitivity in SPECT imaging for prospective determination and monitoring of therapeutic doses in vivo. Beyond that, copper‑64- and copper‑61-labeled versions offer possibilities for pre- and post-therapeutic PET. Therefore, NODAGA-cLAB4-TATE has the potential to advance clinical use of radiocopper in SSTR2-targeted cancer theranostics.

Simplified image-based dosimetry using planar images and patient-specific S-values

BACKGROUND

Single time point measurement approach and hybrid dosimetry were proposed to simplify the dosimetry process. It is anticipated that utilizing patient-specific S-value would enable more accurate dosimetry assessment based on imaging compared to using the conventional MIRD S-values.

PURPOSE

We performed planar image-based dosimetry scaled with a single SPECT image for the entire treatment cycle using patient-specific S-values (PSS dosimetry) of organs. PSS dosimetry could further simplify the dosimetry procedure compared with a conventional 2D planar/3D SPECT hybrid dosimetry, as PSS dosimetry requires only one SPECT/CT image for the treatment of the entire cycle, whereas the conventional hybrid dosimetry requires a SPECT/CT image for each treatment cycle.

METHODS

177Lu-DOTATATE SPECT/CT and planar image datasets acquired from Seoul National University Hospital (SNUH, Seoul, Republic of Korea) were utilized for the evaluation. Images were acquired 4, 24, 48, and 120 h after patients' intravenous injection of 177Lu-DOTATATE. Dose estimations based on a Monte Carlo (MC) simulation using the Geant4 Application for Emission Tomography (GATE) (v.8.2) were considered as the reference. Planar image-based dosimetry scaled with a single SPECT image was performed using the patient-specific S-value (PSS). Briefly, the CT image was considered as the patient's anatomical reference and PSSs were quantified using the multiple voxel S-value (VSV) method. Then, PSS dosimetry was performed by obtaining activity information from sequential planar images and a scaling factor derived from a single SPECT/planar image pair. Hybrid dosimetry using sequential planar images and a single SPECT image was performed for comparison. The absorbed doses of the kidneys, bone marrow (BM) in the lumbar spine, liver, and spleen calculated using the PSS and hybrid dosimetries were compared with the reference MC results.

RESULTS

The mean differences (MDs) of the self-absorption S-values between S-value of OLINDA/EXM and PSS for the kidneys, liver, and spleen were -0.04%, -2.39%, and -2.62%, respectively. However, the differences in the self-absorption S-values were significantly higher for the BM (84.99%) and the remainder of the body (ROB) (280.84%). The absorbed doses estimated by the PSS and hybrid dosimetries showed relatively high errors compared with MC simulation result, regardless of the organ. In contrast, the PSS and hybrid dosimetries produced similar dose estimates. For the entire cycles of the treatment, the MDs of absorbed doses between PSS and hybrid dosimetries were -3.31%, -6.04%, 3.37%, and -2.17% for the kidneys, BM, liver, and spleen, respectively. Through a correlation analysis and the Wilcoxon signed-rank test, we concluded that there was no significant difference between the results obtained by the two dosimetry methods.

CONCLUSIONS

As the PSS was derived using CT images with actual anatomical information and organ-specific volume of interest (VOI), PSS dosimetry provided reliable results. PSS dosimetry was robust in estimating the absorbed dose for the later treatment cycles. Therefore, PSS dosimetry outperformed hybrid dosimetry in terms of dose estimation for a greater number of treatment cycles.

The Effects of Radiation Dose Heterogeneity on the Tumor Microenvironment and Anti-Tumor Immunity

Radiotherapy elicits dose- and lineage-dependent effects on immune cell survival, migration, activation, and proliferation in targeted tumor microenvironments. Radiation also stimulates phenotypic changes that modulate the immune susceptibility of tumor cells. This has raised interest in using radiotherapy to promote greater response to immunotherapies. To clarify the potential of such combinations, it is critical to understand how best to administer radiation therapy to achieve activation of desired immunologic mechanisms. In considering the multifaceted process of priming and propagating anti-tumor immune response, radiation dose heterogeneity emerges as a potential means for simultaneously engaging diverse dose-dependent effects in a single tumor environment. Recent work in spatially fractionated external beam radiation therapy demonstrates the expansive immune responses achievable when a range of high to low dose radiation is delivered in a tumor. Brachytherapy and radiopharmaceutical therapies deliver inherently heterogeneous distributions of radiation that may contribute to immunogenicity. This review evaluates the interplay of radiation dose and anti-tumor immune response and explores emerging methodological approaches for investigating the effects of heterogeneous dose distribution on immune responses.

Is There a Role of Artificial Intelligence in Preclinical Imaging?

This review provides an overview of the current opportunities for integrating artificial intelligence methods into the field of preclinical imaging research in nuclear medicine. The growing demand for imaging agents and therapeutics that are adapted to specific tumor phenotypes can be excellently served by the evolving multiple capabilities of molecular imaging and theranostics. However, the increasing demand for rapid development of novel, specific radioligands with minimal side effects that excel in diagnostic imaging and achieve significant therapeutic effects requires a challenging preclinical pipeline: from target identification through chemical, physical, and biological development to the conduct of clinical trials, coupled with dosimetry and various pre, interim, and post-treatment staging images to create a translational feedback loop for evaluating the efficacy of diagnostic or therapeutic ligands. In virtually all areas of this pipeline, the use of artificial intelligence and in particular deep-learning systems such as neural networks could not only address the above-mentioned challenges, but also provide insights that would not have been possible without their use. In the future, we expect that not only the clinical aspects of nuclear medicine will be supported by artificial intelligence, but that there will also be a general shift toward artificial intelligence-assisted in silico research that will address the increasingly complex nature of identifying targets for cancer patients and developing radioligands.

An Overview of In Vitro Assays of 64Cu-, 68Ga-, 125I-, and 99mTc-Labelled Radiopharmaceuticals Using Radiometric Counters in the Era of Radiotheranostics

Radionuclides are unstable isotopes that mainly emit alpha (α), beta (β) or gamma (γ) radiation through radiation decay. Therefore, they are used in the biomedical field to label biomolecules or drugs for diagnostic imaging applications, such as positron emission tomography (PET) and/or single-photon emission computed tomography (SPECT). A growing field of research is the development of new radiopharmaceuticals for use in cancer treatments. Preclinical studies are the gold standard for translational research. Specifically, in vitro radiopharmaceutical studies are based on the use of radiopharmaceuticals directly on cells. To date, radiometric β- and γ-counters are the only tools able to assess a preclinical in vitro assay with the aim of estimating uptake, retention, and release parameters, including time- and dose-dependent cytotoxicity and kinetic parameters. This review has been designed for researchers, such as biologists and biotechnologists, who would like to approach the radiobiology field and conduct in vitro assays for cellular radioactivity evaluations using radiometric counters. To demonstrate the importance of in vitro radiopharmaceutical assays using radiometric counters with a view to radiogenomics, many studies based on ⁶⁴Cu-, ⁶⁸Ga-, ¹²⁵I-, and ^99mTc-labeled radiopharmaceuticals have been revised and summarized in this manuscript.

Absorbed dose calculation for a realistic CT-derived mouse phantom irradiated with a standard Cs-137 cell irradiator using a Monte Carlo method

Computed tomography (CT) derived Monte Carlo (MC) phantoms allow dose determination within small animal models that is not feasible with in-vivo dosimetry. The aim of this study was to develop a CT-derived MC phantom generated from a mouse with a xenograft tumour that could then be used to calculate both the dose heterogeneity in the tumour volume and out of field scattered dose for pre-clinical small animal irradiation experiments. A BEAMnrc Monte-Carlo model has been built of our irradiation system that comprises a lead collimator with a 1 cm diameter aperture fitted to a Cs-137 gamma irradiator. The MC model of the irradiation system was validated by comparing the calculated dose results with dosimetric film measurement in a polymethyl methacrylate (PMMA) phantom using a 1D gamma-index analysis. Dose distributions in the MC mouse phantom were calculated and visualized on the CT-image data. Dose volume histograms (DVHs) were generated for the tumour and organs at risk (OARs). The effect of the xenographic tumour volume on the scattered out of field dose was also investigated. The defined gamma index analysis criteria were met, indicating that our MC simulation is a valid model for MC mouse phantom dose calculations. MC dose calculations showed a maximum out of field dose to the mouse of 7% of Dmax. Absorbed dose to the tumour varies in the range 60%-100% of Dmax. DVH analysis demonstrated that tumour received an inhomogeneous dose of 12 Gy-20 Gy (for 20 Gy prescribed dose) while out of field doses to all OARs were minimized (1.29 Gy-1.38 Gy). Variation of the xenographic tumour volume exhibited no significant effect on the out of field scattered dose to OARs. The CT derived MC mouse model presented here is a useful tool for tumour dose verifications as well as investigating the doses to normal tissue (in out of field) for preclinical radiobiological research.

A Review on Tumor Control Probability (TCP) and Preclinical Dosimetry in Targeted Radionuclide Therapy (TRT)

Targeted radionuclide therapy (TRT) uses radiopharmaceuticals to specifically irradiate tumor cells while sparing healthy tissue. Response to this treatment highly depends on the absorbed dose. Tumor control probability (TCP) models aim to predict the tumor response based on the absorbed dose by taking into account the different characteristics of TRT. For instance, TRT employs radiation with a high linear energy transfer (LET), which results in an increased effectiveness. Furthermore, a heterogeneous radiopharmaceutical distribution could result in a heterogeneous dose distribution at a tissue, cellular as well as subcellular level, which will generally reduce the tumor response. Finally, the dose rate in TRT is protracted, relatively low, and variable over time. This allows cells to repair more DNA damage, which may reduce the effectiveness of TRT. Within this review, an overview is given on how these characteristics can be included in TCP models, while some experimental findings are also discussed. Many parameters in TCP models are preclinically determined and TCP models also play a role in the preclinical stage of radiopharmaceutical development; however, this all depends critically on the calculated absorbed dose. Accordingly, an overview of the existing preclinical dosimetry methods is given, together with their limitation and applications. It can be concluded that although the theoretical extension of TCP models from external beam radiotherapy towards TRT has been established quite well, the experimental confirmation is lacking. Thus, requiring additional comprehensive studies at the sub-cellular, cellular, and organ level, which should be provided with accurate preclinical dosimetry.

Individualization of Radionuclide Therapies: Challenges and Prospects

Simple Summary

Currently, patient-specific treatment plans and dosimetry calculations are not routinely performed for radionuclide therapies. In external beam radiotherapy, it is quite the opposite. As a result, a small fraction of patients receives optimal radioactivity. This conservative approach provides “radiation safety” to healthy tissues but delivers a lower than indicated absorbed dose to the tumors, resulting in a lower response rate and a higher disease relapse rate. Evidence shows that better and more predictable outcomes can be achieved with patient-individualized dose assessment. Therefore, the incorporation of individual planning into radionuclide therapies is a high priority for nuclear medicine physicians and medical physicists alike. Internal dosimetry is used in tumor therapy to optimize the absorbed dose to the target tissue. The main reasons for the difficulties in incorporating patients’ internal dosimetry into routine clinical practice are discussed. The article presents the prospects for the routine implementation of personalized radionuclide therapies.

Abstract

The article presents the problems of clinical implementation of personalized radioisotope therapy. The use of radioactive drugs in the treatment of malignant and benign diseases is rapidly expanding. Currently, in the majority of nuclear medicine departments worldwide, patients receive standard activities of therapeutic radiopharmaceuticals. Intensively conducted clinical trials constantly provide more evidence of a close relationship between the dose of radiopharmaceutical absorbed in pathological tissues and the therapeutic effect of radioisotope therapy. Due to the lack of individual internal dosimetry (based on the quantitative analysis of a series of diagnostic images) before or during the treatment, only a small fraction of patients receives optimal radioactivity. The vast majority of patients receive too-low doses of ionizing radiation to the target tissues. This conservative approach provides “radiation safety” to healthy tissues, but also delivers lower radiopharmaceutical activity to the neoplastic tissue, resulting in a low level of response and a higher relapse rate. The article presents information on the currently used radionuclides in individual radioisotope therapies and on radionuclides newly introduced to the therapeutic market. It discusses the causes of difficulties with the implementation of individualized radioisotope therapies as well as possible changes in the current clinical situation.

A Brief History of Nuclear Medicine Physics, Instrumentation, and Data Sciences in Korea

We review the history of nuclear medicine physics, instrumentation, and data sciences in Korea to commemorate the 60^th anniversary of the Korean Society of Nuclear Medicine. In the 1970s and 1980s, the development of SPECT, nuclear stethoscope, and bone densitometry systems, as well as kidney and cardiac image analysis technology, marked the beginning of nuclear medicine physics and engineering in Korea. With the introduction of PET and cyclotron in Korea in 1994, nuclear medicine imaging research was further activated. With the support of large-scale government projects, the development of gamma camera, SPECT, and PET systems was carried out. Exploiting the use of PET scanners in conjunction with cyclotrons, extensive studies on myocardial blood flow quantification and brain image analysis were also actively pursued. In 2005, Korea's first domestic cyclotron succeeded in producing radioactive isotopes, and the cyclotron was provided to six universities and university hospitals, thereby facilitating the nationwide supply of PET radiopharmaceuticals. Since the late 2000s, research on PET/MRI has been actively conducted, and the advanced research results of Korean scientists in the fields of silicon photomultiplier PET and simultaneous PET/MRI have attracted significant attention from the academic community. Currently, Korean researchers are actively involved in endeavors to solve a variety of complex problems in nuclear medicine using artificial intelligence and deep learning technologies.

High through-plane resolution CT imaging with self-supervised deep learning

CT images for radiotherapy planning are usually acquired in thick slices to reduce the imaging dose, especially for pediatric patients, and to lessen the need for contouring and treatment planning on more slices. However, low through-plane resolution may degrade the accuracy of dose calculations. In this paper, a self-supervised deep learning workflow is proposed to synthesize high through-plane resolution CT images by learning from their high in-plane resolution features. The proposed workflow was designed to facilitate neural networks to learn the mapping from low-resolution (LR) to high-resolution (HR) images in the axial plane. During the inference step, the HR sagittal and coronal images were generated by feeding two parallel-trained neural networks with the respective LR sagittal and coronal images to the trained neural networks. The CT simulation images of a cohort of 75 patients with head and neck cancer (1 mm slice thickness) and 200 CT images of a cohort of 20 lung cancer patients (3 mm slice thickness) were retrospectively investigated in a cross-validation manner. The HR images generated with the proposed method were qualitatively (visual quality, image intensity profiles and preliminary observer study) and quantitatively (mean absolute error, edge keeping index, structural similarity index measurement, information fidelity criterion and visual information fidelity in pixel domain) inspected, while taking the original CT images of the head and neck and lung cancer patients as the reference. The qualitative results showed the capability of the proposed method for generating high through-plane resolution CT images with data from both groups of cancer patients. All the improvements in the measure metrics were confirmed to be statistically significant with paired two-samplet-test analysis. The innovative point of the work is that the proposed deep learning workflow for CT image generation with high through-plane resolution in radiotherapy is self-supervised, meaning that it does not rely on ground truth CT images to train the network. In addition, the assumption that the in-plane HR information can supervise the through-plane HR generation is confirmed. We hope that this will inspire more research on this topic to further improve the through-plane resolution of medical images.