Current Radiopharmaceuticals - Volume 18, Issue 3, 2025

In the context of modern oncology, radiogenic elements have emerged as pivotal tools for targeted cancer therapies. Elements like Iodine-131 and Yttrium-90 offer unique radiological properties that allow precise treatment delivery. This article explores their growing importance and potential in reshaping the landscape of cancer therapy.

Utilizing a systematic literature search, relevant studies, clinical trials, and research articles were collected from databases. The selected material was scrutinized to extract insights into the mechanisms, applications, advantages, and challenges of radiogenic elements. These results are combined in the study to give a perceptive picture of how contemporary oncology treatment is developing.

The article reveals a comprehensive analysis of the outcomes derived from the study of radiogenic elements in contemporary cancer treatment. The results highlight the diverse applications of radionuclides like Iodine-131, Yttrium-90, and actinides in targeted therapies. It showcases their ability to selectively damage cancer cells while sparing healthy tissues, emphasizing precision and efficacy. The review underscores the increasing importance of personalized medicine, combination therapies, and the potential of emerging alpha-particle-based treatments. Furthermore, the results shed light on the challenges posed by radiation safety and potential side effects, prompting a need for vigilant management. This comprehensive examination of results provides a nuanced understanding of the pivotal role that radiogenic elements play in shaping the future of modern oncology therapy.

The article examines the role of radiogenic elements in contemporary cancer treatment. It highlights the significance of elements like 131I, 90Y, and actinides in targeted therapies, discussing their mechanisms and applications. The article emphasizes personalized medicine, combination therapies, and emerging alpha-particle-based treatments. Challenges, including radiation safety and side effects, are also addressed. The review anticipates a promising future where radiogenic elements contribute to precise, effective, and patient-centered cancer care.

This study intended to compare the radiation dose estimates to target and non-target liver compartments from ^99mTc-MAA SPECT/CT and ⁹⁰Y-PET/MR scans in liver tumors treated by ⁹⁰Y-glass microspheres.

Dose estimation was performed for twenty-three eligible patients (13M, 10F) after ^99mTc-MAA simulation using SPECT/CT imaging, and over ⁹⁰Y-PET/MR images after ⁹⁰Y-microsphere therapy. Simplicit90Y™ software was used for voxel-based dosimetry over the liver parenchyma. Dose estimates were obtained for whole healthy liver (HL), healthy injected liver (HIL), and tumor volumes. Pearson correlation, Bland–Altman plot, and Wilcoxon signed-ranks test were used for statistical analysis.

The mean tumor dose was 270 ± 111 Gy, the whole liver parenchyma dose was 26 ± 12 Gy, and the healthy injected liver dose was 55 ± 18 Gy from ^99mTc-MAA simulation. ⁹⁰Y-PET/MR dosimetry yielded a mean tumor dose of 271 ± 125 Gy, a HIL mean dose of 54±18 Gy, and a liver parenchyma dose of 25 ± 12 Gy. An excellent agreement was demonstrated between tumor doses (R2=0.90) and liver doses (R2=0.87), while the agreement was less for HIL doses (R2=0.80). Wilcoxon signed-ranks test yielded no significant difference between the dose estimates for all liver compartments.

It was deduced that ^99mTc-MAA SPECT/CT simulation provides valuable dose prediction in ⁹⁰Y-glass microsphere therapy. Despite the difference in volume measurements and dose estimates with ⁹⁰Y-PET/MR, the predictive value of the ^99mTc-MAA simulation was not affected.

Radiation-induced damage to the hematopoietic and gastrointestinal systems, especially the intestine, is a major concern for individuals exposed to whole-body radiation during an accident. Resveratrol has shown potential in mitigating radiation-induced toxicity, but its efficacy may be limited by its low bioavailability. In this study, we aimed to evaluate the effectiveness of resveratrol-loaded polymeric-based nanocapsules in mitigating radiation-induced injury in the hematopoietic system and intestine after whole-body exposure to radiation.

Sixty male mice were randomly divided into four groups: control, radiation (single dose of 7.2 Gy of X-ray) only, resveratrol-loaded polymeric-based nanocapsules (RES-ACN) only, and radiation plus RES-ACN. Mice were exposed to a single dose of 7.2 Gy of X-ray radiation. RES-ACN was administered to the mice starting 24 h after irradiation up to day 7 post-irradiation. Then, blood and tissue samples were collected for complete blood count and histopathological and biochemical evaluation. Survival analyses were also conducted.

The findings showed that RES-ACN significantly mitigated radiation-induced injury to the hematopoietic system and intestine. The histopathological evaluation showed the mitigation of villi shortening, inflammation, and mucous layer thickness following treatment with RES-ACN. Biochemical evaluation also demonstrated a significant increase in the activity of glutathione peroxidase and superoxide dismutase and a significant reduction in the concentrations of malondialdehyde and nitric oxide. Treatment with RES-ACN also showed a significant improvement in some of the blood parameters and increased survival compared to radiation only.

The findings suggest that resveratrol-loaded polymeric-based nanocapsules can be an effective approach to mitigate radiation-induced damage to the hematopoietic system and intestine after whole-body exposure to X-ray radiation in mice. Further research is needed to explore the optimal dose and timing of resveratrol administration and to investigate the potential for clinical translation of this approach.

A promising material used in radiation synovectomy of small joints is hydroxyapatite, labeled with ¹⁷⁷Lu. During the design and production of radiopharmaceuticals, the condition of the radiolabeling process directly influences the radiochemical yield and consequently the quality of the final product so this process necessitates precise optimization.

In this investigation, a central composite design based on response surface methodology and artificial neural networks modeling coupled with genetic algorithm technique is applied to build predictive models and explore key parameters' effect in hydroxyapatite's radiolabeling process with ¹⁷⁷Lu radionuclide. The variables that directly affected the labeling reaction were the initial ¹⁷⁷Lu radioactivity, pH, radiolabeling reaction time, and temperature.

Based on the validation data set, the statistical values demonstrate that the artificial neural networks model performs better than the response surface methodology model. The artificial neural networks model has a small mean squared error (9.08 artificial neural networks < 12.36 response surface methodology) and a high coefficient of determination (R²: 0.99 artificial neural networks > 0.93 response surface methodology). The optimum conditions to achieve maximum radiochemical yield based on response surface methodology using artificial neural networks modeling coupled with genetic algorithm were at the initial radioactivity of ¹⁷⁷Lu radionuclide = 0.082 Gigabecquerel (GBq), pH = 6.75, time= 22 (min), and temperature = 37.8 (℃).

The ability to generate more data with fewer experiments for optimization and improved production is a pertinent advantage of multivariate optimization methods over traditional methods in radiation-related activities. The central composite design and artificial neural network-genetic algorithm optimization approaches are successfully utilized to create prediction models and investigate the impact of critical variables in the radiolabeling of hydroxyapatite with ¹⁷⁷Lu radionuclide.

Many studies have reported translocator protein (TSPO) overexpression in various neurological disorders. Carbon-11[¹¹C]PBR28 is a widely used TSPO Positron Emission Tomography (PET) radiopharmaceutical. We compared HPLC-based purification with cartridge-based purification and performed PET-MR imaging in ALS patients.

[¹¹C]PBR28 was synthesized using both an HPLC-based and cartridge-based purification technique on the FX2C chemistry module. We injected 350 ± 20 MBq of the [¹¹C]PBR28 intravenously into the patients diagnosed with amyotrophic lateral syndrome (ALS) limb onset (n =3) and bulbar (n =3). Simultaneous PET-MR dynamic imaging was then performed.

The radiochemical purity exceeded 95% with both methods. Using the HPLC-based method, the radiochemical yield was 11.8 ± 3.3%, molar activity was 253 ± 20.9 GBq/μmol, and the total synthesis time of 25 ± 2 minutes. In contrast, the cartridge-based method yielded a radiochemical yield of 53.0 ± 3.6%, a molar activity of 885 ± 17.7 GBq/μmol, and a total synthesis time of 12 ± 2 minutes. In imaging results, higher activity was observed in the precentral gyrus and cerebellum at 2.5 ± 0.5 minutes in bulbar-onset ALS cases, with a standardized uptake value (SUV) of 2.3 ± 0.3. In contrast, limb-onset ALS cases showed the highest uptake at 0.5 ± 0.2 minutes, with an SUV of 1.5 ± 0.2.

The difference in SUV in bulbar and limb onset may be due to pathological changes.

The cartridge-based purification method provided higher radiochemical yield and molar activity as compared to the HPLC purification method.

Nasopharyngeal Carcinoma (NPC) exhibits high incidence in southern China. Despite improved survival with intensity-modulated radiotherapy (IMRT), 10%-20% of patients experience local recurrence. Traditional TNM staging fails to reflect tumor heterogeneity, necessitating robust recurrence prediction models. This study aimed to develop an MRI-based NPC recurrence prediction model by integrating radiomics, deep learning, and clinical features.

A total of 184 pathologically confirmed NPC patients receiving radical radiotherapy were included. After propensity score matching (1:1), 136 cases were analyzed. Stacked denoising autoencoder (SDAE) extracted deep features from contrast-enhanced T1-weighted MRI. Radiomic features (morphology, texture, first-order statistics), clinical parameters (gender, age, TNM stage), and SDAE features were combined to construct 12 models using SVM, MLP, logistic regression (LR), and random forest (RF). Performance was evaluated via AUC, accuracy, sensitivity, and specificity, with external validation (91 cases).

Model 1 (radiomics + SDAE + clinical features + SVM) achieved the highest AUC (0.89, 95% CI: 0.84-0.93), accuracy (81.5%), sensitivity (67.3%), and specificity (97.9%). External validation showed AUC 0.83, sensitivity 88.9%, and specificity 78%. The DeLong test confirmed no significant AUC difference between internal and external cohorts (P >0.05).

The fusion of SDAE-enhanced features outperformed traditional radiomics. SVM demonstrated optimal performance in small samples, likely due to its high-dimensional feature handling and anti-overfitting capability. Limitations include single-center retrospective design and lack of functional imaging (DWI/PET) or molecular markers (EBV-DNA). Future multicenter prospective studies and multimodal data integration are warranted to enhance biological interpretability and clinical utility.

This model provides a tool for early recurrence risk stratification and personalized therapy optimization, advancing precision medicine in NPC management.