Background: Quantum Neural Networks (QNNs) combine quantum computing and artificial intelligence to provide powerful solutions for high-dimensional data analysis. In magnetic resonance imaging (MRI), they address the challenges of advanced imaging sequences and data complexity, enabling faster optimization, enhanced feature extraction, and real-time clinical applications. Methods: A literature review using Scopus, PubMed, IEEE Xplore, ACM Digital Library and arXiv identified 84 studies on QNNs in MRI. After filtering for peer-reviewed original research, 20 studies were analyzed. Key parameters such as datasets, architectures, hardware, tasks, and performance metrics were summarized to highlight trends and gaps. Results: The analysis identified datasets supporting tasks like tumor classification, segmentation, and disease prediction. Architectures included hybrid models (e.g., ResNet34 with quantum circuits) and novel approaches (e.g., Quantum Chebyshev Polynomials). Hardware ranged from high-performance GPUs to quantum-specific devices. Performance varied, with accuracy up to 99.5% in some configurations but lower results for complex or limited datasets. Conclusions: The findings provide the first glimpse into the potential of QNNs in MRI, demonstrating accuracy and specificity in diagnostic tasks and biomarker detection. However, challenges such as dataset variability, limited quantum hardware access, and reliance on simulators remain. Future research should focus on scalable quantum hardware, standardized datasets, and optimized architectures to support clinical applications and precision medicine.

Introduction: Interventional radiotherapy (brachytherapy, IRT, BT) has evolved with technological advancements, improving dose precision while minimizing exposure to healthy tissues. The integration of 3D-printing technology in IRT has enabled the development of patient-specific devices, optimizing treatment personalization and dosimetric accuracy. Methods: A systematic literature search was conducted in PubMed, Scopus, and Google Scholar to identify studies published between 2020 and 2024 on 3D-printing applications in IRT. The selection process resulted in 74 peer-reviewed articles categorized by radioactive source, brachytherapy technique, endpoint of the 3D-printed product, and study type. Results: The analysis highlights the growing implementation of 3D-printed devices in brachytherapy, particularly in gynecological, prostate, and skin cancers. Most studies focus on technique, including intracavitary, interstitial, and contact applications, with custom applicators and templates emerging as predominant endpoints. The majority of studies involved in vivo clinical applications, followed by in silico computational modeling and in vitro experiments. Conclusions: The upward trend in scientific publications underscores the growing attention on 3D printing for enhancing personalized brachytherapy. The increasing use of 3D-printed templates and applicators highlights their role in optimizing dose delivery and expanding personalized treatment strategies. The current research trend is shifting toward real-world data and in vivo studies to assess clinical applications, ensuring these innovations translate effectively into routine practice. The integration of 3D printing represents a major advancement in radiation oncology, with the potential to enhance treatment efficacy and patient outcomes. Future research should focus on standardizing manufacturing processes and expanding clinical validation to facilitate broader adoption.

Background/Objectives: The anatomical complexity of the auricular region poses a unique challenge for contact interventional radiotherapy (IRT, modern brachytherapy), especially in maintaining close conformity between the applicator and skin surface. Air gaps can arise due to the irregular shape of the ear, potentially compromising dose coverage. This study evaluates the dosimetric impact of air gaps in HDR IRT for non-melanoma skin cancer (NMSC) of the ear. Methods: Ten patients treated with contact IRT using alginate as supporting material were retrospectively analyzed. Treatment plans were recalculated using both the TG-43 and the TG-186 formalism. CTV coverage and organ-at-risk dose parameters were evaluated within the two formalisms. Results: CTV coverage was comparable between algorithms (mean V95% 96.2% vs. 94.4%, V100% 89.6% vs. 86.7%, and V150% 2.6% vs. 2.5% for TG-43 vs. TG-186; p > 0.05), while the ipsilateral eye D2cc decreased from 4.0% (TG-43) to 3.2% (TG-186). In silico simulations showed that increasing air gaps reduced skin dose progressively (up to ~15% at 5 mm), whereas alginate thickness produced only a mild dose increase (<5%) across the tested range. Overall, small air pockets (<1 mm) did not substantially alter global dosimetric metrics, although local underdosage may occur at gap locations. Conclusions: This study underscores the importance of accounting for material heterogeneities and geometric uncertainties in anatomically complex regions through advanced dose calculation algorithms.

Background/Objectives: This study investigates the dosimetric impact of a 3D-printed applicator integrating multilayer catheter geometry and high-density shielding, designed for contact interventional radiotherapy (IRT) in non-melanoma skin cancer (NMSC) treatment. The aim is to assess its potential to enhance target coverage and reduce doses in organs at risk (OARs). Methods: A virtual prototype of a multilayer applicator was designed using 3D modeling software and realized through fused deposition modeling. Dosimetric simulations were performed using both TG-43 and TG-186 formalisms on CT scans of a water-equivalent phantom. A five-catheter array was reconstructed, and lead-cadmium-based alloy shielding of varying thicknesses (3–15 mm) was contoured. CTVs of 5 mm and 8 mm thickness were analyzed along with a neighboring OAR. Dosimetric endpoints included V95%, V100%, V150% (CTV), D_2cc (OAR), and therapeutic window (TW). Results: Compared to TG-43, the TG-186 algorithm yielded lower OAR doses while maintaining comparable CTV coverage. Progressive increase in shielding thickness led to improved V95% and V100% values and a notable reduction in OAR dose, with an optimal trade-off observed between 6 and 9 mm of shielding. The TW remained above 7 mm across all configurations, supporting its use in lesions thicker than conventional guidelines recommend. Conclusions: The integration of multilayer catheter geometry with high-density shielding in a customizable 3D-printed applicator enables enhanced dose modulation and OAR sparing in superficial IRT. This approach represents a step toward personalized brachytherapy, aligning with the broader movement in radiation oncology toward patient-specific solutions, adaptive planning, and precision medicine. Future directions should include prototyping and mechanical testing of the applicator, experimental dosimetric validation in phantoms, and pilot clinical feasibility studies to translate these promising in silico results into clinical practice.

Background: Tumor hypoxia is a major determinant of radioresistance, limiting oxygen-mediated fixation of radiation-induced DNA damage and promoting metabolic adaptation, tumor aggressiveness, and treatment failure. Nitroimidazole derivatives and related hypoxia-targeting compounds have been investigated as radiosensitizers because of their oxygen-mimetic properties and selective activation under low-oxygen conditions. Methods: A structured narrative review was conducted to evaluate recent evidence on hypoxia-targeting strategies and nitroimidazole-based radiosensitizers combined with radiotherapy. PubMed and Scopus were searched in March 2026 using terms related to radiotherapy and nitroimidazoles. Studies published within the last five years were included if they investigated hypoxia-targeting compounds, radiosensitization strategies, or dose–response relationships relevant to radiotherapy. Preclinical, computational, and clinical studies were considered. Results: Sixteen studies were included, comprising preclinical, computational, and clinical investigations. Most studies were preclinical and evaluated in vitro cell lines, murine tumor models, or xenografts across multiple tumor types, including head and neck cancer, glioblastoma, breast cancer, colorectal cancer, cervical cancer, renal carcinoma, pancreatic cancer, and ovarian-related models. Nitroimidazole-based and related hypoxia-targeting strategies generally enhanced radiation response under hypoxic conditions, with sensitizer enhancement ratio values ranging from approximately 1.09 to 1.65. In vivo studies reported reductions in tumor growth or tumor volume when radiosensitizers were combined with radiotherapy. Clinical evidence, mainly in head and neck squamous cell carcinoma, showed variable effects on locoregional outcomes. Conclusions: Current evidence supports the biological relevance of hypoxia-targeting strategies for improving radiotherapy response. Nitroimidazole-based compounds show consistent radiosensitizing effects in preclinical models, but clinical translation remains heterogeneous. Future studies should integrate hypoxia imaging, standardized endpoints, advanced drug delivery systems, and clinically relevant radiotherapy models to better define their role in modern radiation oncology.

Background: High-dose-rate interventional radiotherapy (HDR IRT) relies on accurate dose calculations to ensure safe and effective treatment. The AAPM TG-43 formalism, based on homogeneous water assumptions, has long been the clinical standard, but the growing use of patient-specific applicators and heterogeneous materials challenges its accuracy. Materials and Methods: A literature narrative review was performed using PubMed and Scopus to identify studies comparing TG-43 and model-based dose calculation algorithms (MBDCAs), including deterministic methods (ACE, Acuros BV) and Monte Carlo simulations. Thirty-five studies were selected and qualitatively analyzed. Results: TG-43 yielded higher dose compared with MBDCAs, particularly in the presence of tissue heterogeneities, air gaps, shielding materials, and limited scatter conditions. Differences ranged from 2 to 5% in relatively homogeneous settings to more than 10–20% in complex geometries such as superficial mould treatments and head-and-neck IRT. Model-based approaches showed better agreement with Monte Carlo simulations and experimental measurements, especially in contact HDR IRT and applications involving 3D-printed applicators. Conclusion: While TG-43 remains clinically established and widely used, its limitations are increasingly evident in modern personalized HDR IRT. Model-based dose calculation algorithms provide greater dosimetric accuracy and should be progressively integrated into clinical practice, particularly in treatments involving significant heterogeneities.