Search Results (76)

MR-guided adaptive radiotherapy (ART) enables daily plan optimization for prostate cancer but is resource-intensive. This study evaluated dosimetric and clinical outcomes following transition from radiation oncologist (RO)-led to radiation therapist (RTT)-led MR-guided ART. All prostate cancer patients treated with MR-guided ART on a 1.5T MR-linac were retrospectively reviewed. Consecutive RO-led (September 2019–November 2021) and RTT-led (April 2022–October 2023) cohorts were compared, excluding the actual transition period. Toxicities (CTCAE v5.0), dose–volume metrics from daily adapted plans, target volume variation, and biochemical recurrence-free survival (BRFS) were analyzed. A total of 166 patients were included (78 RO-led, 88 RTT-led; median follow-up 40 and 35 months). Dosimetric differences between the cohorts were statistically small (<1%). Rates of G2+ GI adverse events were similar across all timepoints. An increase in on-treatment GU events was observed in the RTT-led cohort (G2+ 27% vs. 9%, G3 incidence n = 2 vs. n = 0), likely reflecting higher baseline urinary dysfunction; no post-treatment differences persisted. Early biochemical outcomes were comparable, with 36-month BRFS of 93.5% (RO-led) and 95.0% (RTT-led). RTT-led MR-guided ART achieved comparable dosimetric quality and early biochemical outcomes to RO-led workflows with adverse advents that resolved in the long term. With structured training and a mature practice setting, RTT-led ART represents a scalable model to support future adaptive radiotherapy practice. Full article

Background/Objectives: Although the Spinal Instability Neoplastic Score (SINS) is widely used to estimate spinal metastases fracture risk and guide decisions on stabilisation procedures, prior studies have demonstrated mixed results. Patients with the same score exhibit clinically heterogeneous outcomes, with some SINS criteria correlating less well with the estimated fracture risk than others. There are also barriers to implementation such as the time burden required for manual calculation and interobserver variability associated with qualitative morphological criteria. SINS also lacks sensitivity for detecting latent structural compromise in treatment-naive patients and those susceptible to the iatrogenic effects of stereotactic body radiation therapy. This review aims to evaluate emerging imaging, biomechanical, and microstructural markers with the potential to improve fracture risk stratification and prognostication for spinal oncology patients. Methods: We synthesise evidence across three innovative frontiers: (1) biomechanical modelling, including CT-derived finite element analysis and failure-load pattern models; (2) radiomics, utilizing radiomics features from radiological imaging to develop a predictive model; and (3) microstructural MRI biomarkers, exploring the translatability of the Vertebral Bone Quality score, fat fraction, and paraspinal muscle atrophy from osteoporosis to the metastatic spine. Results: Emerging biomechanical, radiomic and microstructural imaging markers show potential in addressing some limitations of traditional SINS criteria for fracture risk stratification across the spinal oncology treatment continuum, from initial diagnosis to post-radiation surveillance, thereby facilitating more precise risk assessment. However, current evidence remains largely retrospective and heterogeneous, and further validation is required before clinical adoption. Conclusions: We propose a framework that shifts the paradigm from conventional morphological scoring toward a multiparametric assessment of spinal stability. Full article

Background/Objectives: Traditional radiotherapy treatments assume that patient anatomy remains unchanged over the course of treatment. Image guidance is used to reproduce the patient setup as closely as possible, and planning margins are used to account for setup errors. With the development of MR-guided Adaptive Radiotherapy (MRgART), daily plan adaptations are now feasible, allowing clinicians to edit the plan according to daily anatomical fluctuations. However, MRgART is currently restricted to step-and-shoot IMRT delivery, which can have reduced dose conformality compared to VMAT. In this study, we compare the accumulated dose over all fractions in prostate SBRT treatments for non-adaptive and adaptive external beam workflows. Methods: The simulation and daily images of twenty previously treated MRgART prostate SBRT patients were anonymized. On each simulation image, whole prostate VMAT and MRgART SBRT plans were generated. To simulate non-adaptive treatment dose, the daily images were rigidly registered to the planning images, and the doses were recalculated on the daily images. The MRgART plans were adapted to the daily anatomy and reoptimized. All fractional doses were accumulated, using deformable image registration, and compared to their respective planned doses. Results: All MRgART dose accumulations were within clinical tolerance. Four VMAT dose accumulations had a dose constraint that fell outside of clinical tolerance. The VMAT dose accumulations had statistically lower doses to the target compared to their planned doses. Conclusions: While high-quality plans can be delivered in a non-adaptive VMAT workflow despite interfractional motion, this study suggests that MRgART produces cumulative dose distributions that more closely resemble the initial treatment plan. Full article

Background/Objectives: Magnetic nanoparticles have emerged as powerful tools for biomedical imaging, targeted drug delivery, and hyperthermia therapy. Magnetic particle imaging (MPI) is among the most promising technologies built around its properties: a radiation-free, quantitative tomographic modality that detects superparamagnetic iron oxide nanoparticles (SPIONs) directly against a biologically silent background. This review synthesizes MPI’s physical principles, nanoparticle design strategies, and preclinical applications within the broader landscape of magnetic material engineering for biomedical use. Methods: A systematic review was conducted covering MPI signal generation and image reconstruction, nanoparticle core synthesis and surface coating approaches, and preclinical applications, spanning cell tracking, oncological imaging, vascular perfusion, neuroimaging, and MPI-guided theranostics. Studies were selected to provide quantitative benchmarks and direct comparisons with competing modalities where available. Results: MPI delivers signal-to-background ratios above 1000:1, iron-mass linearity at R² ≥ 0.99, regardless of tissue depth, and acquisition rates up to 46 volumes per second. Tracer architecture—encompassing single-core particles, multicore nanoflowers, and stimuli-responsive cluster designs—is the primary determinant of sensitivity, environmental robustness, and theranostic capability. Preclinical results include detection of cell populations in the low thousands, earlier ischaemia identification than diffusion-weighted MRI, real-time drug release quantification, and spatially confined tumour hyperthermia. Three translational bottlenecks are identified: the absence of a clinically approved tracer with optimal relaxation dynamics, hardware performance losses when scaling to human-bore systems, and overestimation of passive tumour accumulation in murine models. Conclusions: MPI illustrates how progress in magnetic material design directly expands clinical imaging and theranostic possibilities. Successful translation will require indication-driven, interdisciplinary development that integrates materials science, scanner engineering, and regulatory strategy in parallel. Full article

Objective: Inadequate radiation delivery to recurrent pelvic and abdominal tumors is frequently attributable to the dose limitations of surrounding normal structures, particularly the intestines. Radiotherapy guided by magnetic resonance imaging (MRI) significantly enhances the accuracy of soft-tissue delineation. The purposes of this study were to demonstrate the feasibility and effectiveness of MR-Linac Adaptive stereotactic body radiotherapy in patients with pelvic–abdominal recurrent or metastatic gynecological malignancies with or without systemic therapies. Methods: Patients with pelvic–abdominal recurrent or metastatic gynecological malignancies are eligible for MR-Linac Adaptive stereotactic body radiotherapy. Systemic therapies, including chemotherapy, immunotherapy, and targeted therapy, are considered acceptable treatment options. The safety, tolerability, and efficacy of MR-Linac Adaptive stereotactic body radiotherapy were assessed. Results: Between October 2019 and May 2025, 15 patients were subjected to MR-Linac Adaptive stereotactic body radiotherapy. With a median follow-up period of 4.67 months (range, 0.73–20.10 months), the 6-month overall survival (OS), progression-free survival (PFS), and local control (LC) rates were 93.3%, 66.0%, and 92.3%, respectively. The 12-month OS, PFS, and LC rates were 83.8%, 37.7%, and 70.5%, respectively. The best objective response rate (ORR = CR + PR) for the irradiated lesions was 73.3% (11/15 patients). MR-Linac Adaptive stereotactic body radiotherapy led to objective responses in 73.3% (11/15) of the patients. As of the data cutoff (28 May 2025), one patient experienced dose-limiting toxicity (an enteric fistula). Another patient developed grade 4 thrombocytopenia during treatment; it was considered chemotherapy-induced. Conclusions: These findings suggest that MR-Linac Adaptive stereotactic body radiotherapy is relatively effective and safe and can be an important treatment option for patients with pelvic–abdominal recurrent or metastatic gynecological malignancies. MR-Linac Adaptive stereotactic body radiotherapy exhibited acceptable tolerability, promising efficacy, and a favorable local control rate with regard to heavily pretreated advanced solid tumors. Full article

Background: The generation of synthetic CT images from MRI scans represents a crucial step toward enabling MRI-only clinical workflows and supporting multi-modal integration in medical imaging, particularly in radiotherapy planning. Despite significant advancements in deep learning models, many current methods still struggle to reconstruct high-density structures, especially bone, and exhibit limited accuracy in density values. This shortcoming is largely attributed to the passage of excessive or noisy features through skip connections in the traditional U-Net architecture, which degrade the quality of information transmitted to the decoder, negatively impacting the clarity of anatomical boundaries and the pixel-wise accuracy of the resulting synthetic image. Methods: In this work, we propose an enhanced 3D U-Net architecture in which the Convolutional Block Attention Module (CBAM) is systematically integrated within each skip connection. The CBAM sequentially applies channel and spatial attention to adaptively reweight encoder feature maps before fusion with the decoder, thereby emphasizing anatomically relevant structures while suppressing irrelevant feature propagation. The model was trained and evaluated on the SynthRAD2023 (Task 1—Brain) MRI–CT dataset. To rigorously assess the contribution of the attention mechanism, a dedicated ablation study was conducted comparing three variants: 3D U-Net with Squeeze-and-Excitation (SE), Coordinate Attention (CA), and the proposed CBAM module. Performance was evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Normalized Cross-Correlation (NCC). Results: The ablation study demonstrated that the CBAM-enhanced model consistently outperformed both SE- and CA-based variants across all quantitative metrics. Specifically, the proposed method achieved an MAE of $38.2\pm 5.4$ HU and an RMSE of $51.0\pm 12.0$ HU, representing the lowest reconstruction errors among the evaluated models. In addition, it obtained a PSNR of $29.45\pm 2.10$ dB, SSIM of $0.940\pm 0.031$, and NCC of $0.967\pm 0.015$, indicating superior structural preservation and strong voxel-wise correspondence between synthesized and reference CT volumes. These results confirm that the sequential integration of channel and spatial attention provides a statistically and practically meaningful improvement for high-fidelity MRI-to-CT synthesis. Conclusions: Generating high-resolution brain CT images from brain MRI scans using a 3D U-Net network enhanced with a CBAM module can contribute to supporting the clinical workflow by providing additional diagnostic data without the need for extra radiological examinations, thereby enhancing diagnostic efficiency and reducing radiation exposure. This technique helps reduce patient exposure to radiation and improves accessibility in resource-limited settings. Furthermore, this method is valuable for retrospective studies, surgical planning, and image-guided therapy, where complete multi-modal data may not always be available. Full article

Background: In triple-negative breast cancer (TNBC), the addition of immunotherapy has significantly improved outcomes. Immune-related adverse events (irAEs) can be accelerated in patients with pre-existing autoimmune (AI) conditions. The treatment-response standardized protocol used in clinical care raises concerns about the need for right-sizing strategies. As the use of immunotherapy expands, recognizing toxicity from recurrence and optimizing response-adapted approaches are essential to balance cure with quality of survival. Case Presentation: A 38-year-old pregnant woman with a distant history of uveitis and psoriasis was discovered to have pregnancy-associated TNBC. Postnatally, she was treated with neoadjuvant chemotherapy and pembrolizumab, followed by wire-guided left breast wide local excision and sentinel lymph node biopsy of the left axilla. After surgery, residual cancer was noted. She continued adjuvant pembrolizumab and adjuvant radiotherapy 40.05 Gy/15 fr to the breast and nodes, followed by a 13.35 Gy/5 fr boost to the tumour bed (breast). Despite a persistent residual tumour, pembrolizumab was continued as per protocol in a response-agnostic manner. At the end of one year of adjuvant pembrolizumab, she developed progressive numbness and weakness in the ipsilateral arm, initially raising suspicion for local recurrence. Comprehensive MRI and PET-CT imaging did not identify recurrent tumour or new metastatic disease. Electromyography confirmed a lower-trunk brachial plexopathy without a structural cause. An immune-mediated process was diagnosed by a process of elimination. Despite treatment with 1st-line high-dose corticosteroids and 2nd-line intravenous immunoglobulin (IVIG), improvement was limited. Therapeutic plasmapheresis led to marked functional recovery and symptom resolution 20 months later. Discussion: Four main challenges are identified: (1) the diagnostic difficulty in identifying local recurrence or radiation injury from immune-related neuropathy; (2) the emerging therapeutic role of plasmapheresis in steroid-refractory irAEs; (3) the possible inconsistencies between rare toxicities observed in clinical trials vs. clinical practice; and (4) the limitations in response in adjuvant therapy, particularly in patients with coexisting AI conditions. Conclusions: Early recognition and accurate distinction from tumour recurrence, as well as support for plasmapheresis as a potential option in steroid-refractory presentations, have been shown to improve patient survival and symptom reduction. With increasing use of immunotherapy, real-world toxicity data, predictive biomarkers, and personalized treatment strategies are urgently needed to balance cure with long-term functional outcomes. Full article

Background: Glioblastoma (GBM) is a lethal, highly invasive, and recurrent brain tumor. Standard treatment combines maximal surgical resection, radiation, and chemotherapy; however, such approaches are often infeasible for tumors in eloquent brain regions. Objective: Histotripsy is a noninvasive, nonthermal ultrasound-based mechanical ablation modality that employs focused acoustic energy for targeted tissue destruction. This study aimed to investigate the feasibility, safety, and therapeutic effect of a one-time transcranial histotripsy treatment in a pre-clinical murine GBM model. Methods: GL261 GBM cells were orthotopically implanted into C56BL/6 mouse brains. Transcranial histotripsy was performed using a stereotactically guided 2 MHz transducer targeting either lower (25%) or higher (75%) tumor volume, with 5 or 10 pulses per location (PPL) administered. Tumor growth and cerebral injury were monitored with weekly magnetic resonance imaging (MRI) following treatment. At the study endpoint, hematoxylin and eosin (H&E) histology assessed residual tumor burden and histotripsy-induced tissue changes. Results: Mice receiving 5 PPL high-percent treatment (>30 sites) showed a statistically significant median survival extension of 5 days (18.5%) compared to untreated controls. MRI demonstrated marked tumor volume reduction in the high-percent treatment group at week 4, while H&E confirmed increased tumor necrosis and cellular damage in the treated cohort. Conclusions: Single-session, incisionless transcranial histotripsy was well tolerated and conferred mild yet meaningful survival advantages in this GBM model. These results support ongoing exploration of histotripsy, alone or in combination with existing therapies, for safe and effective treatment of challenging brain tumors. Full article

Background: Cardiotoxicity is a major limitation of chemotherapy and radiotherapy for thoracic and systemic cancers, contributing significantly to morbidity and mortality among survivors. Early prediction and prevention are critical to balance oncologic efficacy with cardiovascular safety. Artificial intelligence (AI) offers powerful tools to improve risk stratification, enable earlier detection of subclinical injury, and guide treatment planning in cardio-oncology. Methods: We performed a comprehensive review of the literature on AI applications for cancer therapy-related cardiotoxicity. Evidence was identified from PubMed, Scopus, and Web of Science, focusing on electrocardiography, biomarkers, proteomics, extracellular vesicles, genomics, advanced imaging (echocardiography, cardiac magnetic resonance, computed tomography, nuclear imaging), and radiotherapy dose modeling (dosiomics). Translational insights from animal models and in vitro systems were also included. Methodological quality was appraised with reference to TRIPOD-AI, PROBAST-AI, and CLAIM standards. Results: AI applications span multiple domains. Machine learning models integrating biomarkers, exosomes, and extracellular vesicles show promise for noninvasive early detection. Deep learning enables automated analysis of echocardiographic strain and cardiac MRI mapping, while radiomics and dosiomics approaches combine imaging with cardiac substructure dose maps to predict and prevent late radiation-induced injury. Preclinical studies demonstrate AI-driven advances in small-animal imaging, histopathology quantification, and multi-omics data integration, supporting the discovery of translational biomarkers. Despite encouraging performance, most models remain limited by small cohorts, methodological heterogeneity, and scarce external validation. Conclusions: AI has the potential to transform cardio-oncology by shifting from reactive detection to proactive prevention of cardiotoxicity. Future research should prioritize multimodal integration, harmonized multicenter datasets, prospective validation, and guideline-based clinical trials. As emerging data are incorporated, the field is expanding rapidly—dynamic, complex, and evolving. Full article

Background/Objectives: Existing literature reviews have not focused on the acute and late toxicities of non-magnetic resonance imaging (MRI)-guided adaptive radiation therapy (ART), compared the impacts of non-MRI-guided versus MRI-guided ART, or evaluated the effectiveness of adaptive conventional fractionated radiation therapy (CFRT) and stereotactic body radiation therapy (SBRT) in relation to toxicity in prostate cancer (PCa). The purpose of this scoping review was to systematically identify original articles and evaluate the impact of ART on toxicity in PCa in a comprehensive manner. Methods: A literature search was conducted using electronic databases on 17 June 2025, identifying 27 eligible papers. Results: The overall median toxicities of ART in PCa were 15.0% (acute grade 1 gastrointestinal (GI)), 1.0% (acute grade 2 GI), 0.0% (acute grade 3 GI), 47.1% (acute grade 1 genitourinary (GU)), 9.6% (acute grade 2 GU), 0.0% (acute grade 3 GU), 10.0% (late grade 1 GI), 2.0% (late grade 2 GI), 0.0% (late grade 3 GI), 29.7% (late grade 1 GU), 5.0% (late grade 2 GU), and 0.0% (late grade 3 GU). The choice of image guidance modality for ART does not appear to substantially influence toxicity; however, dedicated commercial ART systems may contribute to reducing toxicity to lower levels in PCa. Furthermore, the toxicity rates of adaptive CFRT and SBRT were comparable. Conclusions: Adaptive CFRT may be considered when SBRT is unsuitable for certain patients, without increasing the risk of side effects. However, further research is warranted to evaluate dedicated commercial cone-beam computed tomography (CT)- and CT-guided ART systems. Full article

Brain metastases have a distinctive vascular ecosystem—shaped by sprouting angiogenesis, vessel co-option, vasculogenic mimicry, and tumor cell transdifferentiation—that governs tumor perfusion, drug exposure, and therapeutic responsiveness. These heterogeneous vascularization patterns exhibit characteristic differences in enhancement morphology, perfusion levels, and metabolic uptake on contrast-enhanced MRI, perfusion imaging, and amino acid PET, providing crucial imaging cues for identifying routes of blood supply, inferring the state of the blood–tumor barrier, and guiding individualized therapeutic strategies. Anti-VEGF therapy is primarily used to alleviate cerebral edema and radiation necrosis, yet it confers limited survival benefit, underscoring the spatiotemporal heterogeneity of the blood–tumor barrier and the persistence of non-classical vascularization pathways. Building on the concept of “vascular normalization,” combinations of anti-angiogenic therapy with immunotherapy, radiotherapy, or targeted agents have shown encouraging intracranial activity in selected settings—most robustly in melanoma brain metastases—but remain insufficiently validated in randomized, brain-metastasis-focused trials. By integrating mechanistic, imaging, and therapeutic perspectives, this review outlines how vascular-ecosystem-based stratification and physics-informed drug-delivery strategies may help transition anti-vascular therapy from symptomatic control toward mechanism-driven precision intervention. Full article

Ultrasound-stimulated microbubble (USMB) therapy, in combination with radiotherapy (XRT), represents a promising approach to enhancing the efficacy of conventional cancer treatments by targeting tumor vasculature. Recent preclinical studies using MRI-guided focused ultrasound have demonstrated that USMB enhances radiation effects in tumor blood vessels, resulting in significantly greater tumor cell death than radiation alone. Dynamic contrast-enhanced MRI (DCE-MRI) has been instrumental in this methodology in mapping tumor perfusion heterogeneity, allowing for precise targeting of additional USMB and XRT to specific vascular regions. This study employed four advanced texture analysis methods, GLCM, GLDM, GLSZM, and NGTDM, to quantitatively assess changes in the cellular structure of prostate tumors following different treatments, including combinations of USMB and XRT targeted to low- and high-perfusion regions. Texture features, particularly those derived from GLCM, GLDM, and GLSZM, revealed significant differences in cell structure patterns across treatment groups. The GLSZM methodology was identified as the most sensitive method for detecting treatment-induced structural changes, effectively identifying regions of necrosis and varied stages of cell death. Texture-derivative analyses further highlighted intra-tumoral heterogeneity, especially in response to additional USMB + XRT treatments. These results align with findings in other tissue models, underscoring the value of texture analysis for monitoring treatment response. Full article

Background: Gross tumor volume (GTV) segmentation of Nasopharyngeal Carcinoma (NPC) crucially determines the precision of image-guided radiation therapy (IGRT) for NPC. Compared to other cancers, the clinical delineation of NPC is especially challenging due to its capricious infiltration of the adjacent rich tissues and bones, and it routinely requires multimodal information from CT and MRI series to identify its ambiguous tumor boundary. However, the conventional deep learning-based multimodal segmentation method suffers from limited prediction accuracy and frequently performs as well as or worse than single-modality segmentation models. The limited multimodal prediction performance indicates defective information extraction and integration from the input channels. This study aims to develop a 3D Gaussian-prompted Diffusion Model (3DG-PDM) for more clinically targeted information extraction and effective multimodal information integration, thereby facilitating more accurate and clinically interpretable GTV segmentation for NPC. Methods: We propose a 3D-Gaussian-Prompted Diffusion Model (3DGS-PDM) that operates NPC tumor contouring in multimodal clinical priors through a guided stepwise process. The proposed model contains two modules: a Gaussian Initialization Module that utilizes a 3D-Gaussian-Splatting technique to distill 3D-Gaussian representations based on clinical priors from CT, MRI-t2 and MRI-t1-contract-enhanced-fat-suppression (MRI-t1-cefs), respectively, and a Diffusion Segmentation Module that generates tumor segmentation step-by-step from the fused 3D-Gaussians prompts. We retrospectively collected data on 600 NPC patients from four hospitals through paired CT, MRI series and clinical GTV annotations, and divided that dataset into 480 training volumes and 120 testing volumes. Results: Our proposed method can achieve a mean dice similarity cofficient (DSC) of 84.29 ± 7.33, a mean average symmetric surface distance (ASSD) of 1.31 ± 0.63, and a 95th percentile of Hausdorff (HD₉₅) of 4.76 ± 1.98 on primary NPC tumor (GTVp) segmentation, and a DSC of 79.25 ± 10.01, an ASSD of 1.19 ± 0.72 and an HD₉₅ of 4.76 ± 1.71 on metastasis NPC tumor (GTVnd) segmentation. Comparative experiments further demonstrate that our method can significantly improve the multimodal segmentation performance on NPC tumors, with superior advantages over five other state-of-the-art comparative methods. Visual evaluation on the segmentation prediction process and a three-step ablation study on input channels further demonstrate the interpretability of our proposed method. Conclusions: This study proposes a performant and interpretable multimodal segmentation method for GTV of NPC, contributing greatly to precision improvement for NPC therapy treatment. Full article

Magnetic Particle Imaging (MPI) is a new type of tracer-based imaging that has great spatial and temporal resolution, does not require ionizing radiation, and can see deep into tissues by directly measuring the nonlinear magnetization response of superparamagnetic iron oxide nanoparticles (SPIONs). Unlike Magnetic Resonance Imaging (MRI) or Computed Tomography (CT), MPI has very high contrast and quantitative accuracy, which makes it perfect for use in dynamic cardiovascular applications. This study presents a full picture of the most recent changes in cardiac MPI, such as the physics behind Field-Free Point (FFP) and Field-Free Line (FFL) encoding, new ideas for tracer design, and important steps in the evolution of scanner hardware. We discuss the clinical relevance of cardiac MPI in visualizing myocardial perfusion, quantifying blood flow, and guiding real-time interventions. A hybrid imaging workflow, which improves anatomical detail and functional assessment, is utilized to explore the integration of MPI with complementary modalities, particularly MRI. By consolidating recent preclinical breakthroughs and highlighting the roadmap toward human-scale implementation, this article underscores the transformative potential of MPI in cardiac diagnostics and image-guided therapy. Full article

The advent of nanotechnology has revolutionized the field of medicine, particularly in the development of targeted therapeutic strategies. Among these, radiolabeled nanomaterials have emerged as promising tools for both diagnostic and therapeutic applications, offering precise delivery of radiation to diseased tissues while minimizing damage to healthy ones. Notably, Lutetium-177 (¹⁷⁷Lu) has gained significant attention due to its favorable emission properties and availability that render it suitable for imaging and therapeutic purposes. When integrated with magnetic nano-formulations, ¹⁷⁷Lu-labeled systems combine the benefits of targeted radiation therapy (TRT) with the unique properties of magnetic nanoparticles (MNPs), such as magnetic resonance imaging (MRI) contrast enhancement and magnetically guided drug delivery to address challenges in diagnosis and treatment of diseases, such as cancer. By examining the latest advancements in their design, particularly surface functionalization and bioconjugation strategies, this study aims to highlight their efficacy in targeted therapy, imaging, and theranostic applications. Furthermore, we discuss the current challenges, such as scalability, biocompatibility, and regulatory hurdles, while proposing future directions to enhance their clinical translation. This comprehensive review underscores the transformative potential of ¹⁷⁷Lu-labeled magnetic nano-formulations in precision medicine and their role in shaping the future of therapeutic interventions. Full article