Search Results (103)

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

Advanced imaging is the cornerstone of modern radiation oncology, contributing to each phase of patient care, from diagnosis and treatment planning to delivery and follow-up. It has evolved from providing purely geometric guidance to enabling biological and dynamic precision, capturing detailed spatial and functional information about tumors and surrounding tissues. This progress has also generated vast amounts of complex data that remain largely underexplored. AI-based methods have shown promises to unlock the potential of these data, ensuring quality and standardization while extracting previously inaccessible insights. AI-driven tools can enhance accuracy, efficiency, and personalization of radiation oncology through precision diagnosis, automated segmentation, adaptive treatment planning, real-time image guidance, and predictive response assessment. In this review, we conducted a systematic bibliometric analysis of relevant literature published in the last decade and explored current advancements in AI and radiomics applications across radiation oncology. We also addressed ongoing challenges, such as data heterogeneity, model interpretability, and clinical implementation, and discussed future directions for integrating AI-powered imaging solutions into routine practice to advance precision cancer care. Full article

The interplay between the environmental exposome and the cancer genome remains a critical gap in precision oncology. While somatic mutational signatures—genomic fossils imprinted by exposures such as ultraviolet radiation; tobacco smoke; and industrial pollutants—are well characterised for their etiological significance; their functional impact on therapeutic efficacy remains largely unexplored. We hypothesised that these environmental genomic scars induce distinct pharmacogenomic vulnerabilities and resistance mechanisms that vary by geographical exposure patterns. This study employs two complementary analytical frameworks. First, a linear regression-based pharmacogenomic screen across four datasets (GDSC1, GDSC2, CTRP, CCLE; 1001 cell lines, 31 cancer types) identified 608 statistically significant (p < 0.01) mutational signature–drug interactions, revealing that UV-associated signature SBS7a is associated with broad-spectrum therapeutic resistance, including to BRAF inhibitors (PLX-4720, p < 10⁻⁴), while pollution-driven oxidative stress (SBS18) is associated with sensitivity to p38 MAPK inhibition (VX-702, r = −0.45, p < 10⁻⁹). Second, an XGBoost predictive model trained exclusively on 33,679 GDSC2 records using a 1265-feature matrix integrating 40 SBS signatures, drug chemistry descriptors, proteomic features, and two satellite-derived environmental variables (NASA PM_2.5 and UV)—achieved R² = 0.7973 on a 20% holdout set (grouped cross-validation R² = 0.7296). SHAP analysis revealed that satellite-derived PM_2.5 (Zone_PM25) ranked 7th of 1265 features, exceeding all 40 individual SBS mutational signatures. Synthesising these findings with satellite-derived atmospheric data, we constructed an exploratory spatially interpolated risk surface spanning 122 nations, generating the hypothesis that uniform drug efficacy assumptions may not apply globally. These findings suggest that a patient’s environmental exposure history may constitute a measurable pharmacogenomic variable. This exploratory framework warrants validation in independent datasets and with individual-level geographic data before clinical application. Full article

Background: Positron emission tomography (PET) provides physiologic information central to oncologic staging and treatment assessment, but its availability is limited by cost, radiation exposure, and scanner access. Synthesizing PET from computed tomography (CT) is attractive but challenging, as tracer uptake is only partially constrained by anatomy, making the mapping inherently one-to-many. Methods: We propose a conditional 3D latent diffusion framework (3D-LDM) for CT-to-PET synthesis in the head–neck and thoracic region. The pipeline localizes anatomy by segmenting lungs in CT and restricting the volume to reduce irrelevant variability. PET volumes are encoded into a compact latent space using a KL-regularized 3D autoencoder, and a conditional 3D diffusion U-Net learns to generate PET latents conditioned on CT via a denoising diffusion process. The model was trained and evaluated on 900 paired PET/CT studies. Performance was assessed in SUV space using MAE, PSNR, and SSIM, and compared against transformer-, CNN-, and GAN-based baselines. Results: On the held-out test cohort, 3D-LDM achieved the best overall quantitative fidelity (MAE = 303.05 ± 22.16 SUV units, PSNR = 32.64 ± 1.79, SSIM = 0.86 ± 0.03), outperforming all baselines with statistically significant differences (p < 0.001). At the lesion level, the model achieved a precision of 0.76 (95% CI: 0.71, 0.81) and recall of 0.76 (95% CI: 0.72, 0.80), detecting an average of 3.19 lesions per scan with a false-positive rate of 0.72/scan. Lesion-wise NMSE was 11.37%, significantly outperforming GAN and transformer baselines. Conclusions: 3D-LDM enables efficient, high-fidelity PET synthesis in the head–neck and thoracic regions, substantially improving lesion-level accuracy over state-of-the-art baselines. While it is not a replacement for diagnostic PET, these results support the model’s potential as a clinical decision support tool. Full article

Thoracic malignancies are uniquely positioned for the integration of emerging technologies such as artificial intelligence (AI), which have the potential to advance precision oncology across the cancer care continuum. In cancer screening, AI has emerged as a promising strategy to enhance diagnostic accuracy, efficiency, and scalability. Deep learning applied to pathology (pathomics) and imaging (radiomics) has enabled the development of novel, noninvasive tools capable of predicting histologic and molecular features that may correlate with treatment response or toxicity. In drug discovery, computational approaches can analyze large-scale genomic, chemical, and clinical datasets to accelerate target identification and match candidate compounds to available targets; this may be particularly useful in the context of resistance to targeted therapy. AI tools may also support treatment planning for radiation and surgery, guide systemic therapy selection, and facilitate continuous monitoring for early identification of treatment resistance or toxicity. As these technologies are integrated into clinical workflows, careful attention to ethical, regulatory, and clinical governance frameworks will be essential to ensure equitable implementation and bias mitigation. Maintaining human oversight and a human-centered approach remain critical, as complex treatment decisions and sensitive patient interactions are central to the care of patients with thoracic malignancies. Full article

Glioblastoma multiforme (GBM) is one of the most aggressive and treatment-resistant forms of brain cancer, posing challenges to modern oncology. Current treatments, including surgery, radiation, and chemotherapy (e.g., Temozolomide or TMZ), often fail due to the inevitable development of drug resistance. TMZ resistance remains a major therapeutic challenge for the reasons that it is the first-line treatment. Recent studies indicate a rising GBM tumour burden and a trend towards earlier age of onset. It highlights the urgent need for evidence-based policymaking and intensified research to address this most difficult-to-treat malignancy in clinical settings. Ferroptosis, a newly recognized type of controlled cell death induced by iron-dependent lipid peroxidation, has emerged as a potential approach to overcome apoptosis resistance and restore drug sensitivity in GBM. This mechanism is modulated by key molecules that can be specifically targeted to either enhance oxidative stress or inhibit antioxidant defences, ultimately leading to tumour cell death. This review conducts a meta-analysis of preclinical evidence to better understand the potential of activating ferroptosis as a key target for developing nanoparticles to resensitize TMZ-resistant GBM cells. Current evidence indicates that combining ferroptosis induction with strategically engineered nanocarrier systems can serve as a novel and effective therapeutic approach to overcome TMZ resistance and advance precision-based GBM treatment. Full article

Background: Radiotherapy (RT) is a core modality for cancer treatment, yet it is plagued by inter-observer variability in target delineation, inefficient manual workflows, and challenges in fusing multi-type clinical data. Large language models (LLMs), with their superior semantic understanding and cross-modal fusion capabilities present novel solutions to these challenges. Scope: This narrative review provided a comprehensive overview of the current landscape and emerging trends of LLM applications across the entire RT workflow. Findings: LLMs demonstrated substantial clinical utility in key RT domains, including automated target volume delineation (e.g., Medformer, Radformer), dose prediction (e.g., DoseGNN), treatment planning automation (e.g., GPT-Plan), patient education, clinical decision support, medical information extraction, and prognosis assessment. These applications not only have the potential to enhance the accuracy and efficiency of RT but also facilitate the standardization of clinical pathways. However, widespread clinical adoption was impeded by critical limitations, including model hallucinations, insufficient generalizability, and unresolved issues regarding data privacy and ethical governance. Conclusions: LLMs possessed transformative potential to revolutionize radiation oncology. Future endeavors should prioritize technical refinements to mitigate model deficiencies, establish standardized evaluation benchmarks, and develop robust ethical frameworks. These concerted efforts are crucial for translating LLM research into clinical practice and advancing the era of intelligent, precision RT. Full article

The remarkable evolution of oncological therapies has dramatically improved cancer survival rates but has simultaneously introduced a significant burden of cardiovascular complications. Cardio-oncology has emerged as a critical multidisciplinary field focused on mitigating the “collateral damage” of life-saving anticancer treatments, ranging from traditional chemotherapeutics to novel immunotherapies. This review provides a comprehensive analysis of the pathophysiological mechanisms, clinical phenotypes, and evolving management strategies for cancer therapy-related cardiac dysfunction (CTRCD). An extensive synthesis of the current literature was conducted, focusing on the molecular pathways of cardiotoxicity, including Topoisomerase IIβ inhibition by anthracyclines, HER2 signaling disruption by targeted agents, and immune-mediated myocarditis triggered by checkpoint inhibitors (ICIs). Cardiotoxicity is increasingly recognized as a spectrum of phenotypes. Heart failure with reduced ejection fraction (HFrEF) remains a primary concern with cytotoxic agents, while heart failure with preserved ejection fraction (HFpEF) is emerging as a critical complication of radiation therapy and tyrosine kinase inhibitors (TKIs). The integration of advanced diagnostic tools—specifically Global Longitudinal Strain (GLS) and Cardiac Magnetic Resonance (CMR) mapping—has shifted the clinical focus toward subclinical detection. Furthermore, pivotal clinical trials such as PRADA and SUCCOUR have validated early pharmacological prophylaxis and strain-guided interventions. Emerging challenges, including the management of CAR-T cell-induced cytokine release syndrome and the specific cardiovascular needs of pediatric and geriatric populations, are also explored. The future of cardio-oncology lies in precision medicine, leveraging genomic profiling and artificial intelligence to identify high-risk individuals. A proactive, multidisciplinary approach is essential to ensure that the success of modern oncology is not compromised by irreversible cardiovascular morbidity. Full article

Radiopharmaceutical therapy (RPT) has emerged as a transformative modality in oncology, particularly for patients with metastatic or inoperable tumors. By leveraging molecularly targeted carriers conjugated to cytotoxic radionuclides, RPT enables precise delivery of ionizing radiation to tumor sites while minimizing off-target effects. Central to this approach are alpha (α) and beta (β) particle-emitting radionuclides. This review aims to provide a comprehensive overview of all clinically relevant alpha and beta emitters and incorporates the most recent advances from 2017–2025, offering a comprehensive and up-to-date perspective. Alpha and beta emitters hold significant promises for the future, especially in nuclear medicine, energy, and environmental monitoring. Medically, these emitters are at the forefront of targeted radiotherapy, offering new hope for cancer treatment. Alpha emitters such as Actinium-225 and Radium-223 are gaining attention for their high linear energy transfer, which allows them to effectively kill cancer cells while minimizing damage to surrounding healthy tissues. Beta emitters, including Lutetium-177 and Iodine-131, are already widely used for treating thyroid cancer, neuroendocrine tumors, and prostate cancer. They offer a longer range in tissue penetration than alpha particles, making them suitable for larger or more diffuse tumors. Alpha and beta emitters hold tremendous promise in targeted radiotherapy. However, current research is limited by an incomplete understanding of resistance pathways, insufficient long-term safety and efficacy data, and underdeveloped personalized treatment frameworks. As production technologies improve and safety protocols advance, these emitters will likely play an even more prominent role in both health care and scientific innovation. Full article

Boron neutron capture therapy (BNCT) is experiencing a global resurgence driven by advances in boron pharmacology, accelerator-based neutron sources, and molecular imaging-guided theranostics. BNCT produces high linear energy transfer particles with micrometer-range energy deposition, enabling cell-selective irradiation confined to boron-enriched tumor cells in a geometrically targeted region by the neutron beam. This mechanism offers the potential for exceptionally high therapeutic ratios, provided two core requirements are met: sufficient differential tumor uptake of ¹⁰B and a neutron beam with appropriate energy and penetration. After early clinical attempts in the mid-20th century were hindered by inadequate boron agents and reactor-based neutron beams, recent technological breakthroughs have made BNCT clinically viable. The development of hospital-compatible accelerator neutron sources, next-generation boron delivery systems (such as receptor-targeted compounds and nanoparticles), advanced theranostic approaches (such as ¹⁸F-BPA positron emission tomography and boron-sensitive magnetic resonance imaging), and AI-driven biodistribution modeling now support personalized treatment planning and patient selection. These innovations have catalyzed modern clinical implementation, exemplified by Japan’s regulatory approval of BNCT for recurrent head and neck cancer and the rapid expansion of clinical programs across Asia, Europe, and South America. Building on these foundations, BNCT has transitioned from a predominantly academic experimental modality into an increasingly commercialized and industrially supported therapeutic platform. The emergence of dedicated BNCT companies, international collaborations between accelerator manufacturers and hospitals, and pharmaceutical development pipelines for next-generation boron carriers has accelerated clinical translation. Moreover, BNCT now occupies a unique position among radiation modalities due to its hybrid nature, namely combining the biological targeting of radiopharmaceutical therapy with the external-beam controllability of radiotherapy, thereby offering new therapeutic opportunities where competitive approaches fall short. Emerging evidence suggests therapeutic promise in glioblastoma, recurrent head and neck cancers, melanoma, meningioma, lung cancer, sarcomas, and other difficult-to-treat malignancies. Looking ahead, continued innovation in compact neutron source engineering, boron nanocarriers, multimodal theranostics, microdosimetry-guided treatment planning, and combination strategies with systemic therapies such as immunotherapy will be essential for optimizing outcomes. Together, these converging developments position BNCT as a biologically targeted and potentially transformative modality in the era of precision oncology. Full article

This review explores the transformative role of three-dimensional (3D) printing in radiation therapy for cancer treatment, emphasizing its potential to deliver patient-specific, cost-effective, and sustainable medical devices. The integration of 3D printing enables rapid fabrication of customized boluses, compensators, immobilization devices, and GRID collimators tailored to individual anatomical and clinical requirements. Comparative analysis reveals that additive manufacturing surpasses conventional machining in design flexibility, lead time reduction, and material efficiency, while offering significant cost savings and recyclability benefits. Case studies demonstrate that 3D-printed GRID collimators achieve comparable dosimetric performance to traditional devices, with peak-to-valley dose ratios optimized for spatially fractionated radiation therapy. Furthermore, emerging applications of artificial intelligence (AI) in conjunction with 3D printing promise automated treatment planning, generative device design, and real-time quality assurance, and are paving the way for adaptive and intelligent radiotherapy solutions. Regulatory considerations, including FDA guidelines for additive manufacturing, are discussed to ensure compliance and patient safety. Despite challenges such as material variability, workflow standardization, and large-scale clinical validation, evidence indicates that 3D printing significantly enhances therapeutic precision, reduces toxicity, and improves patient outcomes. This review underscores the synergy between 3D printing and AI-driven innovations as a cornerstone for next-generation radiation oncology, offering a roadmap for clinical adoption and future research. Full article

Drug discovery is a complex and multi-stage process that requires advanced analytical technologies capable of accelerating preclinical evaluation and improving the precision of therapeutic design. The combination of fluorescence and magnetic resonance imaging (MRI) within multimodal imaging plays an increasingly important role in modern pharmacokinetics, integrating the high molecular sensitivity of fluorescence with the non-invasive anatomical visualization offered by MRI. Fluorescence enables real-time monitoring of cellular processes, including drug–target interactions and molecular dynamics, whereas MRI provides detailed structural information on tissues without exposure to ionizing radiation. Hybrid probes—such as superparamagnetic iron oxide nanoparticles (SPIONs) functionalized with near-infrared (NIR) fluorophores or gadolinium-based complexes linked to optical dyes—enable simultaneous acquisition of molecular and anatomical data in a single examination. These multimodal systems are being explored in oncology, neurology, and cardiology, where they support improved visualization of tumor biology, amyloid pathology, and inflammatory processes in vascular disease. Although multimodal imaging shows great promise for enhancing pharmacokinetic and pharmacodynamic studies, several challenges remain, including the potential toxicity of heavy-metal-based contrast agents, limited tissue penetration of fluorescence signals, probe stability in vivo, and the complexity and cost of synthesis. Advances in nanotechnology, particularly biodegradable carriers and manganese-based MRI contrasts, together with the integration of artificial intelligence algorithms, are helping to address these limitations. In the future, fluorescence–MRI hybrid imaging may become an important tool in personalized medicine, supporting more precise therapy planning and reducing the likelihood of clinical failure. Full article

Online adaptive radiotherapy (ART) is rapidly transforming clinical radiation oncology by enabling adaptation of treatment plans based on patient-specific anatomical and biological changes. However, most medical physics training programs lack structured education in ART. To address this critical gap, the Medical Physics Adaptive Radiotherapy (MPART) Fellowship was established at our center to train post-residency or practicing physicists in advanced adaptive technologies and workflows. The MPART Fellowship is a two-year program that provides immersive, platform-specific training in CBCT-guided (Varian Ethos), MR-guided (Elekta Unity), and PET-guided (RefleXion X1) radiotherapy. Fellows undergo modular clinical rotations, hands-on training, and dedicated research projects. The curriculum incorporates competencies in imaging, contouring, online planning, quality assurance, and team-based decision-making. Evaluation is based on the Accreditation Council for Graduate Medical Education competency domains and includes milestone tracking, mentor reviews, and structured presentations. The fellowship attracted applicants from both domestic and international institutions, reflecting strong demand for formal ART training. Out of 22 applications, two fellows have been successfully recruited into the program since 2024. Fellows actively participate in all phases of adaptive workflows and are expected to function at near-attending levels by the second year of their training. Each fellow also leads at least one translational or operational research project aimed at improving ART delivery. Fellows contribute to clinical coverage and lead developmental projects, resulting in presentations and publications at the national and international levels. The MPART Fellowship addresses a vital educational need by equipping medical physicists with the advanced competencies necessary for implementing and leading ART. This program offers a replicable framework for other institutions seeking to advance precision radiation therapy through structured post-residency training in adaptive radiotherapy. As this fellowship program is still in its early phase of establishment, the primary goal of this paper is to introduce the structure, framework, and implementation model of the program. Comprehensive outcome analyses—such as quantitative assessments, fellow feedback, and longitudinal competency evaluations—will be incorporated in future work as additional cohorts complete training. Full article

Radiotherapy is a fundamental component in the management of head and neck malignancies, but its non-selective effects on surrounding normal tissues can result in significant oral complications. The oral cavity and oropharynx contain several radiosensitive structures, including mucosa, salivary glands, and alveolar bone, which are susceptible to both acute and late toxicities resulting in mucositis, xerostomia, and osteoradionecrosis. Although dentomaxillofacial diagnostic imaging, such as intraoral radiography, panoramic imaging and cone-beam computed tomography (CBCT), delivers radiation doses several orders of magnitude lower than therapeutic exposures, its biological impact on previously irradiated tissues remains underexplored. Even low-dose X-rays may act as secondary stressors, reactivating oxidative and inflammatory pathways in tissues with compromised repair capacity. In this review, we examine the radiobiological and dosimetric implications of using diagnostic ionizing imaging in patients undergoing or recently having completed head and neck radiotherapy. We summarize current evidence on potential additive effects of low-dose imaging, emphasizing the importance of justification, timing, and protocol optimization. Finally, we discuss radioprotective strategies (e.g., dose modulation, field limitation, and integration of modern low-dose imaging technologies) designed to reduce unnecessary exposure, thus enhancing tissue preservation and ensuring diagnostic safety in this vulnerable patient population Full article

Hereditary predisposition substantially shapes prevention and management across urologic oncology. This narrative review synthesizes contemporary, practice-oriented guidance on whom to test, what to test, how to act on results, and how to implement care equitably for hereditary forms of prostate cancer, renal cell carcinoma (RCC), urothelial carcinoma, pheochromocytoma/paraganglioma (PPGL), and adrenocortical carcinoma (ACC). We delineate between forms of indication-driven germline testing (e.g., universal testing in metastatic prostate cancer; early-onset, bilateral/multifocal, or syndromic RCC; reflex tumor mismatch repair (MMR)/microsatellite instability (MSI) screening in upper-tract urothelial carcinoma (UTUC); universal testing in PPGL; universal TP53 testing in ACC) and pair these strategies with minimum actionable gene sets and syndrome-specific surveillance frameworks. Key points include targeted prostate-specific antigen screening in BRCA2 carriers and the impact of BRCA/ATM variants on reclassification during active surveillance; major hereditary RCC syndromes with genotype-tailored surveillance and pathway-directed therapy (e.g., HIF-2α inhibition for von Hippel–Lindau disease); UTUC/bladder cancer in Lynch syndrome with tumor MMR/MSI screening, annual urinalysis (selective cytology), and immunotherapy opportunities in deficient MMR disease/MSI-H; PPGL management emphasizing universal germline testing, intensified surveillance for SDHB, cortical-sparing adrenalectomy, and emerging HIF-2α inhibition; and ACC care modified by Li–Fraumeni syndrome (minimization of radiation/genotoxic therapy with whole-body imaging surveillance). Testicular germ cell tumor remains largely polygenic, with no routine germline testing in typical presentations. Finally, we provide pre-/post-test genetic-counseling checklists and mainstreamed workflows with equity metrics to operationalize precision care and close real-world access gaps. Full article