Modernizing Neuro-Oncology: The Impact of Imaging, Liquid Biopsies, and AI on Diagnosis and Treatment

International Journal of Molecular Sciences, Journal Year: 2025, Volume and Issue: 26(3), P. 917 - 917

Published: Jan. 22, 2025

Advances in neuro-oncology have transformed the diagnosis and management of brain tumors, which are among most challenging malignancies due to their high mortality rates complex neurological effects. Despite advancements surgery chemoradiotherapy, prognosis for glioblastoma multiforme (GBM) metastases remains poor, underscoring need innovative diagnostic strategies. This review highlights recent imaging techniques, liquid biopsies, artificial intelligence (AI) applications addressing current challenges. Advanced including diffusion tensor (DTI) magnetic resonance spectroscopy (MRS), improve differentiation tumor progression from treatment-related changes. Additionally, novel positron emission tomography (PET) radiotracers, such as 18F-fluoropivalate, 18F-fluoroethyltyrosine, 18F-fluluciclovine, facilitate metabolic profiling high-grade gliomas. Liquid biopsy, a minimally invasive technique, enables real-time monitoring biomarkers circulating DNA (ctDNA), extracellular vesicles (EVs), cells (CTCs), tumor-educated platelets (TEPs), enhancing precision. AI-driven algorithms, convolutional neural networks, integrate tools accuracy, reduce interobserver variability, accelerate clinical decision-making. These innovations advance personalized neuro-oncological care, offering new opportunities outcomes patients with central nervous system tumors. We advocate future research integrating these into workflows, accessibility challenges, standardizing methodologies ensure broad applicability neuro-oncology.

Language: Английский

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Advancing laryngology through artificial intelligence: a comprehensive review of implementation frameworks and strategies

Current Opinion in Otolaryngology & Head & Neck Surgery, Journal Year: 2025, Volume and Issue: unknown

Published: March 4, 2025

This review aims to explore the integration of artificial intelligence (AI) in laryngology, with specific focus on barriers preventing translation from pilot studies into routine clinical practice and strategies for successful implementation. Laryngology has seen an increasing number proof-of-concept demonstrating AI's ability enhance diagnostics, treatment planning, patient outcomes. Despite these advancements, few tools have been successfully adopted settings. Effective implementation requires application established science frameworks early design phase. Additional factors required AI applications include addressing needs, fostering diverse interdisciplinary teams, ensuring scalability without compromising model performance. Governance, epistemic, ethical considerations must also be continuously incorporated throughout project lifecycle ensure safe, responsible, equitable use technologies. While hold significant promise advancing its remains limited. Achieving meaningful will require a shift toward practical solutions that prioritize clinicians' patients' usability, sustainability, alignment workflows.

Language: Английский

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Harmonizing foundation models in healthcare: A comprehensive survey of their roles, relationships, and impact in artificial intelligence’s advancing terrain

Computers in Biology and Medicine, Journal Year: 2025, Volume and Issue: 189, P. 109925 - 109925

Published: March 12, 2025

Language: Английский

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Adaptive Vectorial Restoration from Dynamic Speckle Patterns Through Biological Scattering Media Based on Deep Learning

Sensors, Journal Year: 2025, Volume and Issue: 25(6), P. 1803 - 1803

Published: March 14, 2025

Imaging technologies based on vector optical fields hold significant potential in the biomedical field, particularly for non-invasive scattering imaging of anisotropic biological tissues. However, dynamic and nature tissues poses severe challenges to propagation reconstruction due light scattering. To address this, we propose a deep learning-based polarization-resolved restoration method aimed at achieving efficient accurate from speckle patterns generated after passing through time-varying media. By innovatively leveraging two orthogonal polarization components fields, our approach significantly enhances robustness media, benefiting additional information dimension vectorial powerful learning capacity neural network. For first time, hybrid network model is designed that integrates convolutional networks (CNN) with Transformer architecture capturing local global features image, enabling adaptive dynamically patterns. The experimental results demonstrate exhibits excellent generalization capabilities reconstructing behind This study not only provides an solution but also advances application environments integration technologies.

Language: Английский

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Multi-modal fusion model for Time-Varying medical Data: Addressing Long-Term dependencies and memory challenges in sequence fusion

Journal of Biomedical Informatics, Journal Year: 2025, Volume and Issue: unknown, P. 104823 - 104823

Published: April 1, 2025

Language: Английский

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im7G-DCT: A two-branch strategy model based on improved DenseNet and Transformer for m7G site prediction

Computational Biology and Chemistry, Journal Year: 2025, Volume and Issue: 118, P. 108473 - 108473

Published: April 12, 2025

Language: Английский

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RoBIn: A Transformer-based model for risk of bias inference with machine reading comprehension

Journal of Biomedical Informatics, Journal Year: 2025, Volume and Issue: 166, P. 104819 - 104819

Published: April 16, 2025

Language: Английский

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Gender prediction model based on CNN-BiLSTM-attention hybrid

Electronic Research Archive, Journal Year: 2025, Volume and Issue: 33(4), P. 2366 - 2390

Published: Jan. 1, 2025

Language: Английский

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Advancements in deep learning for early diagnosis of Alzheimer’s disease using multimodal neuroimaging: challenges and future directions

Frontiers in Neuroinformatics, Journal Year: 2025, Volume and Issue: 19

Published: May 2, 2025

Introduction Alzheimer’s disease is a progressive neurodegenerative disorder challenging early diagnosis and treatment. Recent advancements in deep learning algorithms applied to multimodal brain imaging offer promising solutions for improving diagnostic accuracy predicting progression. Method This narrative review synthesizes current literature on applications using neuroimaging. The process involved comprehensive search of relevant databases (PubMed, Embase, Google Scholar ClinicalTrials.gov ), selection pertinent studies, critical analysis findings. We employed best-evidence approach, prioritizing high-quality studies identifying consistent patterns across the literature. Results Deep architectures, including convolutional neural networks, recurrent transformer-based models, have shown remarkable potential analyzing neuroimaging data. These models can effectively structural functional modalities, extracting features associated with pathology. Integration multiple modalities has demonstrated improved compared single-modality approaches. also promise predictive modeling, biomarkers forecasting Discussion While approaches show great potential, several challenges remain. Data heterogeneity, small sample sizes, limited generalizability diverse populations are significant hurdles. clinical translation these requires careful consideration interpretability, transparency, ethical implications. future AI neurodiagnostics looks promising, personalized treatment strategies.

Language: Английский

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The role of nanomedicine and artificial intelligence in cancer health care: individual applications and emerging integrations—a narrative review

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: May 8, 2025

Cancer remains one of the deadliest diseases globally, significantly impacting patients' quality life. Addressing rising incidence cancer deaths necessitates innovative approaches such as nanomedicine and artificial intelligence (AI). The convergence AI represents a transformative frontier in healthcare, promising unprecedented advancements diagnosis, treatment, patient management. This narrative review explores distinct applications oncology, alongside their synergistic potential. Nanomedicine leverages nanoparticles for targeted drug delivery, enhancing therapeutic efficacy while minimizing adverse effects. Concurrently, algorithms facilitate early detection, personalized treatment planning, predictive analytics, thereby optimizing clinical outcomes. Emerging integrations these technologies could transform care by facilitating precise, personalized, adaptive strategies. synthesizes current research, highlights individual applications, discusses emerging oncology. goal is to provide comprehensive understanding how cutting-edge can collaboratively improve prognosis.

Language: Английский

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