Cited by Graph regularized least squares regression for automated breast ultrasound imaging

Integrating AI into Cancer Immunotherapy—A Narrative Review of Current Applications and Future Directions DOI

David B. Olawade, Aanuoluwapo Clement David-Olawade, Temitope Adereni

et al.

Diseases, Journal Year: 2025, Volume and Issue: 13(1), P. 24 - 24

Published: Jan. 20, 2025

Background: Cancer remains a leading cause of morbidity and mortality worldwide. Traditional treatments like chemotherapy radiation often result in significant side effects varied patient outcomes. Immunotherapy has emerged as promising alternative, harnessing the immune system to target cancer cells. However, complexity responses tumor heterogeneity challenges its effectiveness. Objective: This mini-narrative review explores role artificial intelligence [AI] enhancing efficacy immunotherapy, predicting responses, discovering novel therapeutic targets. Methods: A comprehensive literature was conducted, focusing on studies published between 2010 2024 that examined application AI immunotherapy. Databases such PubMed, Google Scholar, Web Science were utilized, articles selected based relevance topic. Results: significantly contributed identifying biomarkers predict immunotherapy by analyzing genomic, transcriptomic, proteomic data. It also optimizes combination therapies most effective treatment protocols. AI-driven predictive models help assess response guiding clinical decision-making minimizing effects. Additionally, facilitates discovery targets, neoantigens, enabling development personalized immunotherapies. Conclusions: holds immense potential transforming related data privacy, algorithm transparency, integration must be addressed. Overcoming these hurdles will likely make central component future offering more treatments.

Language: Английский

Citations

Advancements in proteogenomics for preclinical targeted cancer therapy research DOI

Yuying Suo,

Yuanli Song, Yuqiu Wang

et al.

Biophysics Reports, Journal Year: 2025, Volume and Issue: 11(1), P. 56 - 56

Published: Jan. 1, 2025

Advancements in molecular characterization technologies have accelerated targeted cancer therapy research at unprecedented resolution and dimensionality. Integrating comprehensive multi-omic profiling of a tumor, proteogenomics, marks transformative milestone for preclinical research. In this paper, we initially provided an overview proteogenomics research, spanning genomics, transcriptomics, proteomics. Subsequently, the applications were introduced examined from different perspectives, including but not limited to genetic alterations, quantifications, single-cell patterns, post-translational modification levels, subtype signatures, immune landscape. We also paid attention combined multi-omics data analysis pan-cancer analysis. This paper highlights crucial role elucidating mechanisms tumorigenesis, discovering effective therapeutic targets promising biomarkers, developing subtype-specific therapies.

Language: Английский

Citations

MSDAHNet: A multi-scale dual attention hybrid convolution network for breast tumor segmentation DOI

Xue-lian Yang,

Yuanjun Wang, Jingbo Zhao

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110199 - 110199

Published: March 13, 2025

Language: Английский

Citations

Artificial Intelligence Advancements in Oncology: A Review of Current Trends and Future Directions DOI

Ellen N. Huhulea,

Lillian Huang,

Shirley Eng

et al.

Biomedicines, Journal Year: 2025, Volume and Issue: 13(4), P. 951 - 951

Published: April 13, 2025

Cancer remains one of the leading causes mortality worldwide, driving need for innovative approaches in research and treatment. Artificial intelligence (AI) has emerged as a powerful tool oncology, with potential to revolutionize cancer diagnosis, treatment, management. This paper reviews recent advancements AI applications within research, focusing on early detection through computer-aided personalized treatment strategies, drug discovery. We survey AI-enhanced diagnostic explore techniques such deep learning, well integration nanomedicine immunotherapy care. Comparative analyses AI-based models versus traditional methods are presented, highlighting AI’s superior potential. Additionally, we discuss importance integrating social determinants health optimize Despite these advancements, challenges data quality, algorithmic biases, clinical validation remain, limiting widespread adoption. The review concludes discussion future directions emphasizing its reshape care by enhancing personalizing treatments targeted therapies, ultimately improving patient outcomes.

Language: Английский

Citations

Novel Intelligent Exogenous Neuro-Architecture–Driven Machine Learning Approach for Nonlinear Fractional Breast Cancer Risk System DOI

A. Fida,

Muhammad Asif Zahoor Raja, Chuan‐Yu Chang

et al.

Communications in Nonlinear Science and Numerical Simulation, Journal Year: 2025, Volume and Issue: unknown, P. 108955 - 108955

Published: May 1, 2025

Language: Английский

Citations

Unraveling the Impact of Polyethylenimine-Coated Gold Nanoparticle Size on the Efficiency of Sandwich-Style Electrochemical Immunosensors DOI

Thitirat Putnin,

Supakeit Chanarsa, Patrawadee Yaiwong

et al.

ACS Measurement Science Au, Journal Year: 2025, Volume and Issue: 5(1), P. 96 - 108

Published: Jan. 23, 2025

Sometimes, smaller size is not always better, and looking for nanomaterials that offer better device performance requires consideration of their properties at the first stage. In this study, effects polyethylenimine-capped AuNPs (PEI-AuNPs) proteins on immunosensor performances, namely, sensitivity limit detection, are examined. The size-effect investigation PEI-AuNPs involves modification surface disposable screen-printed carbon electrodes to support primary antibodies ability load secondary redox probes perform amplification in immunosensor. correlation average size, electrochemical activities, protein property investigated. synthesized with different diameters ranging from 4.7 44.9 nm employed investigation. When sensor forms a sandwich architecture, detection employs current response Ag+ ions bioconjugate, which greatly increases by increasing concentration. addition, best signal or antibody complexes unique AuNPs' allows superior amplification. effect using sizes target devices significantly observed. Although general small-sized high active areas, can improve electrode surface, reactivity, performance, we observe medium (16.3 nm) gives type. Therefore, finding useful considering optimizing tunable voltammetric acquiring sensor.

Language: Английский

Citations

FPGA Hardware Acceleration of AI Models for Real-Time Breast Cancer Classification DOI

Ayoub Mhaouch,

Wafa Gtifa,

Mohsen Machhout

et al.

AI, Journal Year: 2025, Volume and Issue: 6(4), P. 76 - 76

Published: April 11, 2025

Breast cancer detection is a critical task in healthcare, requiring fast, accurate, and efficient diagnostic tools. However, the high computational demands latency of deep learning models medical imaging present significant challenges, especially resource-constrained environments. This paper addresses these challenges by presenting an FPGA hardware accelerator tailored for breast classification, leveraging Zynq XC7Z020 SoC. The system integrates FPGA-accelerated layers with ARM Cortex-A9 processor to optimize both performance resource efficiency. We developed modular IP cores, including Conv2D, Average Pooling, ReLU, using Vivado HLS maximize utilization. By adopting 8-bit fixed-point arithmetic, design achieves 15.8% reduction execution time compared traditional CPU-based implementations while maintaining classification accuracy. Additionally, our optimized approach significantly enhances energy efficiency, reducing power consumption from 3.8 W 1.4 63.15% reduction. improvement makes highly suitable real-time, power-sensitive applications, particularly embedded edge computing Furthermore, it underscores scalability efficiency FPGA-based AI solutions healthcare diagnostics, enabling faster more energy-efficient inference on devices.

Language: Английский

Citations

Navigating the tumor landscape: VEGF, MicroRNAs, and the future of cancer treatment DOI

K P Ameya,

Parveen Usman,

Durairaj Sekar

et al.

Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, Journal Year: 2025, Volume and Issue: 1868(2), P. 195091 - 195091

Published: May 3, 2025

Language: Английский

Citations

Spatial transcriptomics: a new frontier in accurate localization of breast cancer diagnosis and treatment DOI

Yang Zhang,

Shuhua Gong,

Xiaofei Liu

et al.

Frontiers in Immunology, Journal Year: 2024, Volume and Issue: 15

Published: Oct. 8, 2024

Breast cancer is one of the most prevalent cancers in women globally. Its treatment and prognosis are significantly influenced by tumor microenvironment heterogeneity. Precision therapy enhances efficacy, reduces unwanted side effects, maximizes patients’ survival duration while improving their quality life. Spatial transcriptomics significant importance for precise breast cancer, playing a critical role revealing internal structural differences tumors composition microenvironment. It offers novel perspective studying spatial structure cell interactions within tumors, facilitating more effective personalized treatments cancer. This article will summarize latest findings diagnosis from transcriptomics, focusing on revelation microenvironment, identification new therapeutic targets, enhancement disease diagnostic accuracy, comprehension progression metastasis, assessment drug responses, creation high-resolution maps cells, representation heterogeneity, support clinical decision-making, particularly elucidating immunotherapy correlation with outcomes.

Language: Английский

Citations

Graph regularized least squares regression for automated breast ultrasound imaging DOI

Yi Zhou,

Menghui Zhang,

Pan Yin

et al.

Neurocomputing, Journal Year: 2024, Volume and Issue: unknown, P. 129065 - 129065

Published: Dec. 1, 2024

Language: Английский

Citations