Impact of Artificial Intelligence-Driven Biomarker Analysis on Survival Outcomes in Lung Cancer Patients: A Systematic Review

سال انتشار: 1404
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 95

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شناسه ملی سند علمی:

AIMS02_393

تاریخ نمایه سازی: 29 تیر 1404

چکیده مقاله:

Background and Aims: Lung cancer is a leading cause of death worldwide. Timely, accurate analysis of biomarkers assists in treatment decisions and improves patient survival. However, since analyzing biomarkers using traditional methods is complex, novel tools have emerged to address this challenge. The aim of this study is to review published research on the impact of artificial intelligence through biomarker analysis to improve survival outcomes in patients with lung cancer. Methods: This study was conducted as a systematic review based on PRISMA guidelines in ۱۴۰۳ through searches in databases like PubMed, Scopus, and Google Scholar using MeSH keywords 'lung cancer,' 'artificial intelligence,' 'biomarkers,' 'prognosis,' and equivalents. After screening, ۱۰۵ articles from ۲۰۲۰ to ۲۰۲۵ were found; after reviewing titles, abstracts, and keywords, ۳۴ articles meeting inclusion criteria were selected. The articles were studied and evaluated by researchers for detailed analysis. Results: The research findings showed that artificial intelligence in lung cancer prognosis falls mainly into these three categories: ۱) Accurate prognosis prediction, in which artificial intelligence models are designed to predict lung cancer prognosis based on imaging data and biomarkers with a higher degree of accuracy. This also includes effectively applying machine learning and deep learning to enhance medical decision support systems. ۲) Identification of effective biomarkers, where these applications incorporate biological and genetic data to improve the efficacy of prognosis prediction and marker identification in lung cancer. ۳) Building comprehensive prognostic models, where these applications integrate image data with biomarkers and clinical data to build multifactorial models for predicting lung cancer prognosis together with serving the physician in clinical decisions. Conclusion: Artificial intelligence has significantly aided in analyzing

نویسندگان

Mohammad Bastani

Bachelor of Health Information Technology, Department of Health Information Technology, School of Paramedical Sciences, Aja University of Medical Sciences, Tehran, Iran

Mohammad Alizadeh

Bachelor of Laboratory Sciences, Department of Laboratory Sciences, School of Paramedical Sciences, Aja University of Medical Sciences, Tehran, Iran

Aynaz Esmailzadeh

Bachelor of Health Information Technology, Department of Health Information Technology, Varastegan Institute for Medical Sciences, Mashhad, Iran

Nahid Mehrabi

Assistant Professor, Department of Health Information Technology, School of Paramedical Sciences, Aja University of Medical Sciences, Tehran, Iran

Mahdi Ghorbani

Assistant Professor, Department of Laboratory Sciences, School of Paramedical Sciences, Aja University of Medical Sciences, Tehran, Iran