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ادغام دوزیومیکس در پرتودرمانی بالینی: مروری سیستماتیک

نوع مقاله : مروری

نویسندگان

1 استادیار مرکز تحقیقات سرطان، دانشگاه علوم پزشکی سمنان، سمنان، ایران

2 دانشیار مرکز تحقیقات بیماری‌های غیر واگیر، دانشگاه علوم پزشکی سبزوار، سبزوار، ایران

3 استادیار گروه فیزیک پزشکی و پرتوشناسی، دانشکده پیراپزشکی، دانشگاه علوم پزشکی خراسان شمالی، بجنورد، ایران

چکیده

زمینه و هدف: دوزیومیکس به‌عنوان یک حوزه نوظهور در پرتودرمانی، بر استخراج و تحلیل ویژگی‌های کمی توزیع دوز تمرکز دارد. این رویکرد در مقایسه با دوزیمتری سنتی که عمدتاً بر معیارهای حجم-دوز متکی است، توانایی درک عمیق‌تری از رابطه بین دوز و نتایج بالینی را فراهم می‌آورد.
مواد و روش­ها: این پژوهش سیستماتیک با پیروی از چک‌لیست PRISMA و جستجوی جامع در پایگاه‌های PubMed، Scopus، ScienceDirect و Google Scholar برای شناسایی مطالعات مرتبط با کلیدواژه‌های «دوزیومیکس» و «پرتودرمانی» انجام شد. مقالات منتشرشده در ۱۰ سال گذشته موردبررسی قرار گرفت. پس از غربالگری عناوین و چکیده‌های 250 مقاله یافت شده، 32 مورد بر اساس چک‌لیست توسط دو نویسنده انتخاب و مورد ارزیابی دقیق‌تر قرار گرفت و سنتز داده‌ها به‌صورت روایتی انجام شد.
یافته­ها: نتایج این پژوهش نشان می‌دهد که دوزیومیکس با ترکیب داده‌های دوز با ابعاد بالا و فنون محاسباتی مدرن نظیر یادگیری ماشین و هوش مصنوعی، توانسته است به بهبود کنترل تومور و کاهش عوارض جانبی در بیماران تحت پرتودرمانی کمک کند. مطالعات کلیدی، پتانسیل پیش‌بینی‌کنندگی دوزیومیکس را در انواع مختلف سرطان، نشان داده و نقش مهمی در بهینه‌سازی تحویل دوز و افزایش دقت مدل‌های احتمال عارضه بافت نرمال (NTCP) ایفا کرده است.
نتیجه‌گیری: ادغام دوزیومیکس در پرتودرمانی بالینی می‌تواند به طراحی درمان‌های شخصی شده کمک کند و فرصتی برای بهینه‌سازی فنون درمانی ایجاد کند. بااین‌حال، چالش‌هایی نظیر ناهمگونی داده‌ها و استانداردسازی باقی‌مانده‌اند که با همکاری‌های چندمرکزی و پیشرفت‌های فنّاورانه باید موردبررسی قرار گیرند تا پتانسیل کامل دوزیومیکس در بالین به ظهور برسد.

تازه های تحقیق

https://scholar.google.com/citations?user=3a9MpdAAAAAJ&hl=en&oi=ao

https://pubmed.ncbi.nlm.nih.gov/?term=Marziyeh+Behmadi

https://scholar.google.com/citations?user=cQPDe4sAAAAJ&hl=en&oi=ao

https://pubmed.ncbi.nlm.nih.gov/?term=Ruhollah+Ghahramani-Asl

https://scholar.google.com/citations?user=mCimRHkAAAAJ&hl=en&oi=ao

https://pubmed.ncbi.nlm.nih.gov/?term=Hamid-Reza+Sadoughi

 

کلیدواژه‌ها

موضوعات

عنوان مقاله [English]

Integrating Dosiomics into Radiotherapy Practice: A Systematic Review

نویسندگان [English]

  • Marziyeh Behmadi 1
  • Ruhollah Ghahramani-Asl 2
  • Hamid-Reza Sadoughi 3

1 Assistant Professor, Cancer Research Center, Semnan University of Medical Sciences, Semnan, Iran Assistant Professor, Medical Physics Department, Faculty of Medicine, Semnan University of Medical Sciences, Semnan, Iran

2 Associate Professor, Non-Communicable Diseases Research Center, Sabzevar University of Medical Sciences, Sabzevar, Iran

3 Assistant Professor, Department of Medical Physics and Radiology, Faculty of Paramedicine, North Khorasan University of Medical Sciences, Bojnurd, Iran

چکیده [English]

 
Introduction: Dosiomics is an emerging field in radiotherapy that focuses on the extraction and analysis of quantitative characteristics of dose distribution patterns. Compared to traditional dosimetry, which primarily relies on volume-dose metrics, dosiomics provides a deeper understanding of the relationship between dose distributions and clinical outcomes.
Materials and Methods: This systematic review was conducted with the PRISMA checklist. A comprehensive search was performed in PubMed, Scopus, ScienceDirect, and Google Scholar to identify studies related to dosiomics in radiotherapy. Articles published within the last 10 years were included. Following the title and abstract screening of 250 articles, two authors selected 32 articles for evaluation of the full texts, and a narrative synthesis of the data was performed.
Results: The findings indicated that dosiomics, by integrating high-dimensional dose data with modern computational techniques such as machine learning and artificial intelligence, can improve tumor control and reduce side effects in patients undergoing radiotherapy. Main studies have demonstrated the predictive potential of dosiomics across various cancer types, emphasizing its significant role in optimizing dose delivery and enhancing the accuracy of normal tissue complication probability (NTCP) models.
Conclusion: Integrating dosiomics into clinical radiotherapy can facilitate the design of personalized treatment approaches and create opportunities for further optimizing therapeutic techniques. However, challenges such as data heterogeneity and the need for standardization must be addressed through multicenter collaborations and technological advancements to unlock the full potential of dosiomics in clinical practice.

کلیدواژه‌ها [English]

  • Dosiomics
  • Radiotherapy
  • Dose Distribution
  • Machine Learning
  • Deep Learning
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مجله دانشگاه علوم پزشکی سبزوار
دوره 32، شماره 3
مرداد و شهریور 1404
صفحه 245-267
فایل ها
سابقه مقاله
  • تاریخ دریافت: 17 دی 1403
  • تاریخ بازنگری: 07 اسفند 1403
  • تاریخ پذیرش: 07 اسفند 1403
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