โก Quick Summary
This systematic review and meta-analysis protocol aims to evaluate the effectiveness of deep learning-based autosegmentation for pelvic malignancies in radiation oncology. By synthesizing data from various studies, the review seeks to enhance clinicians’ confidence in implementing these AI-driven tools in routine practice.
๐ Key Details
- ๐ Study Period: January 2004 to December 2025
- ๐ Databases Searched: Medline (PubMed), Scopus, Cochrane Library, EMBASE, Web of Science
- ๐ฅ Review Process: Two reviewers for screening and data extraction, with a third for conflict resolution
- ๐ Reporting Standards: PRISMA guidelines for systematic reviews
๐ Key Takeaways
- ๐ค Deep learning models represent the fourth generation of autosegmentation technology.
- ๐ Performance Metrics will include quantitative indices, qualitative assessments, and dosimetry.
- ๐ Time Savings and efficiency improvements are anticipated with AI integration.
- ๐ฏ Focus Areas include accuracy for targets and various pelvic organs.
- ๐ Systematic Review Registration: Registered on PROSPERO (ID: CRD42024491066).
- ๐ Global Relevance: The findings will have implications for radiation oncology practices worldwide.
๐ Background
The integration of artificial intelligence in healthcare has been transformative, particularly in radiation oncology. The use of deep learning for autosegmentation has emerged as a promising strategy to enhance the precision and efficiency of treatment planning for pelvic malignancies. This systematic review aims to consolidate existing research and provide a comprehensive evaluation of these technologies.
๐๏ธ Study
The systematic review will involve a thorough search of multiple databases to identify relevant studies published between January 2004 and December 2025. The review process will include title, abstract, and full-text screening based on predefined eligibility criteria. Two independent reviewers will assess the quality of the studies and extract data, ensuring a robust and reliable synthesis of findings.
๐ Results
The anticipated outcomes of this review will focus on the accuracy of deep learning models in autosegmentation for pelvic malignancies. The performance will be evaluated using various quantitative and qualitative metrics, which will help in understanding the potential benefits of these technologies in clinical settings.
๐ Impact and Implications
The findings from this systematic review could significantly influence the adoption of AI-driven autosegmentation tools in radiation oncology. By demonstrating their accuracy and efficiency, this research may encourage clinicians to integrate these technologies into their practice, ultimately improving patient outcomes and streamlining treatment workflows.
๐ฎ Conclusion
This systematic review and meta-analysis protocol highlights the potential of deep learning in enhancing the accuracy of autosegmentation for pelvic malignancies. As the field of radiation oncology continues to evolve, embracing these innovative technologies could lead to improved treatment planning and patient care. We look forward to the results of this comprehensive review and its implications for clinical practice.
๐ฌ Your comments
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Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol.
Abstract
BACKGROUND: Deep learning (DL)-based artificial intelligence (AI) models, the fourth generation in autosegmentation, have been adopted both for commercial and research applications worldwide and have shown great promise as reliable and comprehensive resource strategies for radiotherapy workflows.
METHODS: A comprehensive search will be conducted on Medline (PubMed), Scopus, the Cochrane Library, EMBASE, and Web of Science between January 2004 and December 2025. We will conduct a title, abstract, and full-text screening of all studies as per the eligibility criteria. Two reviewers will be involved in screening studies, quality appraisal, and data extraction, and a third reviewer will be consulted to resolve conflicts. Based on data availability, the data will be synthesised via meta-analysis and narrative synthesis. The reporting will be performed using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, ensuring the reliability and validity of the results.
DISCUSSION: The model’s performance will be assessed using various quantitative indices, qualitative tools, timesavings, and dosimetry. This review will enable us to determine the accuracy of autosegmentation for targets and various pelvic organs, boosting clinicians’ confidence in facilitating the clinical implementation of such tools in routine clinical practice.
SYSTEMATIC REVIEW REGISTRATION: The protocol is prospectively registered on PROSPERO. The registration ID is CRD42024491066.
Author: [‘Menon SS’, ‘Velu U’, ‘Gurram L’, ‘Paramasivam G’, ‘Kn M’, ‘Patil DS’, ‘Dhyani VS’, ‘Jathanna RD’, ‘Lewis S’]
Journal: Syst Rev
Citation: Menon SS, et al. Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol. Segmentation for pelvic malignancies in radiation oncology practice: a systematic review and meta-analysis protocol. 2026; (unknown volume):(unknown pages). doi: 10.1186/s13643-026-03173-2