Related Experiment Video
Updated: Jun 26, 2025
04:45
Intraoperative Assessment of Resection Margins in Oral Cavity Cancer: This is the Way
Published on: May 10, 2021
3.8K
Diagnostic accuracy of intraoperative methods for margin assessment in breast cancer surgery: A systematic review &
Gavin P Dowling1, Cian M Hehir1, Gordon R Daly1
1Department of Surgery, Royal College of Surgeons in Ireland (RCSI), University of Medicine and Health Sciences, Dublin, Ireland; Department of Surgery, Beaumont Hospital, Dublin, Ireland.
Breast (Edinburgh, Scotland)
|May 17, 2024
Summary
Intraoperative breast margin assessment techniques like cytology and optical spectroscopy show high diagnostic accuracy. Emerging technologies aim to match this accuracy with improved speed and usability for better breast cancer surgery outcomes.
Area of Science:
- Surgical Oncology
- Pathology
- Medical Imaging
Background:
- Achieving negative margins in breast surgery is crucial for patient outcomes.
- Various intraoperative techniques exist to assess surgical margins.
- Standard histopathology has limitations in providing timely intraoperative feedback.
Purpose of the Study:
- To systematically review and determine the pooled diagnostic accuracy of intraoperative breast margin assessment techniques.
- To compare the effectiveness of different methods evaluated in clinical practice.
Main Methods:
- Systematic literature search adhering to PRISMA guidelines.
- Inclusion of clinical studies with raw diagnostic accuracy data compared to final histopathology.
- Bivariate diagnostic meta-analysis to calculate pooled sensitivity and specificity.
Main Results:
- Sixty-one studies were included in the meta-analysis.
- Cytology demonstrated the highest diagnostic accuracy (sensitivity 0.92, specificity 0.95).
- Optical spectroscopy also showed good accuracy (sensitivity 0.86, specificity 0.92).
Conclusions:
- Cytology, optical spectroscopy, and frozen section offer the highest diagnostic accuracy for intraoperative margin assessment.
- Current limitations include long turnaround times and resource intensity.
- Future technologies aim to enhance speed and usability while maintaining diagnostic performance.
