Emerging Technologies for Real-Time Intraoperative Margin Assessment in Future Breast-Conserving Surgery

Area of Science:

• Oncology
• Surgical Pathology
• Medical Imaging

Background:

• Clean surgical margins are critical for preventing breast cancer recurrence after breast-conserving surgery (BCS).
• Current intraoperative pathologic methods like frozen section analysis are time-consuming and complex, limiting global use.
• There is a need for faster, more accessible methods for real-time margin assessment.

Purpose of the Study:

• To review traditional and novel intraoperative techniques for assessing surgical margins in BCS.
• To discuss the advantages and disadvantages of various diagnostic methods.
• To propose the integration of new technologies with artificial intelligence (AI) for improved margin assessment.

Main Methods:

• Summary of conventional pathologic diagnostic methods (frozen section, imprint cytology).
• Review of emerging non-pathologic intraoperative techniques including imaging (spectroscopy, tomography, MRI), microscopy, fluorescent probes, and multimodal approaches.
• Discussion of AI algorithms for analyzing margin status.

Main Results:

• Traditional methods face limitations in speed and complexity.
• Emerging techniques show promise for real-time assessment of breast margins in live tissues.
• AI offers potential for standardized data analysis, reducing reliance on expert pathologists.

Conclusions:

• Integrating advanced imaging and AI with novel techniques can enhance real-time intraoperative margin assessment in BCS.
• Future research should focus on developing and validating these combined approaches for wider clinical adoption.
• Improved margin assessment can lead to reduced recurrence rates and better patient outcomes in breast cancer surgery.