Clinical Feasibility of Low-Magnification Fluorescence Imaging for Breast Cancer Margin Detection Using Texture Analysis and Deep Learning

2026-08-11

AI Summary

This study addresses the clinical need for efficient and accurate intraoperative assessment of breast cancer margins, where high-magnification imaging is often limited by small field-of-view and slow acquisition. For the first time, it systematically compares 4× and 10× MUSE fluorescence imaging performance, integrating Local Binary Pattern (LBP) texture analysis with Vision Transformer (ViT)-based deep learning for tissue classification. Results demonstrate that 4× imaging achieves 96.30% sensitivity, 100% specificity, and 98.18% accuracy under the ViT model, while LBP yields consistent 96.67% accuracy across both magnifications. These findings indicate that low-magnification imaging can deliver diagnostic accuracy comparable to high-magnification approaches while substantially improving field-of-view coverage and imaging speed, thereby enhancing practicality in intraoperative settings.

Abstract

High-resolution images of unprocessed surgical breast tissue can be obtained using microscopy with ultraviolet surface excitation (MUSE). This technique is considered a promising method for checking surgical margins during breast cancer surgery. In this study, MUSE images at 4x and 10x magnifications were compared using patch-level classification methods. Texture analysis (TA) based on local binary patterns (LBP) and deep learning (DL) with a base Vision Transformer (ViT) model were used. Both methods achieved similar performance at both magnifications. Using DL method, both 4x and 10x magnifications achieved 96.30% sensitivity, 100% specificity and 98.18% accuracy. Using TA method, 4x achieved better specificity (100% vs 93.33%) and 10x yielded higher sensitivity (100% vs 93.33%), but both had the same accuracy (96.67%). No clear improvement in performance was observed with 10x magnification. These results show that 4x imaging achieves the same diagnostic accuracy as 10x imaging. At the same time, 4x offers a larger field of view and faster image capture. Therefore, lower magnification can be effectively used in MUSE systems for accurate and efficient intraoperative margin assessment.

Problem

• breast cancer margin detection
• low-magnification imaging
• intraoperative assessment
• fluorescence imaging
• MUSE

Innovation

• low-magnification imaging
• MUSE
• texture analysis
• Vision Transformer
• intraoperative margin assessment