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

A study using microscopy with ultraviolet surface excitation (MUSE) found that 4x magnification achieves the same diagnostic accuracy as 10x for breast cancer margin detection, with deep learning (DL) achieving 96.30% sensitivity, 100% specificity, and 98.18% accuracy at both magnifications. Texture analysis (TA) yielded 96.67% accuracy at both magnifications, with 4x providing better specificity (100% vs 93.33%) and 10x higher sensitivity (100% vs 93.33%). The findings suggest lower magnification can be effectively used in MUSE systems for faster intraoperative margin assessment.

arXiv:2608.11317v1 Announce Type: new 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.