4倍低倍荧光成像可实现与10倍高倍相同的乳腺癌切缘检测精度。
Clinical Feasibility of Low-Magnification Fluorescence Imaging for Breast Cancer Margin Detection Using Texture Analysis and Deep Learning
- 采用纹理分析与视觉变压器模型，对比4倍和10倍放大图像的切缘分类效果。
- 4倍与10倍成像在深度学习下均达96.3%敏感度、100%特异度和98.18%准确率。
- 4倍成像视野更大、采集更快，适合术中快速精准评估乳腺切缘。
利用紫外表面激发显微技术（MUSE）可获取未经处理的乳腺手术组织高分辨率图像，被认为是一种有前景的术中切缘检测方法。本研究对比了4倍和10倍放大下的MUSE图像，采用基于局部二值模式（LBP）的纹理分析（TA）与基础视觉变压器（ViT）模型的深度学习（DL）方法进行像素块级分类。两种方法在不同放大倍数下表现相近：深度学习方法下，4倍和10倍均达到96.30%敏感度、100%特异度和98.18%准确率；纹理分析方法下，4倍具更高特异度（100% vs 93.33%），10倍具更高敏感度（100% vs 93.33%），但准确率相同（96.67%）。未观察到10倍放大带来性能提升。结果表明，4倍成像可实现与10倍相当的诊断准确性，同时具有更广视野和更快采集速度，因此可在MUSE系统中有效用于术中高效精准的切缘评估。
原文摘要 · Abstract (English)
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.
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