Lu Tongtong, Jorns Julie M, Ye Dong Hye, Patton Mollie, Gilat-Schmidt Taly, Yen Tina, Yu Bing
Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI 53233 & 53226, USA.
Department of Pathology & Laboratory Medicine, Medical College of Wisconsin, Milwaukee, WI 53226, USA.
Proc SPIE Int Soc Opt Eng. 2023 Jan-Feb;12368. doi: 10.1117/12.2649552. Epub 2023 Mar 6.
Positive margin status after breast-conserving surgery (BCS) is a predictor of higher rates of local recurrence. Intraoperative margin assessment aims to achieve negative surgical margin status at the first operation, thus reducing the re-excision rates that are usually associated with potential surgical complications, increased medical costs, and mental pressure on patients. Microscopy with ultraviolet surface excitation (MUSE) can rapidly image tissue surfaces with subcellular resolution and sharp contrasts by utilizing the nature of the thin optical sectioning thickness of deep ultraviolet light. We have previously imaged 66 fresh human breast specimens that were topically stained with propidium iodide and eosin Y using a customized MUSE system. To achieve objective and automated assessment of MUSE images, a machine learning model is developed for binary (tumor vs. normal) classification of obtained MUSE images. Features extracted by texture analysis and pre-trained convolutional neural networks (CNN) have been investigated for sample descriptions. A sensitivity, specificity, and accuracy better than 90% have been achieved for detecting tumorous specimens. The result suggests the potential of MUSE with machine learning being utilized for intraoperative margin assessment during BCS.
保乳手术（BCS）后切缘阳性是局部复发率较高的一个预测指标。术中切缘评估旨在在首次手术时实现手术切缘阴性，从而降低通常与潜在手术并发症、医疗成本增加以及患者心理压力相关的再次切除率。利用深紫外光薄光学切片厚度的特性，紫外表面激发显微镜（MUSE）能够以亚细胞分辨率和鲜明对比度快速对组织表面成像。我们之前使用定制的MUSE系统对66个用碘化丙啶和伊红Y进行局部染色的新鲜人类乳腺标本进行了成像。为了实现对MUSE图像的客观和自动评估，开发了一种机器学习模型，用于对获得的MUSE图像进行二元（肿瘤与正常）分类。已研究通过纹理分析和预训练卷积神经网络（CNN）提取的特征用于样本描述。在检测肿瘤标本方面，灵敏度、特异性和准确率均达到了90%以上。结果表明MUSE结合机器学习在保乳手术术中切缘评估方面具有应用潜力。
