Analysis of Deep Ultraviolet Fluorescence Images for Intraoperative Breast Tumor Margin Assessment.
Analysis of Deep Ultraviolet Fluorescence Images for Intraoperative Breast Tumor Margin Assessment.
复制标题
用于术中乳腺肿瘤边缘评估的深紫外荧光图像分析。
DOI：
10.1117/12.2649552
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Yu,Bing
中科院分区：
文献类型：
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作者：
Lu,Tongtong;Jorns,JulieM;Ye,DongHye;Patton,Mollie;Gilat-Schmidt,Taly;Yen,Tina;Yu,Bing
保乳手术（BCS）后切缘阳性是局部复发率较高的预测因子。术中切缘评估的目的是在第一次手术时获得阴性手术切缘状态，从而降低再切除率，再切除率通常与潜在的手术并发症、增加的医疗费用和患者的精神压力相关。紫外表面激发显微镜（MUSE）可以利用深紫外光的薄光学切片厚度的性质，以亚细胞分辨率和鲜明的对比度快速成像组织表面。我们以前成像66新鲜的人类乳房标本，局部碘化丙啶和曙红Y染色使用定制的MUSE系统。为了实现对MUSE图像的客观和自动化评估，开发了一种机器学习模型，用于对所获得的MUSE图像进行二进制（肿瘤与正常）分类。纹理分析和预训练的卷积神经网络（CNN）提取的特征已被研究用于样本描述。检测肿瘤标本的敏感性、特异性和准确性均优于90%。结果表明，MUSE和机器学习在BCS期间用于术中切缘评估的潜力。
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.