Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.
Structured Illumination Microscopy and a Quantitative Image Analysis for the Detection of Positive Margins in a Pre-Clinical Genetically Engineered Mouse Model of Sarcoma.
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DOI：
10.1371/journal.pone.0147006
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发表时间：
2016
期刊：
影响因子：
3.7
通讯作者：
Ramanujam N
中科院分区：
文献类型：
--
作者：
Fu HL;Mueller JL;Whitley MJ;Cardona DM;Willett RM;Kirsch DG;Brown JQ;Ramanujam N
术中对手术切缘的评估是确保肿瘤不残留在患者体内的关键。此前，我们开发了一种荧光结构照明显微镜系统，其单次视野为2.1×1.6 mm(3.4mm2)，亚细胞分辨率(4.4mm2)(μm)。这项研究的目的是测试这项技术在检测基因工程小鼠肉瘤模型中残留疾病的实用性。原发软组织肉瘤发生在后肢，手术切除肿瘤后，相关边缘用吖啶橙(AO)染色，这是一种鲜艳的染色细胞核和纤维组织的染料。用SIM系统对这些组织进行成像，主要目的是可视化肿瘤细胞核的荧光特征。考虑到背景组织的异质性(脂肪组织和肌肉的存在)，一种称为最大稳定极值区域(MSER)的算法被优化并应用于图像以特定地分割核特征。使用Logistic回归模型通过计算分割特征的面积分数和形状来将组织部位分类为阳性或阴性，并通过改变概率阈值生成最终的接收器算子曲线(ROC)。根据ROC曲线，该模型能够对肿瘤和正常组织进行分类，敏感度为77%，特异度为81%(约登指数)。对于模型性能的无偏测量，它被应用于一个单独的验证数据集，其结果是73%的灵敏度和80%的特异度。当这种方法应用于具有代表性的整个切缘时，对于50%的肿瘤概率阈值，来自负切缘的所有区域中只有1.2%超过该阈值，而来自正切缘的所有区域中超过14.8%的区域超过该阈值。
Intraoperative assessment of surgical margins is critical to ensuring residual tumor does not remain in a patient. Previously, we developed a fluorescence structured illumination microscope (SIM) system with a single-shot field of view (FOV) of 2.1×1.6 mm (3.4 mm2) and sub-cellular resolution (4.4 μm). The goal of this study was to test the utility of this technology for the detection of residual disease in a genetically engineered mouse model of sarcoma. Primary soft tissue sarcomas were generated in the hindlimb and after the tumor was surgically removed, the relevant margin was stained with acridine orange (AO), a vital stain that brightly stains cell nuclei and fibrous tissues. The tissues were imaged with the SIM system with the primary goal of visualizing fluorescent features from tumor nuclei. Given the heterogeneity of the background tissue (presence of adipose tissue and muscle), an algorithm known as maximally stable extremal regions (MSER) was optimized and applied to the images to specifically segment nuclear features. A logistic regression model was used to classify a tissue site as positive or negative by calculating area fraction and shape of the segmented features that were present and the resulting receiver operator curve (ROC) was generated by varying the probability threshold. Based on the ROC curves, the model was able to classify tumor and normal tissue with 77% sensitivity and 81% specificity (Youden’s index). For an unbiased measure of the model performance, it was applied to a separate validation dataset that resulted in 73% sensitivity and 80% specificity. When this approach was applied to representative whole margins, for a tumor probability threshold of 50%, only 1.2% of all regions from the negative margin exceeded this threshold, while over 14.8% of all regions from the positive margin exceeded this threshold.