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在仓鼠颊囊模型中,通过新型无标记暗场成像定义的致癌过程中不同的血管生成变化。

Distinct Angiogenic Changes during Carcinogenesis Defined by Novel Label-Free Dark-Field Imaging in a Hamster Cheek Pouch Model.

作者信息

Hu Fangyao, Martin Hannah, Martinez Amy, Everitt Jeffrey, Erkanli Alaattin, Lee Walter T, Dewhirst Mark, Ramanujam Nimmi

机构信息

Department of Biomedical Engineering, Duke University, Durham, North Carolina.

Department of Pathology, Duke University Medical Center, Durham, North Carolina.

出版信息

Cancer Res. 2017 Dec 15;77(24):7109-7119. doi: 10.1158/0008-5472.CAN-17-1058. Epub 2017 Oct 11.

Abstract

There remain gaps in knowledge concerning how vascular morphology evolves during carcinogenesis. In this study, we imaged neovascularization by label-free dark-field microscopy of a 7,12-Dimethylbenz[a]anthracene (DMBA)-induced hamster cheek pouch model of oral squamous cell carcinoma (SCC). Wavelength-dependent imaging revealed distinct vascular features at different imaging depths and vessel sizes. Vascular tortuosity increased significantly in high-risk lesions, whereas diameter decreased significantly in hyperplastic and SCC lesions. Large vessels preserved the same trends seen in the original images, whereas small vessels displayed different trends, with length and diameter increasing during carcinogenesis. On the basis of these data, we developed and validated a classification algorithm incorporating vascular features from different vessel masks. Receiver operator curves generated from the classification results demonstrated high accuracies in discriminating normal and hyperplasia from high-grade lesions (AUC > 0.94). Overall, these results provided automated imaging of vasculature in the earliest stages of carcinogenesis from which one can extract robust endpoints. The optical toolbox described here is simple, low-cost and portable, and can be used in a variety of health care and research settings for cancer prevention and pharmacology research. .

摘要

关于血管形态在致癌过程中如何演变,仍存在知识空白。在本研究中,我们通过对7,12-二甲基苯并[a]蒽(DMBA)诱导的口腔鳞状细胞癌(SCC)仓鼠颊囊模型进行无标记暗场显微镜成像,对新生血管形成进行了成像。波长依赖性成像揭示了不同成像深度和血管大小下的独特血管特征。在高危病变中,血管迂曲度显著增加,而在增生性病变和SCC病变中,血管直径显著减小。大血管保持了原始图像中所见的相同趋势,而小血管则呈现不同趋势,在致癌过程中长度和直径增加。基于这些数据,我们开发并验证了一种分类算法,该算法整合了来自不同血管掩码的血管特征。分类结果生成的受试者工作特征曲线在区分正常和增生与高级别病变方面显示出高准确率(AUC>0.94)。总体而言,这些结果提供了致癌最早阶段血管系统的自动成像,从中可以提取可靠的终点指标。这里描述的光学工具箱简单、低成本且便于携带,可用于各种医疗保健和研究环境中的癌症预防和药理学研究。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/dfb9/6221460/d5342d514d69/nihms-912818-f0001.jpg

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