Microvascular Ultrasonic Imaging of Angiogenesis Identifies Tumors in a Murine Spontaneous Breast Cancer Model.

IF 3.3 Q2 ENGINEERING, BIOMEDICAL International Journal of Biomedical Imaging Pub Date : 2020-02-06 eCollection Date: 2020-01-01 DOI:10.1155/2020/7862089
Sarah E Shelton, Jodi Stone, Fei Gao, Donglin Zeng, Paul A Dayton
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引用次数: 6

Abstract

The purpose of this study is to determine if microvascular tortuosity can be used as an imaging biomarker for the presence of tumor-associated angiogenesis and if imaging this biomarker can be used as a specific and sensitive method of locating solid tumors. Acoustic angiography, an ultrasound-based microvascular imaging technology, was used to visualize angiogenesis development of a spontaneous mouse model of breast cancer (n = 48). A reader study was used to assess visual discrimination between image types, and quantitative methods utilized metrics of tortuosity and spatial clustering for tumor detection. The reader study resulted in an area under the curve of 0.8, while the clustering approach resulted in the best classification with an area under the curve of 0.95. Both the qualitative and quantitative methods produced a correlation between sensitivity and tumor diameter. Imaging of vascular geometry with acoustic angiography provides a robust method for discriminating between tumor and healthy tissue in a mouse model of breast cancer. Multiple methods of analysis have been presented for a wide range of tumor sizes. Application of these techniques to clinical imaging could improve breast cancer diagnosis, as well as improve specificity in assessing cancer in other tissues. The clustering approach may be beneficial for other types of morphological analysis beyond vascular ultrasound images.

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血管生成的微血管超声成像识别小鼠自发性乳腺癌模型中的肿瘤。
本研究的目的是确定微血管扭曲是否可以作为肿瘤相关血管生成的成像生物标志物,以及这种生物标志物的成像是否可以作为定位实体肿瘤的一种特异性和敏感性方法。声学血管造影是一种基于超声的微血管成像技术,用于观察自发性乳腺癌小鼠模型(n = 48)的血管生成发育。一项读者研究用于评估图像类型之间的视觉区分,定量方法利用扭曲度和空间聚类指标进行肿瘤检测。读者研究的曲线下面积为0.8,聚类方法的最佳分类曲线下面积为0.95。定性和定量方法均得出敏感性与肿瘤直径的相关性。超声血管造影血管几何成像提供了一个强大的方法来区分肿瘤和健康组织的小鼠乳腺癌模型。多种分析方法已经提出了广泛的肿瘤大小范围。将这些技术应用于临床影像学,可以提高乳腺癌的诊断,也可以提高其他组织中癌症评估的特异性。聚类方法可能有利于血管超声图像以外的其他类型的形态学分析。
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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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