Suppr超能文献

光学光谱监测乳腺组织地貌,以检测乳腺肿瘤切缘的残留病变。

Optical spectral surveillance of breast tissue landscapes for detection of residual disease in breast tumor margins.

机构信息

Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America.

出版信息

PLoS One. 2013 Jul 26;8(7):e69906. doi: 10.1371/journal.pone.0069906. Print 2013.

Abstract

We demonstrate a strategy to "sense" the micro-morphology of a breast tumor margin over a wide field of view by creating quantitative hyperspectral maps of the tissue optical properties (absorption and scattering), where each voxel can be deconstructed to provide information on the underlying histology. Information about the underlying tissue histology is encoded in the quantitative spectral information (in the visible wavelength range), and residual carcinoma is detected as a shift in the histological landscape to one with less fat and higher glandular content. To demonstrate this strategy, fully intact, fresh lumpectomy specimens (n = 88) from 70 patients were imaged intra-operatively. The ability of spectral imaging to sense changes in histology over large imaging areas was determined using inter-patient mammographic breast density (MBD) variation in cancer-free tissues as a model system. We discovered that increased MBD was associated with higher baseline β-carotene concentrations (p = 0.066) and higher scattering coefficients (p = 0.007) as measured by spectral imaging, and a trend toward decreased adipocyte size and increased adipocyte density as measured by histological examination in BMI-matched patients. The ability of spectral imaging to detect cancer intra-operatively was demonstrated when MBD-specific breast characteristics were considered. Specifically, the ratio of β-carotene concentration to the light scattering coefficient can report on the relative amount of fat to glandular density at the tissue surface to determine positive margin status, when baseline differences in these parameters between patients with low and high MBD are taken into account by the appropriate selection of threshold values. When MBD was included as a variable a priori, the device was estimated to have a sensitivity of 74% and a specificity of 86% in detecting close or positive margins, regardless of tumor type. Superior performance was demonstrated in high MBD tissue, a population that typically has a higher percentage of involved margins.

摘要

我们展示了一种通过创建组织光学特性(吸收和散射)的定量高光谱图来“感知”乳房肿瘤边缘的微观形态的策略,其中每个体素都可以进行分解,以提供有关潜在组织学的信息。关于潜在组织学的信息编码在定量光谱信息中(在可见波长范围内),并且残留的癌被检测为组织学景观向脂肪含量较低且腺体含量较高的方向转变。为了证明这一策略,我们对 70 名患者的 88 例完整的新鲜保乳术标本进行了术中成像。使用无癌组织中患者间乳房密度(MBD)变化作为模型系统,确定了光谱成像在大成像区域中感知组织学变化的能力。我们发现,MBD 增加与基线 β-胡萝卜素浓度升高(p = 0.066)和散射系数升高(p = 0.007)相关,在 BMI 匹配的患者中,通过组织学检查发现脂肪细胞大小减小和脂肪细胞密度增加的趋势。当考虑 MBD 特异性乳房特征时,证明了光谱成像在术中检测癌症的能力。具体来说,当考虑患者之间这些参数的基线差异时,β-胡萝卜素浓度与光散射系数的比值可以报告组织表面脂肪与腺体密度的相对量,以确定阳性边缘状态,通过适当选择阈值值。当 MBD 作为变量先验时,无论肿瘤类型如何,该设备估计在检测接近或阳性边缘时的灵敏度为 74%,特异性为 86%。在 MBD 较高的组织中表现出更好的性能,这是一个通常具有更高百分比受累边缘的人群。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae21/3724737/6b236b30f602/pone.0069906.g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验