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首页> 外文期刊>Applied optics >Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis
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Monte Carlo-based inverse model for calculating tissue optical properties. Part II: Application to breast cancer diagnosis

机译:基于蒙特卡洛的逆模型,用于计算组织的光学特性。第二部分:在乳腺癌诊断中的应用

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摘要

The Monte Carlo-based inverse model of diffuse reflectance described in part I of this pair of companion papers was applied to the diffuse reflectance spectra of a set of 17 malignant and 24 normal-benign ex vivo human breast tissue samples. This model allows extraction of physically meaningful tissue parameters, which include the concentration of absorbers and the size and density of scatterers present in tissue. It was assumed that intrinsic absorption could be attributed to oxygenated and deoxygenated hemoglobin and beta-carotene, that scattering could be modeled by spheres of a uniform size distribution, and that the refractive indices of the spheres and the surrounding medium are known. The tissue diffuse reflectance spectra were evaluated over a wavelength range of 400-600 nm. The extracted parameters that showed the statistically most significant differences between malignant and nonmalignant breast tissues were hemoglobin saturation and the mean reduced scattering coefficient. Malignant tissues showed decreased hemoglobin saturation and an increased mean reduced scattering coefficient compared with nonmalignant tissues. A support vector machine classification algorithm was then used to classify a sample as malignant or nonmalignant based on these two extracted parameters and produced a cross-validated sensitivity and specificity of 82percent and 92percent, respectively.
机译:在这对伴侣论文的第一部分中描述的基于蒙特卡罗的漫反射逆模型被应用于一组17个恶性和24个正常良性离体人体乳房组织样品的漫反射光谱。该模型允许提取具有物理意义的组织参数,其中包括吸收剂的浓度以及组织中存在的散射体的大小和密度。假定固有吸收可以归因于氧化和脱氧的血红蛋白和β-胡萝卜素,散射可以由大小分布均匀的球模拟,并且已知球和周围介质的折射率。在400-600nm的波长范围内评估组织漫反射光谱。提取出的参数显示了恶性和非恶性乳腺组织之间的统计学上最显着的差异是血红蛋白饱和度和平均降低的散射系数。与非恶性组织相比,恶性组织显示血红蛋白饱和度降低,平均散射系数降低。然后,基于这两个提取的参数,使用支持向量机分类算法将样本分类为恶性或非恶性,并产生交叉验证的敏感性和特异性,分别为82%和92%。

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