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Classification of breast tissue density by optical transillumination spectroscopy: optical and physiological effects governing predictive value

机译:光学透明度光谱分类乳腺组织密度:控制预测值的光学和生理效应

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Preventive oncology is in need of a risk assessment technique that can identify individuals at high risk for breast cancer and that has the ability to monitor the efficacy of a risk reducing intervention. Optical transillumination spectroscopy (OTS) was shown to give information about breast tissue composition and tissue density. OTS is non-invasive, and in contrast to mammography, uses non-ionizing radiation. It is safe and can be used frequently on younger women, potentially permitting early risk detection and thus increasing the time available for risk reduction interventions to assert their influence. Before OTS can be used as a risk assessment and/or monitoring technique, its predictive ability needs to be demonstrated and maximized through the construction of mathematical models relating OTS and risk. To establish a correlation between OTS and mammographic density, Principle Components Analysis (PCA), using risk classification, is employed. PCA scores are presented in 3D cluster plots and a plane of differentiation that separates high and low tissue density is used to calculate the predictive value. Stratificaiton of PCA for measurement position on the breast in cranial-caudal projection is introduced. Analysis of PCA scores as a function of the volunteer's age and body mass index (BMI) is examined. A small but significant correlation between the component scores and age or BMI is noted but the correlation is dependnet on the tissue density category examined. Correction of the component scores for age and BMI is not recommended, since a priori knowledge of a women's tissue density is required. Stratification for the center and distal measurement positions provides a predictive value for OTS above 96%.
机译:预防性肿瘤可能需要风险评估技术,可以识别乳腺癌高风险的个体,并且能够监测风险减少干预的效果。显示光学透射光谱(OTS),显示有关乳房组织成分和组织密度的信息。 OTS是非侵入性的,与乳房X线照相术相比,使用非电离辐射。它是安全的,可以经常用于年轻女性,可能允许早期风险检测,从而增加可用于减少风险减少干预的时间以断言它们的影响力。在OTS可以用作风险评估和/或监测技术之前,需要通过构建有关OTS和风险的数学模型来证明和最大化其预测能力。为了建立OTS和乳房X光密度之间的相关性,采用了使用风险分类的原理分析分析(PCA)。 PCA分数呈现在3D簇图中,分离高低组织密度的差异平面用于计算预测值。介绍了颅骨投影中乳房测量位置的PCA的STRATIFITON。检查了PCA分数作为志愿者年龄和体重指数(BMI)的函数。注意组分评分和年龄或BMI之间的小但重要的相关性,但相关性是在检查的组织密度类别上的相关性。不建议使用年龄和BMI的组件分数校正,因为需要先验的女性组织密度的知识。中心和远端测量位置的分层为96%的OTS提供了预测值。

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