Breast composition measurements using retrospective standard mammogram form (SMF)
Breast composition measurements using retrospective standard mammogram form (SMF)
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DOI：
10.1088/0031-9155/51/11/001
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发表时间：
2006-06-07
影响因子：
3.5
通讯作者：
Brady, M.
中科院分区：
文献类型：
--
作者：
Highnam, R.;Pan, X.;Brady, M.
X射线乳房X线照片的标准乳房X线照片形式（SMF）表示是乳房的标准化定量表示，从中可以轻松估计非脂肪组织的体积和乳房密度，这两者在确定乳腺癌风险方面具有重要意义。之前的SMF理论分析表明，需要一组完整且大量的校准数据（如mAs和kVp）来生成真实的乳房组成测量值，但仍有许多有趣的试验回顾性收集了没有校准数据的图像。本文的主要贡献是重新审视我们以前的理论分析SMF的校准数据中的误差，并显示如何以及为什么理论分析不匹配的结果，从实际实施的SMF。特别是，我们展示了如何通过估计每幅图像的乳房厚度，有效地补偿校准数据中的任何误差。为了说明我们的研究结果，SMF（2.2 beta版）的当前实施是在1988-2002年期间从6个地点拍摄的4028张数字化胶片屏幕乳房X线照片上运行的，使用和不使用已知的校准数据。结果表明，在没有任何校准数据的情况下运行的SMF实现生成的结果与使用完整的校准数据集运行时显示出很强的关系，最重要的是，与专家使用现有技术对乳房成分的视觉评估有很强的关系。SMF在与乳腺癌相关的大型流行病学研究中显示出相当大的前景，这些研究需要自动分析多年前很少或没有校准数据的大量电影。
The standard mammogram form (SMF) representation of an x-ray mammogram is a standardized, quantitative representation of the breast from which the volume of non-fat tissue and breast density can be easily estimated, both of which are of significant interest in determining breast cancer risk. Previous theoretical analysis of SMF had suggested that a complete and substantial set of calibration data (such as mAs and kVp) would be needed to generate realistic breast composition measures and yet there are many interesting trials that have retrospectively collected images with no calibration data. The main contribution of this paper is to revisit our previous theoretical analysis of SMF with respect to errors in the calibration data and to show how and why that theoretical analysis did not match the results from the practical implementations of SMF. In particular, we show how by estimating breast thickness for every image we are, effectively, compensating for any errors in the calibration data. To illustrate our findings, the current implementation of SMF (version 2.2 beta) was run over 4028 digitized film-screen mammograms taken from six sites over the years 1988-2002 with and without using the known calibration data. Results show that the SMF implementation running without any calibration data at all generates results which display a strong relationship with when running with a complete set of calibration data, and, most importantly, to an expert's visual assessment of breast composition using established techniques. SMF shows considerable promise in being of major use in large epidemiological studies related to breast cancer which require the automated analysis of large numbers of films from many years previously where little or no calibration data is available.