Highnam R, Pan X, Warren R, Jeffreys M, Davey Smith G, Brady M
Siemens Molecular Imaging Ltd, Hythe Bridge Street, Oxford, UK.
Phys Med Biol. 2006 Jun 7;51(11):2695-713. doi: 10.1088/0031-9155/51/11/001. Epub 2006 May 9.
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 2.2beta) 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.
乳腺X线摄影的标准乳房造影图（SMF）表示是乳房的一种标准化定量表示，从中可以轻松估算出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都具有重要意义。先前对SMF的理论分析表明，需要一套完整且大量的校准数据（如毫安秒和千伏峰值）来生成逼真的乳房成分测量值，然而，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先前关于校准数据误差的SMF理论分析，并展示该理论分析与SMF实际应用结果不匹配的方式和原因。特别是，我们展示了如何通过估计每张图像的乳房厚度，有效地补偿校准数据中的任何误差。为了说明我们的发现，对1988年至2002年期间从六个地点采集的4028张数字化胶片-屏片乳腺造影片运行了当前的SMF实现版本（2.2beta版），分别使用和不使用已知的校准数据。结果表明，完全不使用任何校准数据运行的SMF实现所生成的结果，与使用完整校准数据集运行时的结果显示出很强的相关性，并且，最重要的是，与专家使用既定技术对乳房成分进行的视觉评估结果相关。SMF在与乳腺癌相关的大型流行病学研究中具有很大的应用前景，这些研究需要对多年前的大量胶片进行自动分析，而此时几乎没有或根本没有校准数据。
