Department of Neurosurgery, School of Medicine, Rize University, Rize, Turkey.
J Clin Neurosci. 2010 Dec;17(12):1499-502. doi: 10.1016/j.jocn.2010.03.044. Epub 2010 Aug 30.
In this study, we aimed to describe the application of the Cavalieri principle for the assessment of tumor volume using MRI without an over-projection/estimation effect. For this purpose, the volume of a patient's brain and the brain tumor volume, or the volume of the former tumor region, were estimated preoperatively and postoperatively using a combination of the Cavalieri principle and MRI. The previously described formula was modified for MRI measurements to eliminate the over-estimation effects of imaging. The total brain and tumor volumes estimated using the MRI of a representative patient with glioblastoma multiforme were: preoperative, 1562.46 cm³ and 81.59 cm³, respectively; and postoperative, 1571.72 cm³ and 86.92 cm³, respectively. The mean time to count points for an estimation of brain and tumor volume (or the volume of the former tumor region) were 14 minutes and 3 minutes, respectively. The coefficients of the errors of the estimates for brain and tumor volume (former tumor volume, postoperative) measurements were: preoperative 0.01 and 0.02; and postoperative 0.01 and 0.03, respectively. Our results show that the combination of MRI and the Cavalieri principle can provide an unbiased, direct and assumption-free estimate of the regions of interest. Therefore, the presented method could be applied efficiently without any need for special software, additional equipment or personnel other than that required for routine MRI in daily use.
在这项研究中，我们旨在描述在不产生过度投影/估计效应的情况下，使用 MRI 应用 Cavalieri 原理评估肿瘤体积的方法。为此，我们在术前和术后使用 Cavalieri 原理和 MRI 组合来分别评估患者大脑和脑肿瘤体积，或先前肿瘤区域的体积。我们对先前描述的公式进行了修改，以用于 MRI 测量，以消除成像的过度估计效应。一位多形性胶质母细胞瘤患者的 MRI 测量的总脑和肿瘤体积分别为：术前分别为 1562.46cm³和 81.59cm³；术后分别为 1571.72cm³和 86.92cm³。估计脑和肿瘤体积（或先前肿瘤区域的体积）所需的点数的平均时间分别为 14 分钟和 3 分钟。脑和肿瘤体积（术后肿瘤体积）测量的估计误差系数分别为术前 0.01 和 0.02；以及术后 0.01 和 0.03。我们的结果表明，MRI 和 Cavalieri 原理的组合可以提供无偏、直接和无假设的感兴趣区域的估计。因此，该方法可以在不需要任何特殊软件、额外设备或人员的情况下高效应用，而这些是在日常使用中进行常规 MRI 所必需的。
