Research Center for Bioengineering in the Service of Humanity and Society, School of Computer Science and Engineering, Hebrew University of Jerusalem, Israel.
Physiol Meas. 2011 Sep;32(9):1373-87. doi: 10.1088/0967-3334/32/9/002. Epub 2011 Jul 20.
Biopsies are currently the 'gold standard' method for identifying cancer of the prostate. While biopsies yield very accurate information regarding the area they sample, they are performed at discrete points and provide no information on the adjacent tissue. To enhance procedural accuracy, biopsies of a large number of sites are routinely carried out. Although more accurate, this method is both more complex and nevertheless remains discrete. In this paper, we evaluate the advantages of using bio-impedance information as the input for a support vector machines (SVMs) classifier to overcome these limitations. In this method, the biopsy probes are used as electrodes to obtain electrical impedance data during each biopsy sample. Using a computer model of the prostate, a SVM was trained and tested. Different tumor shapes and conductivity values, and the classifier's ability to generalize to these different properties, were examined. We demonstrate that by using this classifier the number of biopsies can be reduced and valuable information concerning the adjacent tissue which was not biopsied can be generated.
活检目前是识别前列腺癌的“金标准”方法。虽然活检可以提供关于其采样区域的非常准确的信息，但它们是在离散的点上进行的，并且无法提供相邻组织的信息。为了提高程序的准确性，通常会对大量的部位进行活检。尽管这种方法更准确，但它既更复杂，而且仍然是离散的。在本文中，我们评估了将生物阻抗信息用作支持向量机 (SVM) 分类器的输入的优势，以克服这些限制。在这种方法中，活检探针被用作电极，在每次活检样本中获取电阻抗数据。使用前列腺的计算机模型，训练和测试了一个 SVM。检查了不同的肿瘤形状和电导率值，以及分类器对这些不同特性的概括能力。我们证明，通过使用这个分类器，可以减少活检的次数，并生成有关未进行活检的相邻组织的有价值的信息。
