Department of Biomedical Engineering, Hashemite University, Zarqa, Jordan.
J Med Syst. 2011 Aug;35(4):463-71. doi: 10.1007/s10916-009-9382-6. Epub 2009 Oct 16.
In brain cancer, a biopsy as an invasive procedure is needed in order to differentiate between malignant and benign brain tumor. However, in some cases, it is difficult or harmful to perform such a procedure, to the brain. The aim of this study is to investigate a new method in maximizing the probability of brain cancer type detection without actual biopsy procedure. The proposed method combines both image and statistical analysis for tumor type detection. It employed image filtration and segmentation of the target region of interest with MRI to assure an accurate statistical interpretation of the results. Statistical analysis was based on utilizing the mean, range, box plot, and testing of hypothesis techniques to reach acceptable and accurate results in differentiating between those two types. This method was performed, examined and compared on actual patients with brain tumors. The results showed that the proposed method was quite successful in distinguishing between malignant and benign brain tumor with 95% confident that the results are correct based on statistical testing of hypothesis.
在脑癌中，需要进行活检这一有创操作，以便区分恶性和良性脑肿瘤。然而，在某些情况下，对大脑进行这种操作是困难或有害的。本研究旨在探索一种新方法，即在不进行实际活检的情况下，最大限度地提高脑癌类型检测的概率。该方法结合了图像和统计分析来进行肿瘤类型检测。它采用了 MRI 对目标感兴趣区域的图像滤波和分割，以确保对结果进行准确的统计解释。统计分析基于利用均值、范围、箱线图和假设检验技术，以达到区分这两种类型的可接受和准确的结果。该方法在实际脑肿瘤患者中进行了检查和比较。结果表明，该方法在区分恶性和良性脑肿瘤方面非常成功，基于假设检验的统计测试，有 95%的置信度认为结果是正确的。
