Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI
Computerized analysis of prostate lesions in the peripheral zone using dynamic contrast enhanced MRI
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DOI：
10.1118/1.2836419
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发表时间：
2008-03-01
期刊：
影响因子：
3.8
通讯作者：
Huisman, Henkjan J.
中科院分区：
文献类型：
--
作者：
Vos, Pieter C.;Hambrock, Thomas;Huisman, Henkjan J.
一种新的自动化的计算机化的计划已经开发用于确定恶性肿瘤的可能性措施的前列腺癌症可疑区域的基础上动态对比度增强磁共振成像（MRI）（DCE-MRI）图像。我们的数据库包括连续34例经组织学证实的前列腺外周带腺癌患者。由放射科医师和研究人员使用整体切片组织病理学作为参考标准，在MR图像上一致注释癌和非恶性组织。注释被用作感兴趣区域（ROI）。从ROI中提取包括药代动力学参数和T1估计的特征集，以训练支持向量机作为分类器。分类器的输出被用作恶性肿瘤可能性的量度。使用ROC曲线下面积评价该方案的诊断性能。鉴别前列腺癌与外周区非恶性疾病的诊断准确率为0.83（0.75-0.92）。这表明，它是可行的，以开发一个计算机辅助诊断系统，能够表征前列腺癌的周边区的基础上DCE-MRI。(c)2008年美国医学物理学家协会。
A novel automated computerized scheme has been developed for determining a likelihood measure of malignancy for cancer suspicious regions in the prostate based on dynamic contrast-enhanced magnetic resonance imaging (MRI) (DCE-MRI) images. Our database consisted of 34 consecutive patients with histologically proven adenocarcinoma in the peripheral zone of the prostate. Both carcinoma and non-malignant tissue were annotated in consensus on MR images by a radiologist and a researcher using whole mount step-section histopathology as standard of reference. The annotations were used as regions of interest (ROIs). A feature set comprising pharmacokinetic parameters and a T1 estimate was extracted from the ROIs to train a support vector machine as classifier. The output of the classifier was used as a measure of likelihood of malignancy. Diagnostic performance of the scheme was evaluated using the area under the ROC curve. The diagnostic accuracy obtained for differentiating prostate cancer from non-malignant disorders in the peripheral zone was 0.83 (0.75-0.92). This suggests that it is feasible to develop a computer aided diagnosis system capable of characterizing prostate cancer in the peripheral zone based on DCE-MRI. (c) 2008 American Association of Physicists in Medicine.