Beyersdorff D, Lüdemann L, Dietz E, Galler D, Marchot P, Franiel T
Department of Radiology, Charité, Universitätsmedizin Berlin, Campus Mitte.
Rofo. 2011 May;183(5):456-61. doi: 10.1055/s-0029-1246051. Epub 2011 Mar 25.
To evaluate the usefulness of a commercially available post-processing software tool for detecting prostate cancer on dynamic contrast-enhanced magnetic resonance imaging (MRI) and to compare the results to those obtained with a custom-made post-processing algorithm already tested under clinical conditions.
Forty-eight patients with proven prostate cancer were examined by standard MRI supplemented by dynamic contrast-enhanced dual susceptibility contrast (DCE-DSC) MRI prior to prostatectomy. A custom-made post-processing algorithm was used to analyze the MRI data sets and the results were compared to those obtained using a post-processing algorithm from In vivo Corporation (Dyna CAD for Prostate) applied to dynamic T 1-weighted images. Histology was used as the gold standard.
The sensitivity for prostate cancer detection was 78 % for the custom-made algorithm and 60 % for the commercial algorithm and the specificity was 79 % and 82 %, respectively. The accuracy was 79 % for our algorithm and 77.5 % for the commercial software tool. The chi-square test (McNemar-Bowker test) yielded no significant differences between the two tools (p = 0.06).
The two investigated post-processing algorithms did not differ in terms of prostate cancer detection. The commercially available software tool allows reliable and fast analysis of dynamic contrast-enhanced MRI for the detection of prostate cancer.
评估一种市售后处理软件工具在动态对比增强磁共振成像（MRI）上检测前列腺癌的有效性，并将结果与已在临床条件下测试过的定制后处理算法所得结果进行比较。
48例经证实患有前列腺癌的患者在前列腺切除术前接受了标准MRI检查，并辅以动态对比增强双敏感对比（DCE-DSC）MRI。使用定制的后处理算法分析MRI数据集，并将结果与应用于动态T1加权图像的In vivo公司的后处理算法（前列腺动态CAD）所得结果进行比较。组织学用作金标准。
定制算法检测前列腺癌的敏感性为78%，商业算法为60%，特异性分别为79%和82%。我们的算法准确率为79%，商业软件工具为
