D Beyersdorff1, L Lüdemann, E Dietz, D Galler, P Marchot, T Franiel.
Abstract
PURPOSE: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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).
CONCLUSION: 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. © Georg Thieme Verlag KG Stuttgart · New York.
PURPOSE: 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.
MATERIALS AND METHODS: 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.
RESULTS: 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).
CONCLUSION: 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. © Georg Thieme Verlag KG Stuttgart · New York.
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Year: 2011
PMID: 21442558 DOI: 10.1055/s-0029-1246051
Source DB: PubMed Journal: Rofo ISSN: 1438-9010