Preoperative MR imaging in the evaluation of seminal vesicle invasion in prostate cancer: Pattern analysis of seminal vesicle lesions

Purpose To develop an image‐based classification system for seminal vesicle lesions (SVL) to detect seminal vesicle invasion (SVI) in prostate cancer and to evaluate whether pattern analysis of SVL with MR imaging could improve the accuracy of evaluating SVI. Materials and Methods The MR images of 2...

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Bibliographic Details
Published in:Journal of magnetic resonance imaging 2008-07, Vol.28 (1), p.144-150
Main Authors: Jung, Dae Chul, Lee, Hak Jong, Kim, Seung Hyup, Choe, Ghee Young, Lee, Sang Eun
Format: Article
Language:English
Subjects:
Adult
Aged
Humans
Magnetic Resonance Imaging
Male
Middle Aged
MR imaging
Neoplasm Invasiveness
prostate cancer
Prostatectomy
Prostatic Neoplasms - pathology
Retrospective Studies
seminal vesicle invasion
Seminal Vesicles - pathology
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Summary:Purpose To develop an image‐based classification system for seminal vesicle lesions (SVL) to detect seminal vesicle invasion (SVI) in prostate cancer and to evaluate whether pattern analysis of SVL with MR imaging could improve the accuracy of evaluating SVI. Materials and Methods The MR images of 217 patients who had undergone retropubic radical prostatectomy (RRP) due to prostate cancer were retrospectively analyzed by two uroradiologists, focusing on SVL. The SVL on T2‐weighted sequences was classified as five classes. In each class the results were correlated with histopathologic findings. Results Fourteen (6.5%) of 217 patients had evidence of SVI at histopathologic evaluation after RRP. Pattern analysis of SVL showed 71.4% sensitivity, 96.6% specificity for predicting SVI. ROC curves for the subjective scoring of SVI showed that reader 1 had an area under the curve (AUC) of 0.69 and reader 2 had an AUC of 0.81. The overall accuracy of pattern analysis was superior to both serum prostate‐specific antigen (PSA) level and subjective scoring (P < 0.01, McNemar test). Conclusion The classification system of SVL on the basis of their imaged morphologic features can provide an objective standard and simplify the abnormal findings to help predict SVI in prostate cancer. Pattern analysis of SVL with MR imaging improves the accuracy of detecting SVI. J. Magn. Reson. Imaging 2008. © 2008 Wiley‐Liss, Inc.
ISSN:1053-1807
1522-2586
DOI:10.1002/jmri.21422