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- W2079461185 abstract "To retrospectively determine the accuracy of endorectal magnetic resonance (MR) imaging in demonstrating seminal vesicle invasion (SVI) and to investigate the MR imaging features that can predict SVI.The Institutional Review Board granted exempt status for this retrospective study, with waiver of informed consent; patient data were collected and handled in accordance with HIPAA regulations. Fifty-one men (age range, 44-73 years) with SVI and 303 men (age range, 40-76 years) without SVI who underwent endorectal MR imaging before radical prostatectomy between January 2000 and October 2004 were included in the study. Endorectal MR images were retrospectively and independently analyzed by two radiologists for SVI, tumor at prostate base, extracapsular extension, and other features considered indicative of SVI. Areas under the receiver operating characteristic curves (AUCs) were used to assess the accuracy of detecting SVI at endorectal MR imaging. A multiple logistic regression was used to explore the combinations of MR imaging features that might facilitate the detection of SVI.Readers 1 and 2 had an AUC of 0.93 and 0.81, respectively, for the detection of SVI. For both readers, the features that had the highest sensitivity and specificity were low signal intensity within the seminal vesicle and lack of preservation of seminal vesicle architecture. At multiple regression analysis, tumor at the prostate base that extended beyond the capsule and low signal intensity within a seminal vesicle that has lost its normal architecture were highly predictive of SVI.Endorectal MR imaging is accurate in demonstrating SVI prior to radical prostatectomy, and recognition of the most predictive features may facilitate the use of this modality." @default.
- W2079461185 created "2016-06-24" @default.
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- W2079461185 date "2006-03-01" @default.
- W2079461185 modified "2023-09-27" @default.
- W2079461185 title "Endorectal MR Imaging in the Evaluation of Seminal Vesicle Invasion: Diagnostic Accuracy and Multivariate Feature Analysis" @default.
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- W2079461185 doi "https://doi.org/10.1148/radiol.2383050657" @default.
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