Literature DB >> 7744142

Automated analysis and interpretation of transrectal ultrasonography images in patients with prostatitis.

J J de la Rosette1, R J Giesen, A L Huynen, R G Aarnink, M P van Iersel, F M Debruyne, H Wijkstra.   

Abstract

Transrectal ultrasound (TRUS) offers a valuable complement to digital rectal examination (DRE) in diagnosing prostate diseases. However, in case of prostatitis syndromes, contradictions are found with regard to characteristic ultrasound features in these patients. Therefore we sought for better imaging techniques. This paper describes a study on the automated analysis of ultrasonographic prostate images (AUDEX). With image processing, in the present study, tissue characterization is performed to predict the probability of the presence of inflammated prostate tissue. This technique already proved its validity in patients with prostate cancer. During prostate examinations, images were recorded with clear indication of the puncture position. Only patients with an unambiguously inflamed or noninflamed benign histologic result were included. Evaluation showed that a sensitivity of 90.6% and a specificity of 64.2% was reached. Finally, the prospective positive and negative predictive value for prostatitis was 50.0% and 94.6%, respectively. This means that AUDEX predicts the diagnosis 'prostatitis' in a large number of patients with no infection. In case of prostatitis, however, this prediction is almost always right.

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Year:  1995        PMID: 7744142     DOI: 10.1159/000475123

Source DB:  PubMed          Journal:  Eur Urol        ISSN: 0302-2838            Impact factor:   20.096


  4 in total

Review 1.  Interventions for chronic abacterial prostatitis.

Authors:  C McNaughton; R Mac Donald; T Wilt
Journal:  Cochrane Database Syst Rev       Date:  2001

2.  [Transrectal ultrasound of the prostate. Current status and prospects].

Authors:  M Zacharias; K V Jenderka; H Heynemann; P Fornara
Journal:  Urologe A       Date:  2002-11       Impact factor: 0.639

3.  A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images.

Authors:  Seokmin Han; Sung Il Hwang; Hak Jong Lee
Journal:  J Digit Imaging       Date:  2020-08       Impact factor: 4.056

4.  Computer-aided prostate cancer detection using texture features and clinical features in ultrasound image.

Authors:  Seok Min Han; Hak Jong Lee; Jin Young Choi
Journal:  J Digit Imaging       Date:  2008-03-06       Impact factor: 4.056

  4 in total

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