Literature DB >> 17951346

Hematuria: portal venous phase multi detector row CT of the bladder--a prospective study.

Sung Bin Park1, Jeong Kon Kim, Hyun Joo Lee, Hyuck Jae Choi, Kyoung-Sik Cho.   

Abstract

PURPOSE: To prospectively determine the accuracy of portal venous phase helical multi-detector row computed tomography (CT) for bladder lesion evaluation in patients with hematuria by using cystoscopy as the reference standard.
MATERIALS AND METHODS: The study was approved by the institutional review board for human investigation, and informed consent was obtained from all patients. This study included 118 patients (91 male, 27 female; age range, 15-87 years; mean age +/- standard deviation, 62 years +/- 14) who underwent portal venous phase multi-detector row CT (scanning delay, 70 seconds; section thickness, 2 mm) and conventional cystoscopy because of painless gross hematuria or recurrent microscopic hematuria. Two reviewers with different experience levels independently evaluated the bladder for lesions at CT in a prospective fashion. The kappa statistic was used to determine the per lesion and per patient agreement between the two reviewers and between the CT and cystoscopic findings. The sensitivity and specificity of multi-detector row CT for bladder lesion detection were analyzed for numbers of lesions and for numbers of patients.
RESULTS: Multi-detector row CT showed excellent per lesion (kappa = 0.839) and per patient (kappa = 0.881) agreement between the two reviewers. Respective per lesion and per patient agreement between the CT and cystoscopic findings was also excellent in the first (kappa = 0.866 and kappa = 0.881) and second (kappa = 0.802 and kappa = 0.863) reviewers. The sensitivity and specificity of multi-detector row CT were 89%-92% and 88%-97%, respectively, in the per lesion analysis and 95% and 91%-93%, respectively, in the per patient analysis for both reviewers. All statistical parameters of diagnostic accuracy were similar between the two reviewers (P > .05).
CONCLUSION: Portal venous phase multi-detector row CT can provide high accuracy and reader agreement for bladder lesion detection in patients with painless gross hematuria and recurrent microscopic hematuria; these results indicate that multi-detector row CT can be used as the initial bladder examination in such patients. (c) RSNA, 2007.

Entities:  

Mesh:

Year:  2007        PMID: 17951346     DOI: 10.1148/radiol.2452061060

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  12 in total

1.  Urinary bladder segmentation in CT urography (CTU) using CLASS.

Authors:  Lubomir Hadjiiski; Heang-Ping Chan; Richard H Cohan; Elaine M Caoili; Yuen Law; Kenny Cha; Chuan Zhou; Jun Wei
Journal:  Med Phys       Date:  2013-11       Impact factor: 4.071

2.  Comparison of post contrast CT urography phases in bladder cancer detection.

Authors:  Malin Helenius; Par Dahlman; Maria Lonnemark; Einar Brekkan; Lisa Wernroth; Anders Magnusson
Journal:  Eur Radiol       Date:  2015-05-24       Impact factor: 5.315

3.  Detection of urinary bladder mass in CT urography with SPAN.

Authors:  Kenny Cha; Lubomir Hadjiiski; Heang-Ping Chan; Richard H Cohan; Elaine M Caoili; Chuan Zhou
Journal:  Med Phys       Date:  2015-07       Impact factor: 4.071

4.  U-Net based deep learning bladder segmentation in CT urography.

Authors:  Xiangyuan Ma; Lubomir M Hadjiiski; Jun Wei; Heang-Ping Chan; Kenny H Cha; Richard H Cohan; Elaine M Caoili; Ravi Samala; Chuan Zhou; Yao Lu
Journal:  Med Phys       Date:  2019-02-28       Impact factor: 4.071

5.  Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets.

Authors:  Kenny H Cha; Lubomir Hadjiiski; Ravi K Samala; Heang-Ping Chan; Elaine M Caoili; Richard H Cohan
Journal:  Med Phys       Date:  2016-04       Impact factor: 4.071

6.  Accuracy of contrast-enhanced ultrasound in the detection of bladder cancer.

Authors:  C Nicolau; L Bunesch; L Peri; R Salvador; J M Corral; C Mallofre; C Sebastia
Journal:  Br J Radiol       Date:  2010-12-01       Impact factor: 3.039

7.  Deep-learning convolutional neural network: Inner and outer bladder wall segmentation in CT urography.

Authors:  Marshall N Gordon; Lubomir M Hadjiiski; Kenny H Cha; Ravi K Samala; Heang-Ping Chan; Richard H Cohan; Elaine M Caoili
Journal:  Med Phys       Date:  2019-01-04       Impact factor: 4.071

8.  CT urography: segmentation of urinary bladder using CLASS with local contour refinement.

Authors:  Kenny Cha; Lubomir Hadjiiski; Heang-Ping Chan; Elaine M Caoili; Richard H Cohan; Chuan Zhou
Journal:  Phys Med Biol       Date:  2014-05-07       Impact factor: 3.609

9.  Auto-initialized cascaded level set (AI-CALS) segmentation of bladder lesions on multidetector row CT urography.

Authors:  Lubomir Hadjiiski; Heang-Ping Chan; Elaine M Caoili; Richard H Cohan; Jun Wei; Chuan Zhou
Journal:  Acad Radiol       Date:  2012-10-22       Impact factor: 3.173

Review 10.  Urinary bladder: normal appearance and mimics of malignancy at CT urography.

Authors:  Atul B Shinagare; Cheryl A Sadow; V Anik Sahni; Stuart G Silverman
Journal:  Cancer Imaging       Date:  2011-06-28       Impact factor: 3.909

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