Literature DB >> 19375739

Renal stone detection using unenhanced multidetector row computerized tomography--does section width matter?

Daniel H Jin1, Gregory R Lamberton, Dale R Broome, Hans Saaty, Shravani Bhattacharya, Tekisha U Lindler, D Duane Baldwin.   

Abstract

PURPOSE: We determined the effect of reconstructed section width on sensitivity and specificity for detecting renal calculi using multidetector row computerized tomography.
MATERIALS AND METHODS: Three to 5 renal stones 2 to 4 mm in size were randomly placed into 14 human cadaveric kidneys and scanned by 16-row detector computerized tomography at 1.25 mm collimation and identical scanning parameters. After acquisition images were reconstructed with a section width of 1.25, 2.5, 3.75 and 5.0 mm, and reviewed independently by 2 blinded radiologists. Comparisons of sensitivity and specificity between different section widths were assessed with the McNemar test and Cochran's Q statistics.
RESULTS: Specificity was not significantly affected by section width (94.6% to 97.7%). In contrast, sensitivity increased as stone size increased and as section width decreased. Sensitivity to detect all stones was 80.7%, 80.7%, 87.7% and 92.1% for 5.0, 3.75, 2.5 and 1.25 mm section widths, respectively. Interobserver agreement for stone detection was excellent (kappa 0.858). Although the 2.0 mm stone detection rate improved with thinner section widths (79.4% vs 52.9% for 1.25 vs 5.0 mm, p = 0.004), stones greater than 2.0 mm were similarly detected at different slice selections (p = 0.056 to 0.572).
CONCLUSIONS: Independent of other scanning parameters reconstruction section width influences the ability to detect small renal calculi. It must be considered when creating computerized tomography protocols.

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Year:  2009        PMID: 19375739     DOI: 10.1016/j.juro.2009.01.092

Source DB:  PubMed          Journal:  J Urol        ISSN: 0022-5347            Impact factor:   7.450


  4 in total

1.  Detection of urinary tract calculi on CT images reconstructed with deep learning algorithms.

Authors:  Samjhana Thapaliya; Samuel L Brady; Elanchezhian Somasundaram; Christopher G Anton; Brian D Coley; Alexander J Towbin; Bin Zhang; Jonathan R Dillman; Andrew T Trout
Journal:  Abdom Radiol (NY)       Date:  2021-10-04

2.  Association of 42 SNPs with genetic risk for cervical cancer: an extensive meta-analysis.

Authors:  Shaoshuai Wang; Haiying Sun; Yao Jia; Fangxu Tang; Hang Zhou; Xiong Li; Jin Zhou; Kecheng Huang; Qinghua Zhang; Ting Hu; Ru Yang; Changyu Wang; Ling Xi; Dongrui Deng; Hui Wang; Shixuan Wang; Ding Ma; Shuang Li
Journal:  BMC Med Genet       Date:  2015-04-15       Impact factor: 2.103

Review 3.  Meta-Analysis of Polymorphic Variants Conferring Genetic Risk to Cervical Cancer in Indian Women Supports CYP1A1zzm321990as an Important Associated Locus

Authors:  Debmalya Sengupta; Udayan Guha; Sagnik Mitra; Sampurna Ghosh; Samsiddhi Bhattacharjee; Mainak Sengupta
Journal:  Asian Pac J Cancer Prev       Date:  2018-08-24

4.  A comprehensive meta-analysis and a case-control study give insights into genetic susceptibility of lung cancer and subgroups.

Authors:  Debmalya Sengupta; Souradeep Banerjee; Pramiti Mukhopadhyay; Ritabrata Mitra; Tamohan Chaudhuri; Abhijit Sarkar; Gautam Bhattacharjee; Somsubhra Nath; Susanta Roychoudhury; Samsiddhi Bhattacharjee; Mainak Sengupta
Journal:  Sci Rep       Date:  2021-07-16       Impact factor: 4.379

  4 in total

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