Literature DB >> 15855107

Determination of splenomegaly by CT: is there a place for a single measurement?

Alexandre S Bezerra1, Giuseppe D'Ippolito, Salomão Faintuch, Jacob Szejnfeld, Muneeb Ahmed.   

Abstract

OBJECTIVE: Our objective was to determine if there is a single parameter that can be used as a marker of splenomegaly using CT.
MATERIALS AND METHODS: Splenic length, width, and thickness were measured in 249 CT scans and multidimensional indexes were obtained from the multiplication of these measurements. Volume was calculated by summing the volumes of multiple contiguous scans. The relationship of the spleen to the left liver lobe and inferior third of the left kidney was also evaluated. Linear equations were obtained to correlate each measurement to the splenic volume.
RESULTS: The unidimensional measurements with best correlation to volume were splenic length (r = 0.81, p < 0.01) and width (r = 0.804, p < 0.01). Correlation was better for the multidimensional indexes (r = 0.95, p < 0.01). Using a previously described upper limit of normality for splenic volume of 314.5 cm3 in the linear regression equation obtained, a maximum spleen length of 9.76 cm was the upper limit of normality. The relation of the lowest point of the spleen to the inferior third of the kidney also showed that if the spleen reached or extended below this portion of the kidney, it could be used as evidence of splenomegaly (p < 0.005), although it had a low sensitivity.
CONCLUSION: Splenic length and multidimensional indexes correlate well with splenic CT volume. A splenic length of 9.76 cm can be used to accurately diagnose splenomegaly and can replace multiple-measurement, time-consuming methods in the clinical routine.

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Year:  2005        PMID: 15855107     DOI: 10.2214/ajr.184.5.01841510

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  51 in total

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4.  Splenomegaly Segmentation using Global Convolutional Kernels and Conditional Generative Adversarial Networks.

Authors:  Yuankai Huo; Zhoubing Xu; Shunxing Bao; Camilo Bermudez; Andrew J Plassard; Jiaqi Liu; Yuang Yao; Albert Assad; Richard G Abramson; Bennett A Landman
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2018-03

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8.  Multi-Atlas Spleen Segmentation on CT Using Adaptive Context Learning.

Authors:  Jiaqi Liu; Yuankai Huo; Zhoubing Xu; Albert Assad; Richard G Abramson; Bennett A Landman
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9.  Liver Stiffness Assessed by Shear Wave Elastography Predicts Postoperative Liver Failure in Patients with Hepatocellular Carcinoma.

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10.  Assessing splenomegaly: automated volumetric analysis of the spleen.

Authors:  Marius George Linguraru; Jesse K Sandberg; Elizabeth C Jones; Ronald M Summers
Journal:  Acad Radiol       Date:  2013-03-25       Impact factor: 3.173

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