Literature DB >> 22588267

Planimetric hematoma measurement in patients with intraventricular hemorrhage: is total volume a preferred target for reliable analysis?

Dar Dowlatshahi1, Jayme C Kosior, Sherif Idris, Muneer Eesa, Peter Dickhoff, Manish Joshi, Suresh Subramaniam, Sarah Tymchuk, Michael D Hill, Richard I Aviv, Richard Frayne, Andrew M Demchuk.   

Abstract

BACKGROUND AND
PURPOSE: Reliable quantification of both intracerebral hemorrhage and intraventricular hemorrhage (IVH) volume is important for hemostatic trials. We evaluated the reliability of computer-assisted planimetric volume measurements of IVH.
METHODS: Computer-assisted planimetry was used to quantify IVH volume. Five raters measured IVH volumes, total (intracerebral hemorrhage+IVH) volumes, and Graeb scores from 20 randomly selected computed tomography scans twice. Estimates of interrater and intrarater reliability were calculated and expressed as an intrarater correlation coefficient and an absolute minimum detectable difference.
RESULTS: Planimetric IVH volume analysis had excellent intra- and interrater agreement (intrarater correlation coefficient, 0.96 and 0.92, respectively), which was superior to the Graeb score (intrarater correlation coefficient, 0.88 and 0.83). Minimum detectable differences for intra- and interrater volumes were 12.1 mL and 17.3 mL, and were dependent on the total size of the hematoma; hematomas smaller than the median 43.8 mL had lower minimum detectable differences, whereas those larger than the median had higher minimum detectable differences. Planimetric total hemorrhage volume analysis had the best intra- and interrater agreement (intrarater correlation coefficient, 0.99 and 0.97, respectively).
CONCLUSIONS: Computer-assisted planimetric techniques provide a reliable measurement of ventricular hematoma volume, but are susceptible to higher absolute error when assessing larger hematomas.

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Year:  2012        PMID: 22588267     DOI: 10.1161/STROKEAHA.112.654970

Source DB:  PubMed          Journal:  Stroke        ISSN: 0039-2499            Impact factor:   7.914


  10 in total

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Authors:  Rajat Dhar; Guido J Falcone; Yasheng Chen; Ali Hamzehloo; Elayna P Kirsch; Rommell B Noche; Kilian Roth; Julian Acosta; Andres Ruiz; Chia-Ling Phuah; Daniel Woo; Thomas M Gill; Kevin N Sheth; Jin-Moo Lee
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Journal:  Stroke       Date:  2016-03-29       Impact factor: 7.914

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Authors:  Jay F Yu; Andrew D Nicholson; Jeffrey Nelson; Matthew D Alexander; Stephanie H Tse; Steven W Hetts; J Claude Hemphill; Helen Kim; Daniel L Cooke
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7.  New and expanding ventricular hemorrhage predicts poor outcome in acute intracerebral hemorrhage.

Authors:  Vignan Yogendrakumar; Tim Ramsay; Dean Fergusson; Andrew M Demchuk; Richard I Aviv; David Rodriguez-Luna; Carlos A Molina; Yolanda Silva; Imanuel Dzialowski; Adam Kobayashi; Jean-Martin Boulanger; Cheemun Lum; Gord Gubitz; Padma Srivastava; Jayanta Roy; Carlos S Kase; Rohit Bhatia; Michael D Hill; Andrew D Warren; Christopher D Anderson; Mahmut E Gurol; Steve M Greenberg; Anand Viswanathan; Jonathan Rosand; Joshua N Goldstein; Dar Dowlatshahi
Journal:  Neurology       Date:  2019-08-01       Impact factor: 9.910

8.  3D Deep Neural Network Segmentation of Intracerebral Hemorrhage: Development and Validation for Clinical Trials.

Authors:  Matthew F Sharrock; W Andrew Mould; Hasan Ali; Meghan Hildreth; Issam A Awad; Daniel F Hanley; John Muschelli
Journal:  Neuroinformatics       Date:  2020-09-27

9.  Dynamic characterization of the CT angiographic 'spot sign'.

Authors:  Santanu Chakraborty; Mohammed Alhazzaa; Jason K Wasserman; Yang Yang Sun; Grant Stotts; Mathew J Hogan; Andrew Demchuk; Richard I Aviv; Dar Dowlatshahi
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10.  Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement.

Authors:  Tao Wang; Na Song; Lingling Liu; Zichao Zhu; Bing Chen; Wenjun Yang; Zhiqiang Chen
Journal:  BMC Med Imaging       Date:  2021-08-13       Impact factor: 1.930

  10 in total

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