Literature DB >> 28746939

Consistency between Targets Delineated by Angiography, Computed Tomography, and Magnetic Resonance Imaging in Stereotactic Radiosurgery for Arteriovenous Malformation.

Yu-Jie Huang1, Shih-Wei Hsu, Tsair-Fwu Lee, Jih-Tsun Ho, Wu-Fu Chen.   

Abstract

BACKGROUND: Target identification is important for radiosurgery for arteriovenous malformations (AVMs). Targets defined by different imaging modalities may be inconsistent in practice.
OBJECTIVES: The goal of this study is to review and analyze the consistency between targets defined by different imaging modalities in radiosurgery for AVMs.
METHODS: From March 2007 to June 2011, AVM patients for radiosurgery whose targets were delineated by angiography/computed tomography (CT)/magnetic resonance imaging (MRI) were reviewed. Spetzler-Martin grades, hemorrhage history, and treatment volumes were checked. Dice similarity coefficients (DSCs) between targets were calculated and analyzed.
RESULTS: Twenty-three patients were enrolled. The mean DSCs were between 0.37 and 0.51 for targets by different modalities. There was no significant difference in DSCs regarding Spetzler-Martin grades and hemorrhage history. For CT-delineated target volumes <3 cm3, MRI-delineated target volumes <5 cm3, and angiography-delineated target volumes <2 cm3, the DSCs between the different image modalities were significantly decreased.
CONCLUSIONS: Consistency between targets delineated using different image modalities was likely to be unsatisfactory and worsen significantly in niduses with volumes <5 cm3. An iterative multimodality approach to confirm the delineated targets of AVMs is suggested to be indispensable for robust treatment in radiosurgery.
© 2017 S. Karger AG, Basel.

Entities:  

Keywords:  Angiography, image registration; Arteriovenous malformations; Computed tomography; Magnetic resonance imaging; Stereotactic radiosurgery; Target delineation

Mesh:

Year:  2017        PMID: 28746939     DOI: 10.1159/000469667

Source DB:  PubMed          Journal:  Stereotact Funct Neurosurg        ISSN: 1011-6125            Impact factor:   1.875


  1 in total

1.  Automated segmentation of multiparametric magnetic resonance images for cerebral AVM radiosurgery planning: a deep learning approach.

Authors:  Aaron B Simon; Brian Hurt; Roshan Karunamuni; Gwe-Ya Kim; Vitali Moiseenko; Scott Olson; Nikdokht Farid; Albert Hsiao; Jona A Hattangadi-Gluth
Journal:  Sci Rep       Date:  2022-01-17       Impact factor: 4.379

  1 in total

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