Literature DB >> 31493607

Texture Analysis of Standard Magnetic Resonance Images to Predict Response to Gamma Knife Radiosurgery in Vestibular Schwannomas.

Herwin Speckter1, Jairo Santana2, José Bido2, Giancarlo Hernandez2, Diones Rivera2, Luis Suazo2, Santiago Valenzuela2, Jairo Oviedo3, Cesar F Gonzalez4, Peter Stoeter3.   

Abstract

PURPOSE: To search for texture features of routine magnetic resonance imaging to predict tumor volume reduction and transient versus permanent tumor progression of vestibular schwannomas treated by Gamma Knife stereotactic radiosurgery.
MATERIALS AND METHODS: Included were 23 patients with vestibular schwannomas treated in our center and followed over a period of 23.7-80.3 months (mean 42.7). Magnetic resonance imaging was performed on a 3-Tesla scanner and included T1-weighted images with and without contrast enhancement, T2-weighted, and fluid-attenuated inversion recovery images. Volumetric results were followed longitudinally over time and correlated to texture features as mean, minimum, maximum, standard deviation, skewness, and kurtosis of normalized signals taken from regions of interest covering the total tumor volume.
RESULTS: In total, 14 tumors showed early progression during the first 5-18 months (2 cases permanent, 12 cases transient), whereas 9 tumors regressed immediately after SRS. Kurtosis of T2-weighted image intensity values turned out to predict progression best with a sensitivity and specificity of 71% and 78%. From all texture feature parameters, only the minimum of the normalized T2-weighted image intensity values correlated significantly to the final reduction of tumor volume per month (correlation coefficient = -0.634, P < 0.05, corrected for false discovery rate).
CONCLUSIONS: Texture feature analysis helps to predict permanent versus transient enlargement and final volume reduction of schwannomas after SRS. Thus, alternative treatment strategies might be considered, mainly in large tumors, where further clinical deterioration cannot be excluded. To confirm these results, a prospective study including more cases and a longer follow-up period is necessary.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Magnetic resonance imaging; Radiosurgery; Texture analysis; Vestibular schwannoma

Mesh:

Year:  2019        PMID: 31493607     DOI: 10.1016/j.wneu.2019.08.193

Source DB:  PubMed          Journal:  World Neurosurg        ISSN: 1878-8750            Impact factor:   2.104


  2 in total

Review 1.  Machine Learning for the Detection and Segmentation of Benign Tumors of the Central Nervous System: A Systematic Review.

Authors:  Paul Windisch; Carole Koechli; Susanne Rogers; Christina Schröder; Robert Förster; Daniel R Zwahlen; Stephan Bodis
Journal:  Cancers (Basel)       Date:  2022-05-27       Impact factor: 6.575

2.  Applying artificial intelligence to longitudinal imaging analysis of vestibular schwannoma following radiosurgery.

Authors:  Cheng-Chia Lee; Wei-Kai Lee; Chih-Chun Wu; Chia-Feng Lu; Huai-Che Yang; Yu-Wei Chen; Wen-Yuh Chung; Yong-Sin Hu; Hsiu-Mei Wu; Yu-Te Wu; Wan-Yuo Guo
Journal:  Sci Rep       Date:  2021-02-04       Impact factor: 4.379

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.