Literature DB >> 28980887

Accuracy of Presurgical Functional MR Imaging for Language Mapping of Brain Tumors: A Systematic Review and Meta-Analysis.

Hsu-Huei Weng1, Kyle R Noll1, Jason M Johnson1, Sujit S Prabhu1, Yuan-Hsiung Tsai1, Sheng-Wei Chang1, Yen-Chu Huang1, Jiann-Der Lee1, Jen-Tsung Yang1, Cheng-Ta Yang1, Ying-Huang Tsai1, Chun-Yuh Yang1, John D Hazle1, Donald F Schomer1, Ho-Ling Liu1.   

Abstract

Purpose To compare functional magnetic resonance (MR) imaging for language mapping (hereafter, language functional MR imaging) with direct cortical stimulation (DCS) in patients with brain tumors and to assess factors associated with its accuracy. Materials and Methods PubMed/MEDLINE and related databases were searched for research articles published between January 2000 and September 2016. Findings were pooled by using bivariate random-effects and hierarchic summary receiver operating characteristic curve models. Meta-regression and subgroup analyses were performed to evaluate whether publication year, functional MR imaging paradigm, magnetic field strength, statistical threshold, and analysis software affected classification accuracy. Results Ten articles with a total of 214 patients were included in the analysis. On a per-patient basis, the pooled sensitivity and specificity of functional MR imaging was 44% (95% confidence interval [CI]: 14%, 78%) and 80% (95% CI: 54%, 93%), respectively. On a per-tag basis (ie, each DCS stimulation site or "tag" was considered a separate data point across all patients), the pooled sensitivity and specificity were 67% (95% CI: 51%, 80%) and 55% (95% CI: 25%, 82%), respectively. The per-tag analysis showed significantly higher sensitivity for studies with shorter functional MR imaging session times (P = .03) and relaxed statistical threshold (P = .05). Significantly higher specificity was found when expressive language task (P = .02), longer functional MR imaging session times (P < .01), visual presentation of stimuli (P = .04), and stringent statistical threshold (P = .01) were used. Conclusion Results of this study showed moderate accuracy of language functional MR imaging when compared with intraoperative DCS, and the included studies displayed significant methodologic heterogeneity. © RSNA, 2017 Online supplemental material is available for this article.

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Year:  2017        PMID: 28980887     DOI: 10.1148/radiol.2017162971

Source DB:  PubMed          Journal:  Radiology        ISSN: 0033-8419            Impact factor:   11.105


  11 in total

1.  Accuracy analysis of fMRI and MEG activations determined by intraoperative mapping.

Authors:  David G Ellis; Matthew L White; Satoru Hayasaka; David E Warren; Tony W Wilson; Michele R Aizenberg
Journal:  Neurosurg Focus       Date:  2020-02-01       Impact factor: 4.047

Review 2.  Harnessing networks and machine learning in neuropsychiatric care.

Authors:  Eli J Cornblath; David M Lydon-Staley; Danielle S Bassett
Journal:  Curr Opin Neurobiol       Date:  2019-01-12       Impact factor: 6.627

3.  Multivariate machine learning-based language mapping in glioma patients based on lesion topography.

Authors:  Nan Zhang; Binke Yuan; Jing Yan; Jingliang Cheng; Junfeng Lu; Jinsong Wu
Journal:  Brain Imaging Behav       Date:  2021-02-22       Impact factor: 3.978

4.  IClinfMRI Software for Integrating Functional MRI Techniques in Presurgical Mapping and Clinical Studies.

Authors:  Ai-Ling Hsu; Ping Hou; Jason M Johnson; Changwei W Wu; Kyle R Noll; Sujit S Prabhu; Sherise D Ferguson; Vinodh A Kumar; Donald F Schomer; John D Hazle; Jyh-Horng Chen; Ho-Ling Liu
Journal:  Front Neuroinform       Date:  2018-03-09       Impact factor: 4.081

5.  Lower-Grade Gliomas: An Epidemiological Voxel-Based Analysis of Location and Proximity to Eloquent Regions.

Authors:  Tomás Gómez Vecchio; Alice Neimantaite; Alba Corell; Jiri Bartek; Margret Jensdottir; Ingerid Reinertsen; Ole Solheim; Asgeir S Jakola
Journal:  Front Oncol       Date:  2021-09-21       Impact factor: 6.244

6.  Longitudinal assessment of network reorganizations and language recovery in postoperative patients with glioma.

Authors:  Binke Yuan; Nan Zhang; Fangyuan Gong; Xindi Wang; Jing Yan; Junfeng Lu; Jinsong Wu
Journal:  Brain Commun       Date:  2022-04-06

7.  fMRI Retinotopic Mapping in Patients with Brain Tumors and Space-Occupying Brain Lesions in the Area of the Occipital Lobe.

Authors:  Katharina Hense; Tina Plank; Christina Wendl; Frank Dodoo-Schittko; Elisabeth Bumes; Mark W Greenlee; Nils Ole Schmidt; Martin Proescholdt; Katharina Rosengarth
Journal:  Cancers (Basel)       Date:  2021-05-18       Impact factor: 6.639

8.  Preoperative functional magnetic resonance imaging in patients undergoing surgery for tumors around left (dominant) inferior frontal gyrus region.

Authors:  V Gunal; Amey R Savardekar; B Indira Devi; Rose D Bharath
Journal:  Surg Neurol Int       Date:  2018-06-26

9.  Automatic identification of atypical clinical fMRI results.

Authors:  J Martijn Jansma; Geert-Jan Rutten; Lenny E Ramsey; T J Snijders; Alberto Bizzi; Katharina Rosengarth; Frank Dodoo-Schittko; Elke Hattingen; Mar Jiménez de la Peña; Gord von Campe; Margit Jehna; Nick F Ramsey
Journal:  Neuroradiology       Date:  2020-08-18       Impact factor: 2.804

Review 10.  Functional Mapping before and after Low-Grade Glioma Surgery: A New Way to Decipher Various Spatiotemporal Patterns of Individual Neuroplastic Potential in Brain Tumor Patients.

Authors:  Hugues Duffau
Journal:  Cancers (Basel)       Date:  2020-09-13       Impact factor: 6.639

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