Literature DB >> 30977059

Evaluation of pediatric glioma outcomes using intraoperative MRI: a multicenter cohort study.

Michael Karsy1, S Hassan Akbari2, David Limbrick2, Eric C Leuthardt2, John Evans2, Matthew D Smyth2, Jennifer Strahle2, Jeffrey Leonard3, Samuel Cheshier1, Douglas L Brockmeyer1, Robert J Bollo1, John R Kestle1, John Honeycutt4, David J Donahue4, Richard A Roberts4, Daniel R Hansen4, Jay Riva-Cambrin5, Garnette Sutherland5, Clair Gallagher5, Walter Hader5, Yves Starreveld5, Mark Hamilton5, Ann-Christine Duhaime6, Randy L Jensen7,8, Michael R Chicoine2.   

Abstract

BACKGROUND: The use of intraoperative MRI (iMRI) during treatment of gliomas may increase extent of resection (EOR), decrease need for early reoperation, and increase progression-free and overall survival, but has not been fully validated, particularly in the pediatric population.
OBJECTIVE: To assess the accuracy of iMRI to identify residual tumor in pediatric patients with glioma and determine the effect of iMRI on decisions for resection, complication rates, and other outcomes.
METHODS: We retrospectively analyzed a multicenter database of pediatric patients (age ≤ 18 years) who underwent resection of pathologically confirmed gliomas.
RESULTS: We identified 314 patients (mean age 9.7 ± 4.6 years) with mean follow-up of 48.3 ± 33.6 months (range 0.03-182.07 months) who underwent surgery with iMRI. There were 201 (64.0%) WHO grade I tumors, 57 (18.2%) grade II, 24 (7.6%) grade III, 9 (2.9%) grade IV, and 23 (7.3%) not classified. Among 280 patients who underwent resection using iMRI, 131 (46.8%) had some residual tumor and underwent additional resection after the first iMRI. Of the 33 tissue specimens sent for pathological analysis after iMRI, 29 (87.9%) showed positive tumor pathology. Gross total resection was identified in 156 patients (55.7%), but this was limited by 69 (24.6%) patients with unknown EOR.
CONCLUSIONS: Analysis of the largest multicenter database of pediatric gliomas resected using iMRI demonstrated additional tumor resection in a substantial portion of cases. However, determining the impact of iMRI on EOR and outcomes remains challenging because iMRI use varies among providers nationally. Continued refinement of iMRI techniques for use in pediatric patients with glioma may improve outcomes.

Entities:  

Keywords:  Extent of resection; Glioma; Intraoperative MRI; Outcome; Pediatric; WHO grade; World Health of Organization; iMRI

Year:  2019        PMID: 30977059     DOI: 10.1007/s11060-019-03154-7

Source DB:  PubMed          Journal:  J Neurooncol        ISSN: 0167-594X            Impact factor:   4.130


  5 in total

Review 1.  Intraoperative MRI versus intraoperative ultrasound in pediatric brain tumor surgery: is expensive better than cheap? A review of the literature.

Authors:  Carlo Giussani; Andrea Trezza; Vittorio Ricciuti; Andrea Di Cristofori; Andrea Held; Valeria Isella; Maura Massimino
Journal:  Childs Nerv Syst       Date:  2022-05-05       Impact factor: 1.532

2.  Image Guidance in Cranial Neurosurgery: How a Six-Ton Magnet and Fluorescent Colors Make Brain Tumor Surgery Better.

Authors:  Michael R Chicoine; Peter Sylvester; Alexander T Yahanda; Amar Shah
Journal:  Mo Med       Date:  2020 Jan-Feb

3.  Immune Checkpoint-Associated Locations of Diffuse Gliomas Comparing Pediatric With Adult Patients Based on Voxel-Wise Analysis.

Authors:  Li Zhang; Buyi Zhang; Zhangqi Dou; Jiawei Wu; Yasaman Iranmanesh; Biao Jiang; Chongran Sun; Jianmin Zhang
Journal:  Front Immunol       Date:  2021-03-17       Impact factor: 7.561

4.  Combining Pre-operative Diffusion Tensor Images and Intraoperative Magnetic Resonance Images in the Navigation Is Useful for Detecting White Matter Tracts During Glioma Surgery.

Authors:  Manabu Tamura; Hiroyuki Kurihara; Taiichi Saito; Masayuki Nitta; Takashi Maruyama; Shunsuke Tsuzuki; Atsushi Fukui; Shunichi Koriyama; Takakazu Kawamata; Yoshihiro Muragaki
Journal:  Front Neurol       Date:  2022-01-20       Impact factor: 4.003

5.  Rapid intraoperative diagnosis of pediatric brain tumors using Raman spectroscopy: A machine learning approach.

Authors:  Rashad Jabarkheel; Chi-Sing Ho; Adrian J Rodrigues; Michael C Jin; Jonathon J Parker; Kobina Mensah-Brown; Derek Yecies; Gerald A Grant
Journal:  Neurooncol Adv       Date:  2022-07-26
  5 in total

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