Literature DB >> 27037557

Prediction of pediatric meningioma recurrence by preoperative MRI assessment.

Hao Li1,2, Meng Zhao1,2, Shuo Wang1,2, Yong Cao3,4, Jizong Zhao5,6.   

Abstract

Preoperative identification of high-recurrent pediatric meningiomas with MRI features would help clinicians to make optimal treatment strategies; however, the relationships between radiological features and recurrence of meningiomas in pediatric population have not been clearly demonstrated yet. The aim of this study is to identify preoperative MRI features which are significant risk factors for recurrence of pediatric meningiomas. From January 2005 to December 2012, we retrospectively reviewed 52 pediatric meningiomas in terms of preoperative MRI features and their clinical data and followed them up from 22 to 128 months (mean 63 months) after the initial surgery. The relationships between these radiological findings and relapse-free survival (RFS) time were assessed initially with univariate Cox analysis and then corrected by multivariate Cox analysis. According to univariate analysis, irregular shape, narrow-based attachment, and skull base location were significantly correlated with shorter time to recurrences of meningiomas in pediatric patients. When corrected by multivariate analysis, irregular shape (P = 0.05; OR 3.442, 95 % CI 1.001-11.831) and narrow-based attachment (P = 0.004; OR 7.164, 95 % CI 1.894-27.09) were strong independent predictive factors for worse RFS of pediatric meningiomas. In pediatric population, narrow-based attachment and irregular shape were significantly correlated with recurrences of meningiomas. Our results could help clinicians to make optimal therapeutic strategies for pediatric patients with intracranial meningiomas before surgery.

Entities:  

Keywords:  MRI; Pediatric meningioma; Prediction; Recurrence

Mesh:

Year:  2016        PMID: 27037557     DOI: 10.1007/s10143-016-0716-9

Source DB:  PubMed          Journal:  Neurosurg Rev        ISSN: 0344-5607            Impact factor:   3.042


  25 in total

1.  Intracranial meningiomas of childhood and adolescence.

Authors:  Kadir Tufan; Fikret Dogulu; Gokhan Kurt; Hakan Emmez; Necdet Ceviker; M Kemali Baykaner
Journal:  Pediatr Neurosurg       Date:  2005 Jan-Feb       Impact factor: 1.162

2.  Risk factors predicting recurrence in patients operated on for intracranial meningioma. A multivariate analysis.

Authors:  J Ayerbe; R D Lobato; J de la Cruz; R Alday; J J Rivas; P A Gómez; A Cabrera
Journal:  Acta Neurochir (Wien)       Date:  1999       Impact factor: 2.216

3.  Predicting the probability of meningioma recurrence based on the quantity of peritumoral brain edema on computerized tomography scanning.

Authors:  R E Mantle; B Lach; M R Delgado; S Baeesa; G Bélanger
Journal:  J Neurosurg       Date:  1999-09       Impact factor: 5.115

Review 4.  Meningiomas in children and adolescents: a meta-analysis of individual patient data.

Authors:  Rishi S Kotecha; Elaine M Pascoe; Elisabeth J Rushing; Lucy B Rorke-Adams; Ted Zwerdling; Xing Gao; Xin Li; Stephanie Greene; Abbas Amirjamshidi; Seung-Ki Kim; Marco A Lima; Po-Cheng Hung; Fayçal Lakhdar; Nirav Mehta; Yuguang Liu; B Indira Devi; B Jayanand Sudhir; Morten Lund-Johansen; Flemming Gjerris; Catherine H Cole; Nicholas G Gottardo
Journal:  Lancet Oncol       Date:  2011-11-15       Impact factor: 41.316

5.  Classic, atypical, and anaplastic meningioma: three histopathological subtypes of clinical relevance.

Authors:  H Maier; D Ofner; A Hittmair; K Kitz; H Budka
Journal:  J Neurosurg       Date:  1992-10       Impact factor: 5.115

6.  Pediatric intracranial meningiomas--do they differ from their counterparts in adults?

Authors:  A Arivazhagan; B Indira Devi; Sastry V R Kolluri; R G Abraham; S Sampath; B A Chandramouli
Journal:  Pediatr Neurosurg       Date:  2007-12-14       Impact factor: 1.162

7.  Meningioma: proliferating potential and clinicoradiological features.

Authors:  S Nakasu; M Nakajima; K Matsumura; Y Nakasu; J Handa
Journal:  Neurosurgery       Date:  1995-12       Impact factor: 4.654

8.  Predicting the probability of meningioma recurrence in the preoperative and early postoperative period: a multivariate analysis in the midterm follow-up.

Authors:  Faruk Ildan; Tahsin Erman; A Iskender Göçer; Metin Tuna; Hüseyin Bağdatoğlu; Erdal Cetinalp; Refik Burgut
Journal:  Skull Base       Date:  2007-05

9.  Proposal for a new risk stratification classification for meningioma based on patient age, WHO tumor grade, size, localization, and karyotype.

Authors:  Patrícia Henriques Domingues; Pablo Sousa; Álvaro Otero; Jesus Maria Gonçalves; Laura Ruiz; Catarina de Oliveira; Maria Celeste Lopes; Alberto Orfao; Maria Dolores Tabernero
Journal:  Neuro Oncol       Date:  2014-02-16       Impact factor: 12.300

10.  CT findings in malignant meningiomas.

Authors:  J L Dietemann; N Heldt; J L Burguet; L Medjek; D Maitrot; A Wackenheim
Journal:  Neuroradiology       Date:  1982       Impact factor: 2.804

View more
  5 in total

1.  High-precision radiotherapy for meningiomas : Long-term results and patient-reported outcome (PRO).

Authors:  Kerstin A Kessel; Hanna Fischer; Markus Oechnser; Claus Zimmer; Bernhard Meyer; Stephanie E Combs
Journal:  Strahlenther Onkol       Date:  2017-06-15       Impact factor: 3.621

2.  Changes in a sensorimotor network, occipital network, and psychomotor speed within three months after focal surgical injury in pediatric patients with intracranial space-occupying lesions.

Authors:  Xue-Yi Guan; Wen-Jian Zheng; Kai-Yu Fan; Xu Han; Xiang Li; Zi-Han Yan; Zheng Lu; Jian Gong
Journal:  BMC Pediatr       Date:  2022-06-01       Impact factor: 2.567

3.  Clinical features and long-term outcomes of pediatric intraventricular meningiomas: data from a single neurosurgical center.

Authors:  Zhicen Li; Hao Li; Yuming Jiao; Ji Ma; Shuo Wang; Yong Cao; Jizong Zhao
Journal:  Neurosurg Rev       Date:  2017-08-02       Impact factor: 3.042

4.  Automatic Prediction of Meningioma Grade Image Based on Data Amplification and Improved Convolutional Neural Network.

Authors:  Hong Zhu; Qianhao Fang; Hanzhi He; Junfeng Hu; Daihong Jiang; Kai Xu
Journal:  Comput Math Methods Med       Date:  2019-10-01       Impact factor: 2.238

5.  Predicting the risk of postoperative recurrence and high-grade histology in patients with intracranial meningiomas using routine preoperative MRI.

Authors:  Dorothee Cäcilia Spille; Alborz Adeli; Peter B Sporns; Katharina Heß; Eileen Maria Susanne Streckert; Caroline Brokinkel; Christian Mawrin; Werner Paulus; Walter Stummer; Benjamin Brokinkel
Journal:  Neurosurg Rev       Date:  2020-04-23       Impact factor: 3.042

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.