Literature DB >> 32789536

Peritumoral edema correlates with mutational burden in meningiomas.

Corey M Gill1, Joshua Loewenstern2, John W Rutland2, Hanane Arib3, Margaret Pain2, Melissa Umphlett4, Yayoi Kinoshita4, Russell B McBride4,5, Joshua Bederson2, Michael Donovan4, Robert Sebra3,6, Mary Fowkes4, Raj K Shrivastava2.   

Abstract

PURPOSE: Meningiomas are the most common primary central nervous system tumor. Emerging data supports that higher mutational burden portends worse clinical outcomes in meningiomas. However, there is a lack of imaging biomarkers that are associated with tumor genomics in meningiomas.
METHODS: We performed next-generation targeted sequencing in a cohort of 75 primary meningiomas and assessed preoperative imaging for tumor volume and peritumoral brain edema (PTBE). An Edema Index was calculated.
RESULTS: Meningiomas that were high grade (WHO grade II or grade III) had significantly larger tumor volume and were more likely to present with PTBE. Moreover, PTBE was associated with brain invasion on histopathology and reduced overall survival. There was a direct association between Edema Index and mutational burden. For every one increase in Edema Index, the number of single nucleotide variants increased by 1.09-fold (95% CI: 1.02, 1.2) (P = 0.01).
CONCLUSION: These data support that Edema Index may serve as a novel imaging biomarker that can inform underlying mutational burden in patients with meningiomas.

Entities:  

Keywords:  Biomarker; Central nervous system; Edema; Imaging; Meningioma; Sequencing

Year:  2020        PMID: 32789536     DOI: 10.1007/s00234-020-02515-8

Source DB:  PubMed          Journal:  Neuroradiology        ISSN: 0028-3940            Impact factor:   2.804


  23 in total

1.  Comparison of machine learning classifiers for differentiation of grade 1 from higher gradings in meningioma: A multicenter radiomics study.

Authors:  Gordian Hamerla; Hans-Jonas Meyer; Stefan Schob; Daniel T Ginat; Ashley Altman; Tchoyoson Lim; Georg Alexander Gihr; Diana Horvath-Rizea; Karl-Titus Hoffmann; Alexey Surov
Journal:  Magn Reson Imaging       Date:  2019-08-16       Impact factor: 2.546

2.  Correlation between magnetic resonance imaging grading and pathological grading in meningioma.

Authors:  Bon-Jour Lin; Kuan-Nein Chou; Hung-Wen Kao; Chin Lin; Wen-Chiuan Tsai; Shao-Wei Feng; Meei-Shyuan Lee; Dueng-Yuan Hueng
Journal:  J Neurosurg       Date:  2014-08-22       Impact factor: 5.115

3.  Prediction of High-Grade Histology and Recurrence in Meningiomas Using Routine Preoperative Magnetic Resonance Imaging: A Systematic Review.

Authors:  Dorothee Cäcilia Spille; Peter B Sporns; Katharina Heß; Walter Stummer; Benjamin Brokinkel
Journal:  World Neurosurg       Date:  2019-05-10       Impact factor: 2.104

4.  Imaging and diagnostic advances for intracranial meningiomas.

Authors:  Raymond Y Huang; Wenya Linda Bi; Brent Griffith; Timothy J Kaufmann; Christian la Fougère; Nils Ole Schmidt; Jöerg C Tonn; Michael A Vogelbaum; Patrick Y Wen; Kenneth Aldape; Farshad Nassiri; Gelareh Zadeh; Ian F Dunn
Journal:  Neuro Oncol       Date:  2019-01-14       Impact factor: 12.300

5.  Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging.

Authors:  Andrew T Hale; David P Stonko; Li Wang; Megan K Strother; Lola B Chambless
Journal:  Neurosurg Focus       Date:  2018-11-01       Impact factor: 4.047

6.  Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.

Authors:  Yae Won Park; Jongmin Oh; Seng Chan You; Kyunghwa Han; Sung Soo Ahn; Yoon Seong Choi; Jong Hee Chang; Se Hoon Kim; Seung-Koo Lee
Journal:  Eur Radiol       Date:  2018-11-15       Impact factor: 5.315

7.  Outcome of resection of WHO Grade II meningioma and correlation of pathological and radiological predictive factors for recurrence.

Authors:  Anil Nanda; Shyamal C Bir; Subhas Konar; Tanmoy Maiti; Piyush Kalakoti; Jamie A Jacobsohn; Bharat Guthikonda
Journal:  J Clin Neurosci       Date:  2016-07-15       Impact factor: 1.961

8.  Radiographic prediction of meningioma grade by semantic and radiomic features.

Authors:  Thibaud P Coroller; Wenya Linda Bi; Elizabeth Huynh; Malak Abedalthagafi; Ayal A Aizer; Noah F Greenwald; Chintan Parmar; Vivek Narayan; Winona W Wu; Samuel Miranda de Moura; Saksham Gupta; Rameen Beroukhim; Patrick Y Wen; Ossama Al-Mefty; Ian F Dunn; Sandro Santagata; Brian M Alexander; Raymond Y Huang; Hugo J W L Aerts
Journal:  PLoS One       Date:  2017-11-16       Impact factor: 3.240

9.  The association between preoperative edema and postoperative cognitive functioning and health-related quality of life in WHO grade I meningioma patients.

Authors:  David van Nieuwenhuizen; K Mariam Slot; Martin Klein; Dagmar Verbaan; Esther Sanchez Aliaga; Jan J Heimans; W Peter Vandertop; Saskia M Peerdeman; Jaap C Reijneveld
Journal:  Acta Neurochir (Wien)       Date:  2019-02-13       Impact factor: 2.216

10.  CORRELATION OF PERITUMORAL BRAIN EDEMA WITH MORPHOLOGICAL CHARACTERISTICS AND KI67 PROLIFERATIVE INDEX IN RESECTED INTRACRANIAL MENINGIOMAS.

Authors:  Hakija Bečulić; Rasim Skomorac; Aldin Jusić; Fahrudin Alić; Anes Mašović; Eldin Burazerović; Ibrahim Omerhodžić; Mirsad Dorić; Melica Imamović; Alma Mekić-Abazović; Alma Efendić; Dalma Udovčić-Gagula
Journal:  Acta Clin Croat       Date:  2019-03       Impact factor: 0.780

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  5 in total

1.  Nomogram based on MRI can preoperatively predict brain invasion in meningioma.

Authors:  Jing Zhang; Yuntai Cao; Guojin Zhang; Zhiyong Zhao; Jianqing Sun; Wenyi Li; Jialiang Ren; Tao Han; Junlin Zhou; Kuntao Chen
Journal:  Neurosurg Rev       Date:  2022-09-30       Impact factor: 2.800

2.  Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area.

Authors:  Teiji Tominaga; Kei Takase; Naoko Mori; Shunji Mugikura; Toshiki Endo; Hidenori Endo; Yo Oguma; Li Li; Akira Ito; Mika Watanabe; Masayuki Kanamori
Journal:  Neuroradiology       Date:  2022-08-31       Impact factor: 2.995

Review 3.  Synthesizing Molecular and Immune Characteristics to Move Beyond WHO Grade in Meningiomas: A Focused Review.

Authors:  Nivedha V Kannapadi; Pavan P Shah; Dimitrios Mathios; Christopher M Jackson
Journal:  Front Oncol       Date:  2022-05-31       Impact factor: 5.738

4.  Are the clinical manifestations of CT scan and location associated with World Health Organization histopathological grades of meningioma?: A retrospective study.

Authors:  Razieh Behzadmehr; Rezvaneh Behzadmehr
Journal:  Ann Med Surg (Lond)       Date:  2021-04-30

5.  Efficacy of Bevacizumab in High-Grade Meningiomas: A Retrospective Clinical Study.

Authors:  Xuexue Bai; Xiaomin Liu; Jun Wen
Journal:  Neuropsychiatr Dis Treat       Date:  2022-08-06       Impact factor: 2.989

  5 in total

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