Literature DB >> 29353434

Prediction of progression in skull base meningiomas: additional benefits of apparent diffusion coefficient value.

Ching-Chung Ko1,2, Sher-Wei Lim1,3,4, Tai-Yuan Chen2,5, Jeon-Hor Chen6,7, Chien-Feng Li8, Yow-Ling Shiue9.   

Abstract

A subset of benign (WHO grade I) skull base meningiomas show early progression/recurrence (P/R) in the first years after surgical resection. Besides, complete surgical resection may be difficult to achieve safely in skull base meningiomas due to complex neurovascular structures. The one main challenge in the treatment of skull base meningiomas is to determine factors that correlate with P/R. We retrospectively investigated the preoperative CT and MR imaging features for the prediction of P/R in skull base meningiomas, with emphasis on quantitative ADC values. Only patients had postoperative MRI follow-ups for more than 1 year (at least every 6 months) were included. From October 2006 to December 2015, total 73 patients diagnosed with benign (WHO grade I) skull base meningiomas were included (median follow-up time 41 months), and 17 (23.3%) patients had P/R (median time to P/R 28 months). Skull base meningiomas with spheno-orbital location, adjacent bone invasion, high DWI, and lower ADC value/ratio were significantly associated with P/R (P < 0.05). The cut-off points of ADC value and ADC ratio for prediction of P/R are 0.83 × 10- 3 mm2/s and 1.09 respectively, with excellent area under curve (AUC) values (0.86 and 0.91) (P < 0.05). In multivariate logistic regression, low ADC values (< 0.83 × 10- 3 mm2/s) and adjacent bone invasion are high-risk factors of P/R (P < 0.05), with odds ratios of 31.53 and 17.59 respectively. The preoperative CT and MRI features for prediction of P/R offered clinically vital information for the planning of treatment in skull base meningiomas.

Entities:  

Keywords:  ADC; MRI; Meningioma; Recurrence; Skull base

Mesh:

Year:  2018        PMID: 29353434     DOI: 10.1007/s11060-018-2769-9

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


  32 in total

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4.  Quantitative apparent diffusion coefficients in the characterization of brain tumors and associated peritumoral edema.

Authors:  A Server; B Kulle; J Maehlen; R Josefsen; T Schellhorn; T Kumar; C W Langberg; P H Nakstad
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5.  Diffusion-weighted MR imaging: diagnosing atypical or malignant meningiomas and detecting tumor dedifferentiation.

Authors:  V A Nagar; J R Ye; W H Ng; Y H Chan; F Hui; C K Lee; C C T Lim
Journal:  AJNR Am J Neuroradiol       Date:  2008-03-20       Impact factor: 3.825

6.  Primary cerebral lymphoma and glioblastoma multiforme: differences in diffusion characteristics evaluated with diffusion tensor imaging.

Authors:  C-H Toh; M Castillo; A M-C Wong; K-C Wei; H-F Wong; S-H Ng; Y-L Wan
Journal:  AJNR Am J Neuroradiol       Date:  2007-12-07       Impact factor: 3.825

7.  Early recurrences in histologically benign/grade I meningiomas are associated with large tumors and coexistence of monosomy 14 and del(1p36) in the ancestral tumor cell clone.

Authors:  Angel Maillo; Alberto Orfao; Ana B Espinosa; José María Sayagués; Marta Merino; Pablo Sousa; Monica Lara; María Dolores Tabernero
Journal:  Neuro Oncol       Date:  2007-08-17       Impact factor: 12.300

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

Review 9.  Postradiation imaging changes in the CNS: how can we differentiate between treatment effect and disease progression?

Authors:  Amanda J Walker; Jake Ruzevick; Ashkan A Malayeri; Daniele Rigamonti; Michael Lim; Kristin J Redmond; Lawrence Kleinberg
Journal:  Future Oncol       Date:  2014-05       Impact factor: 3.404

10.  A comparison of World Health Organization tumor grades at recurrence in patients with non-skull base and skull base meningiomas.

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Journal:  J Neurosurg       Date:  2010-05       Impact factor: 5.115

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2.  Pre-operative MRI Radiomics for the Prediction of Progression and Recurrence in Meningiomas.

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3.  Machine Learning for Prediction of Recurrence in Parasagittal and Parafalcine Meningiomas: Combined Clinical and MRI Texture Features.

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4.  Spheno-orbital meningiomas: Is orbit reconstruction mandatory? Long-term outcomes and exophthalmos improvement.

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5.  Radiomics approach for prediction of recurrence in skull base meningiomas.

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6.  Long-Term Clinical Outcome of First Recurrence Skull Base Meningiomas.

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Journal:  J Clin Med       Date:  2019-12-31       Impact factor: 4.241

7.  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

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