Literature DB >> 20428902

Diffusion-weighted imaging does not predict histological grading in meningiomas.

Luca Santelli1, Gaetano Ramondo, Alessandro Della Puppa, Mario Ermani, Renato Scienza, Domenico d'Avella, Renzo Manara.   

Abstract

PURPOSE: This study aims to verify the reliability of diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) measurements to differentiate benign from atypical/malignant meningiomas and among different sub-types.
METHODS: Pre-operative DWI of 102 patients (74 females, mean age 58 years, age range 34-85 years) affected by a pathologically proven intracranial meningioma were retrospectively reviewed. DWI signal intensity of tumors was classified as hypo-, iso- or hyper-intense to grey matter. ADC values and normalised ADC(ratio) (ADC(meningioma)/ADC(normal appearing white matter)) of the neoplastic tissue (avoiding calcifications and cystic or necrotic areas) were measured by two neuroradiologists unaware of each others' reading. MRI and histological findings were compared.
RESULTS: Meningiomas were histologically graded as malignant (1%), atypical (21.5%) and benign (77.5%). Meningothelial, transitional and fibrous were the most frequent benign sub-types (44, 16 and 10 cases, respectively). There was no statistical difference between typical and atypical/malignant meningiomas when considering mean ADC values (0.964 +/- 0.192 x 10(-3) vs 0.923 +/- 0.085 x 10(-3) cm(2)/s, p = 0.3 t-Student) or ADC(ratio) (1.266 +/- 0.290 vs 1.185 +/- 0.115, p = 0.2 t-Student). ADC values or ADC(ratio) did not differ significantly among meningioma sub-types although a nearly significant difference was found between meningothelial and transitional (post hoc analysis p = 0.06). Inter-observer agreement of ADC and ADC(ratio) measurements was high (r = 0.95 and 0.92, respectively, Pearson's linear coefficient). DWI intensity did not reach a significant correlation with meningioma's grading (p = 0.08).
CONCLUSIONS: According to our study, DWI and ADC measurement do not seem reliable in grading meningiomas or identifying histological sub-types. Hence, these parameters should not be recommended for surgical or treatment planning.

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Year:  2010        PMID: 20428902     DOI: 10.1007/s00701-010-0657-y

Source DB:  PubMed          Journal:  Acta Neurochir (Wien)        ISSN: 0001-6268            Impact factor:   2.216


  25 in total

1.  Relation of apparent diffusion coefficient with Ki-67 proliferation index in meningiomas.

Authors:  Ozdil Baskan; Gokalp Silav; Fatih Han Bolukbasi; Ozlem Canoz; Serdar Geyik; Ilhan Elmaci
Journal:  Br J Radiol       Date:  2015-11-05       Impact factor: 3.039

2.  The diagnostic value of using combined MR diffusion tensor imaging parameters to differentiate between low- and high-grade meningioma.

Authors:  Kerim Aslan; Hediye Pinar Gunbey; Leman Tomak; Lutfi Incesu
Journal:  Br J Radiol       Date:  2018-05-31       Impact factor: 3.039

3.  Can amide proton transfer-weighted imaging differentiate tumor grade and predict Ki-67 proliferation status of meningioma?

Authors:  Hao Yu; Xinrui Wen; Pingping Wu; Yueqin Chen; Tianyu Zou; Xianlong Wang; Shanshan Jiang; Jinyuan Zhou; Zhibo Wen
Journal:  Eur Radiol       Date:  2019-03-18       Impact factor: 5.315

4.  A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery.

Authors:  Yan Tan; Shuai-Tong Zhang; Jing-Wei Wei; Di Dong; Xiao-Chun Wang; Guo-Qiang Yang; Jie Tian; Hui Zhang
Journal:  Eur Radiol       Date:  2019-04-10       Impact factor: 5.315

Review 5.  Can MRI predict meningioma consistency?: a correlation with tumor pathology and systematic review.

Authors:  Amy Yao; Margaret Pain; Priti Balchandani; Raj K Shrivastava
Journal:  Neurosurg Rev       Date:  2016-11-21       Impact factor: 3.042

6.  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

Review 7.  Modern meningioma imaging techniques.

Authors:  D Saloner; A Uzelac; S Hetts; A Martin; W Dillon
Journal:  J Neurooncol       Date:  2010-09-01       Impact factor: 4.130

Review 8.  Variants of meningiomas: a review of imaging findings and clinical features.

Authors:  Akira Kunimatsu; Natsuko Kunimatsu; Kouhei Kamiya; Masaki Katsura; Harushi Mori; Kuni Ohtomo
Journal:  Jpn J Radiol       Date:  2016-05-02       Impact factor: 2.374

9.  Imaging and extent of surgical resection predict risk of meningioma recurrence better than WHO histopathological grade.

Authors:  William L Hwang; Ariel E Marciscano; Andrzej Niemierko; Daniel W Kim; Anat O Stemmer-Rachamimov; William T Curry; Fred G Barker; Robert L Martuza; Jay S Loeffler; Kevin S Oh; Helen A Shih; Mykol Larvie
Journal:  Neuro Oncol       Date:  2015-11-22       Impact factor: 12.300

10.  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

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