Long-term follow-up of incidentally discovered meningiomas

Jadid, Kayvan D.; Feychting, Maria; Höijer, Jonas; Hylin, Sofia; Kihlström, Lars; Mathiesen, Tiit

doi:10.1007/s00701-014-2306-3

Cited by 70 publications

(53 citation statements)

References 24 publications

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“…Yano et al reported no significant difference in initial size in diameter between growing and non-growing tumors indicating a p-value of 0.737 [21]. Robin etal and Jadid etal also found it insignificant [8,17]. In our meta analysis we found that tumor growth was associated with larger initial diameter (P= 0.01).…”

Section: Initial Tumor Size:-mentioning

confidence: 44%

“…Nine of them could be statistically evaluated in our meta analysis [4,5,7,9,11,13,16,17,21], calcification was associated with significantly reduced risk of growth (P= 0.001). Furthermore Nakusa etal and Jadid etal [8,12] concluded the same results with different way of calculation. Only Hashiba et al reported that the presence of calcifications was more frequently observed in lesions that followed a linear growth pattern than in those following an exponential pattern (P= 0.05) [5] .…”

Section: Mri Findingsmentioning

confidence: 64%

“…[8]. Studies by Yoneoka et al , Herscovici et al ,Oya etal and Nakusa etal found patients with growing tumors were younger than those with non-growing tumors.…”

Section: General Demographicsmentioning

confidence: 95%

“…Twelve studies reported patients that ultimately underwent surgery [1,4,5,6,7,8,13,14,16,17,21,23]. Among patients with growing tumors, 27.8% underwent surgery, either open, radiosurgery, or both.…”

Section: Growth Rates:-mentioning

confidence: 99%

“…Published reports are often limited by a retrospective study design, small sample size, and short follow-up period. [1][2][3][4][5][6][7][8][9][10][11][12][13][14]16,17,19,[21][22][23] A systematic review and meta-analysis of the studies currently available allows for a better understanding of the natural course of asymptomatic meningiomas, a platform for more research, and a foundation on which a standardized guideline for following these tumors may be built.…”

mentioning

confidence: 99%

See 4 more Smart Citations

The Natural Course of Incidental Intracranial Meningiomas: Systematic Review and Meta-Analysis.

Eltantawy¹,

Nabeel²,

Bayomy³

2018

IJAR

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Section: Initial Tumor Size:-mentioning

confidence: 44%

Section: Mri Findingsmentioning

confidence: 64%

“…[8]. Studies by Yoneoka et al , Herscovici et al ,Oya etal and Nakusa etal found patients with growing tumors were younger than those with non-growing tumors.…”

Section: General Demographicsmentioning

confidence: 95%

Section: Growth Rates:-mentioning

confidence: 99%

mentioning

confidence: 99%

See 3 more Smart Citations

The Natural Course of Incidental Intracranial Meningiomas: Systematic Review and Meta-Analysis.

Eltantawy¹,

Nabeel²,

Bayomy³

2018

IJAR

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Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI

Chen

et al. 2019

Magnetic Resonance Imaging

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BackgroundIt is difficult to prospectively differentiate between benign (World Health Organization [WHO] I) and nonbenign (WHO II and III) meningiomas.PurposeTo evaluate the feasibility of preoperative differentiation between benign and nonbenign meningiomas by using texture analysis from multiparametric MR data.Study TypeRetrospective.SubjectsIn all, 184 patients with meningioma (139 benign and 45 nonbenign) were included as the training cohort and 79 patients with meningioma (60 benign and 19 nonbenign) were included as the external validation cohort.Field Strength/SequenceT1‐weighted, T2‐weighted, and contrast‐enhanced T1‐weighted imaging were performed on 1.5 or 3.0T MR systems from two centers.AssessmentTumor segmentation and radiological characteristic (RC) evaluation were performed by experienced radiologists. The texture features were extracted from preprocessed images and combined with RCs, and then the combined features were reduced by using a two‐step feature selection. Three single‐sequence models and a multiparametric MRI (the combination of single sequences) model were constructed and then evaluated with the external validation cohort.Statistical TestsArea under receiver operating characteristic curve (AUC), accuracy (Acc), f1‐score (F1), sensitivity (Sen), and specificity (Spec), were calculated to quantify the performance of the models.ResultsAmong the four texture models, the multiparametric MRI model demonstrated the best performance for differentiating between benign and nonbenign meningiomas in both the training and external validation cohorts (AUC 0.91, Acc 89%, F1 0.88, Sen 0.93, and Spec 0.87 in the training cohort; AUC 0.83, Acc 80%, F1 0.77, Sen 0.84, and Spec 0.78 in the validation cohort).Data ConclusionNonbenign meningiomas might be preoperatively differentiated from benign meningiomas by using texture analysis from multiparametric MR data.Level of Evidence: 3Technical Efficacy Stage: 2J. Magn. Reson. Imaging 2020;51:1810–1820.

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Surgical Considerations for Newly Diagnosed Meningiomas

Hong

Moliterno

2020

Meningiomas

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Long-term follow-up of incidentally discovered meningiomas

Cited by 70 publications

References 24 publications

The Natural Course of Incidental Intracranial Meningiomas: Systematic Review and Meta-Analysis.

The Natural Course of Incidental Intracranial Meningiomas: Systematic Review and Meta-Analysis.

Differentiation Between Benign and Nonbenign Meningiomas by Using Texture Analysis From Multiparametric MRI

Surgical Considerations for Newly Diagnosed Meningiomas

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