Reliability of the Size Evaluation Method for Meningiomas: Maximum Diameter, ABC/2 Formula, and Planimetry Method

Ishi, Yukitomo; Terasaka, Shunsuke; Yamaguchi, Shigeru; Yoshida, Michiharu; Endo, Shogo; Kobayashi, Hiroyuki; Houkin, Kiyohiro

doi:10.1016/j.wneu.2016.06.108

Cited by 22 publications

(20 citation statements)

References 31 publications

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“…Tumor volume was calculated using manual tracing, considering the largest diameter in the anterior-posterior, craniocaudal, or transverse directions seen on T1-weighted MRI as previously described. 14 Only patients with complete data were included in these analyses; no data imputation was performed. Furthermore, female sex (p = 0.072) and the presence of a draining vein on preoperative MRI (p = 0.079) approached statistical significance.…”

Section: Methodsmentioning

confidence: 99%

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

Hale

Stonko

Wang

et al. 2018

Neurosurgical Focus

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OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression are useful, machine learning (ML) algorithms are often more predictive, have higher discriminative ability, and can learn from new data. The authors used conventional statistical models and an array of ML algorithms to predict atypical meningioma based on radiologist-interpreted preoperative MRI findings. The goal of this study was to compare the performance of ML algorithms to standard statistical methods when predicting meningioma grade.METHODSThe cohort included patients aged 18–65 years with WHO grade I (n = 94) and II (n = 34) meningioma in whom preoperative MRI was obtained between 1998 and 2010. A board-certified neuroradiologist, blinded to histological grade, interpreted all MR images for tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, presence of a draining vein, and patient sex. The authors trained and validated several binary classifiers: k-nearest neighbors models, support vector machines, naïve Bayes classifiers, and artificial neural networks as well as logistic regression models to predict tumor grade. The area under the curve–receiver operating characteristic curve was used for comparison across and within model classes. All analyses were performed in MATLAB using a MacBook Pro.RESULTSThe authors included 6 preoperative imaging and demographic variables: tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, patient sex, and presence of a draining vein to construct the models. The artificial neural networks outperformed all other ML models across the true-positive versus false-positive (receiver operating characteristic) space (area under curve = 0.8895).CONCLUSIONSML algorithms are powerful computational tools that can predict meningioma grade with great accuracy.

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Section: Methodsmentioning

confidence: 99%

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

Hale

Stonko

Wang

et al. 2018

Neurosurgical Focus

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“…Tumour size and presence of oedema were determined by MR imaging. The maximum diameters of the tumours were measured and their volumes estimated using the following formula (tumour volume = ABC/2; A = maximum tumour diameter, B = diameter of the tumour perpendicular to A, and C = maximum height of the tumour) (4, 12). Hyperostosis and calcification were documented based on CT images (13).…”

Section: Methodsmentioning

confidence: 99%

Incidence, Clinico-Radiological Features and Outcome of Skull Base versus Non-Skull Base Meningiomas Treated in Kuala Lumpur General Hospital: A Five-Year Experience

Kong

Kandasamy

Haspani

et al. 2018

MJMS

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Background: Meningiomas are the most common intracranial tumours; they account for 13%-26% of all the primary intracranial tumours. Skull base meningiomas make up 25% of all meningiomas and are one of the most difficult intracranial tumours to be managed surgically. This is due to the fact that it is difficult to approach the lesions which are also close to vital structures such as cranial nerves and major blood vessels. Despite the abundance of these cases in Malaysia, local data on meningiomas is scarce.Methods: This is a retrospective study consisting of 199 patients with meningiomas who have been operated at the Kuala Lumpur General Hospital from January 2010-December 2014. They were categorised into skull base and non-skull base groups. Demography, tumour characteristics, and patient outcomes were analysed. Kaplan-Meier survival curves as well as Cox hazard univariable and multivariable regressions for the possible predictors of survival were analysed.Results: 97.5% of the patients (n = 194) had WHO grade I meningioma and only five patients had WHO grade II meningioma. There was a female predominance (n = 134; 67.3%), with a male-to-female ratio of 1:2. Some 27.1 % patients had skull base meningiomas. Patients with skull base meningiomas had poorer outcomes and discharge conditions (n = 23; 42.6% P < 0.01), in addition to higher risk of incomplete resections (n = 34; 63% P < 0.01). Multivariate cox hazard regressions showed that the skull base meningioma group had four times the risk of death of the non-skull base group.Conclusions: Symptomatic meningiomas can be curative if the tumour is completely removed. Our study has revealed that skull base meningiomas which were operated locally had higher rates of incomplete resection and poorer surgical outcomes as compared to the nonskull base group. Patients with skull base meningiomas had four times the risk of death vis-à-vis non-skull base ones. More local studies are needed to look into skull base meningiomas for the improvement of its surgical outcomes. Keywords Methodology Study PopulationThis is a retrospective review of all patients with newly-diagnosed and positive histology of intracranial meningiomas who underwent surgical treatment at Hospital Kuala Lumpur

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“…After discarding duplicates, 344 articles remained. We read their abstracts and excluded case reports and reports of small series containing fewer than 10 cases (53), extracranial meningiomas (17), nonmeningiomas (57), treatment-related papers (83), radiological studies (37), those with descriptions of the pathology alone (13), and those on radiationinduced meningiomas (26). Among the remaining 58 papers, we selected those papers that had clinical data of untreated meningiomas with radiological examinations performed on at least two timepoints, but excluded neurofibromatosis case series.…”

Section: Article Selectionmentioning

confidence: 99%

“…Moreover, the methods using maximum diameter or volume calculated from three perpendicular axes are insufficiently sensitive to detect subtle changes. 29,37) Studies using volumetry based on image analysis software and a longer follow-up period showed higher growth rates of up to 70%. 4,9,13) Our review showed that, in volumetric studies, the curve of percentage of growing tumor against mean follow-up time fitted well to a logistic curve that approached an asymptotic volume of 66.7% (Fig.…”

Section: Radiological Progressionmentioning

confidence: 99%

Natural History of Meningiomas: Review with Meta-analyses

Nakasu¹,

Nakasu

2020

Neurol. Med. Chir.(Tokyo)

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Observation has been a mainstay in asymptomatic meningiomas, but it may increase the risk associated with treatment due to tumor enlargement and the aging of patients. Understanding the natural course of meningiomas is important to provide appropriate treatment. The majority of previous studies investigated factors related to their growth, but failed to demonstrate their relationship with symptomatic progression (sympP) because of its rarity. We reviewed and meta-analyzed 27 studies that investigated natural courses in asymptomatic or untreated meningiomas to find clinico-radiological factors predictive of radiological progression (radioP), growth speed, and sympP. In results of time-growth analysis, twothirds of meningiomas showed radioP defined by a volume criterion and the rate approached a plateau at 4-5 years. In growth curve analyses, about half of incidental meningiomas presented decelerating or no growth, while less than one-quarter of them grew exponentially. RadioP, growth speed [annual volume change (AVC) or relative growth rate], and sympP each had different factors related to them. Younger age, non-calcification, and high intensity on T2-weighted image were related to radioP and rapid growth speed, but not to sympP. Tumors in males and those of larger size were likely to be symptomatic in the meta-analysis. AVC (≥2.1 cm 3 /year) was the strongest indicator of sympP. Apart from perifocal edema, radiological features at up-front imaging may not be useful for predicting sympP. This may be due to dynamic changes of those radiological markers in the long term. Quantified tumor size and growth speed, especially AVC, are important markers for deciding on treatment.

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Reliability of the Size Evaluation Method for Meningiomas: Maximum Diameter, ABC/2 Formula, and Planimetry Method

Cited by 22 publications

References 31 publications

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

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

Incidence, Clinico-Radiological Features and Outcome of Skull Base versus Non-Skull Base Meningiomas Treated in Kuala Lumpur General Hospital: A Five-Year Experience

Natural History of Meningiomas: Review with Meta-analyses

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