Correlation of volumetric growth and histological grade in 50 meningiomas

Soon, Wai Cheong; Fountain, Daniel M; Koczyk, Kacper; Abdulla, Mutwakil A H; Giri, Sachin Ashok; Allinson, Kieren; Matys, Tomasz; Guilfoyle, Mathew R.; Kirollos, Ramez; Santarius, Thomas

doi:10.1007/s00701-017-3277-y

Cited by 20 publications

(8 citation statements)

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“…No significant difference was present in the relative growth rate; however, meningiomas ≥ 3 cm demonstrated a higher absolute growth rate (AGR) and a greater risk of clinical progression. This may suggest the use of AGR as a more clinically relevant measure of incidental meningioma growth, which has also been suggested to correlate with a higher WHO meningioma grade [ 33 ].…”

Section: Discussionmentioning

confidence: 99%

Incidental intracranial meningiomas: a systematic review and meta-analysis of prognostic factors and outcomes

et al. 2019

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Background Incidental discovery accounts for 30% of newly-diagnosed intracranial meningiomas. There is no consensus on their optimal management. This review aimed to evaluate the outcomes of different management strategies for these tumors. Methods Using established systematic review methods, six databases were scanned up to September 2017. Pooled event proportions were estimated using a random effects model. Meta-regression of prognostic factors was performed using individual patient data. Results Twenty studies (2130 patients) were included. Initial management strategies at diagnosis were: surgery (27.3%), stereotactic radiosurgery (22.0%) and active monitoring (50.7%) with a weighted mean follow-up of 49.5 months (SD = 29.3). The definition of meningioma growth and monitoring regimens varied widely impeding relevant meta-analysis. The pooled risk of symptom development in patients actively monitored was 8.1% (95% CI 2.7–16.1). Associated factors were peritumoral edema (OR 8.72 [95% CI 0.35–14.90]) and meningioma diameter ≥ 3 cm (OR 34.90 [95% CI 5.17–160.40]). The pooled proportion of intervention after a duration of active monitoring was 24.8% (95% CI 7.5–48.0). Weighted mean time-to-intervention was 24.8 months (SD = 18.2). The pooled risks of morbidity following surgery and radiosurgery, accounting for cross-over, were 11.8% (95% CI 3.7–23.5) and 32.0% (95% CI 10.6–70.5) respectively. The pooled proportion of operated meningioma being WHO grade I was 94.0% (95% CI 88.2–97.9). Conclusion The management of incidental meningioma varies widely. Most patients who clinically or radiologically progressed did so within 5 years of diagnosis. Intervention at diagnosis may lead to unnecessary overtreatment. Prospective data is needed to develop a risk calculator to better inform management strategies. Electronic supplementary material The online version of this article (10.1007/s11060-019-03104-3) contains supplementary material, which is available to authorized users.

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Section: Discussionmentioning

confidence: 99%

Incidental intracranial meningiomas: a systematic review and meta-analysis of prognostic factors and outcomes

et al. 2019

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“…Furthermore, these imaging metrics represent a single MRI session at a single point in time, and thus do not take into account intratumoral changes over time, which have been shown to predict grade II meningioma. 29 Despite these limitations, this proof-of-principle study demonstrates the feasibility of harnessing AI and ML techniques in predicting meningioma grade based on a radiologist-interpreted preoperative MR image. Further studies of this algorithm, including external validation across radiologists and institutions, are warranted.…”

Section: Discussionmentioning

confidence: 88%

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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“…Our understanding of the pathophysiology of peritumoural oedema associated with meningiomas remains incomplete. Previous studies have implicated size, [ 49 ] growth rate, [ 50 ] leptomeningeal invasion, development of pial blood supply, [ 51 , 52 ] as well as specific histological types [ 51 , 53 ] with development of peritumoural oedema. In our series, the presence of peritumoural oedema was significantly associated with early aggressive behaviour and recurrence at 24 months.…”

Section: Discussionmentioning

confidence: 99%

Predictors of early progression of surgically treated atypical meningiomas

et al. 2018

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BackgroundClinical behaviour of atypical meningiomas is not uniform. While, as a group, they exhibit a high recurrence rate, some pursue a more benign course, whereas others progress early. We aim to investigate the imaging and pathological factors that predict risk of early tumour progression and to determine whether early progression is related to outcome.MethodsAdult patients with WHO grade II meningioma treated in three regional referral centres between 2007 and 2014 were included. MRI and pathology characteristics were assessed. Gross total resection (GTR) was defined as Simpson 1–3. Recurrence was classified into early and late (≤ 24 vs. > 24 months).ResultsAmong the 220 cases, 37 (16.8%) patients progressed within 24 months of operation. Independent predictors of early progression were subtotal resection (STR) (p = 0.005), parafalcine/parasagittal location (p = 0.015), peritumoural oedema (p = 0.027) and mitotic index (MI) > 7 (p = 0.007). Adjuvant radiotherapy was negatively associated with early recurrence (p = 0.046). Thirty-two per cent of patients with residual tumour and 26% after GTR received adjuvant radiotherapy. There was a significantly lower proportion of favourable outcomes at last follow-up (mRS 0–1) in patients with early recurrence (p = 0.001).ConclusionsAtypical meningiomas are a heterogeneous group of tumours with 16.8% patients having recurrence within 24 months of surgery. Residual tumour, parafalcine/parasagittal location, peritumoural oedema and a MI > 7 were all independently associated with early recurrence. As administration of adjuvant radiotherapy was not protocolised in this cohort, any conclusions about benefits of irradiation of WHO grade II meningiomas should be viewed with caution. Patients with early recurrence had worse neurological outcome. While histological and imaging characteristics provide some prognostic value, further molecular characterisation of atypical meningiomas is warranted to aid clinical decision making.

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Correlation of volumetric growth and histological grade in 50 meningiomas

Cited by 20 publications

References 25 publications

Incidental intracranial meningiomas: a systematic review and meta-analysis of prognostic factors and outcomes

Incidental intracranial meningiomas: a systematic review and meta-analysis of prognostic factors and outcomes

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

Predictors of early progression of surgically treated atypical meningiomas

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