Brain invasion in meningiomas: does surgical sampling impact specimen characteristics and histology?

Timme, Maximilian; Thomas, Christian; Spille, Dorothee Cäcilia; Stummer, Walter; Ebel, Heinrich; Ewelt, Christian; Hans, Franz-Josef; Schick, Uta; Puchner, M. J. A.; Wildförster, U.; Bruns, Bernhard; Trost, H. A.; Holling, Markus; Grauer, Oliver; Heß, Katharina; Brokinkel, Benjamin

doi:10.1007/s10143-019-01125-0

Cited by 14 publications

(15 citation statements)

References 13 publications

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“…9 The authors also suggest that surgical nuances among the different institutions might alter specimen characteristics and findings at neuropathological examination. 8 Another work suggested that extensive sampling improves brain invasion assessment, which is in accordance with our findings. 10 The distribution of other histopathological parameters used for meningioma grading have not been studied to our knowledge.…”

Section: Discussionsupporting

confidence: 92%

“…5 The A recent work showed differences among neuropathological departments for the rate of residual tissue not subjected to neuropathological analyses. 8 The authors also found that rates of tissue not submitted for histopathological analysis were higher in high grade tumour than in low grade tumour. We suggest that this might be explained by the fact that grade 2 meningiomas are larger than grade 1 meningioma.…”

Section: Discussionmentioning

confidence: 94%

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Meningioma sampling: how much is enough for the accurate grading of atypical meningiomas?

et al. 2021

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 94%

Meningioma sampling: how much is enough for the accurate grading of atypical meningiomas?

et al. 2021

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“…In general, surgical techniques have been shown to have an impact on the rate of microscopically detected BI. A recent study observed a significant difference of the rate of brain tissue in the specimen as well as that of BI among different neurosurgical centres [25]. In particular, random intraoperative sampling that does not precisely select tumour close to adjacent brain tissue may influence the detection of BI.…”

Section: Discussionmentioning

confidence: 99%

The prognostic significance of clinicopathological features in meningiomas: Microscopic brain invasion can predict patient outcome in otherwise benign meningiomas

Banan

Abbetmeier-Basse

Hong

et al. 2021

Neuropathology Appl Neurobio

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Aims Brain invasion (BI) was firstly defined as a single criterion of atypia in otherwise benign meningiomas in the revised fourth edition of 2016 WHO classification of brain tumours after being previously inconsistently addressed. However, recent studies have raised doubts about the prognostic significance of BI in otherwise benign meningiomas. In our study, we investigate the reproducibility of such a prognostic effect. Methods We identified two cohorts one consisting of 483 patients with meningioma WHO grade I (M°I) or atypical meningioma WHO grade II (M°II) from Hannover Medical School and the other including atypical meningiomas defined according to the classical WHO criteria (M°IIb) from the University Hospital Heidelberg. Follow‐up data with a median observation time of 38.2 months were available from 308 cases. These included 243 M°I and 65 M°II patients with the latter group consisting of 25 patients with otherwise benign meningiomas with BI (M°IIa) and 40 with M°IIb. Results A significant difference of progression‐free interval (PFI) was found between patients with M°I and M°II, M°I and M°IIa and those with M°I and M°IIb of both cohorts and each separately. However, PFI of M°IIa and M°IIb patients showed no significant difference. In the multivariate regression analysis adjusted for M°I/M°IIa versus M°IIb, sex, age, extent of resection and tumour location, BI exhibited the strongest risk of relapse (Hazard ratio: 4.95) serving as an independent predictor of PFI (p = 0.002). Conclusions Our results clearly support the definition of BI as a single criterion of atypia in WHO classification of 2016.

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“…For example, if it can be shown that patients with BI are genuinely at greater risk of perioperative seizures, antiepileptic treatment could be commenced in a timely fashion. Finally, it has been proposed that surgical procedures and the degree of histological sampling may affect the neuropathological diagnosis of BI ( 1 , 36 – 38 ). Preoperative BI awareness should be demonstrated in communication among neurosurgeons and neuropathologists, enhancing neuropathological diagnosis precision by taking strategic measures.…”

Section: Discussionmentioning

confidence: 99%

“…Several operation-related variables, including partial resection and the use of a cavitron ultrasonic surgical aspirator (CUSA), appear to be relevant to the reduced ease of access to CNS tissue. Timme et al demonstrated that microsurgery nuances could affect the existence of CNS parenchyma on meningioma specimens and thus change the efficiency of BI detection ( 38 ). Histopathological sampling methods may also affect the evaluation of BI ( 7 ).…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study

et al. 2021

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BackgroundMeningioma invasion can be preoperatively recognized by radiomics features, which significantly contributes to treatment decision-making. Here, we aimed to evaluate the comparative performance of radiomics signatures derived from varying regions of interests (ROIs) in predicting BI and ascertaining the optimal width of the peritumoral regions needed for accurate analysis.MethodsFive hundred and five patients from Wuhan Union Hospital (internal cohort) and 214 cases from Taihe Hospital (external validation cohort) pathologically diagnosed as meningioma were included in our study. Feature selection was performed from 1,015 radiomics features respectively obtained from nine different ROIs (brain-tumor interface (BTI)2–5mm; whole tumor; the amalgamation of the two regions) on contrast-enhanced T1-weighted imaging using least-absolute shrinkage and selection operator and random forest. Principal component analysis with varimax rotation was employed for feature reduction. Receiver operator curve was utilized for assessing discrimination of the classifier. Furthermore, clinical index was used to detect the predictive power.ResultsModel obtained from BTI4mm ROI has the maximum AUC in the training set (0.891 (0.85, 0.932)), internal validation set (0.851 (0.743, 0.96)), and external validation set (0.881 (0.833, 0.928)) and displayed statistically significant results between nine radiomics models. The most predictive radiomics features are almost entirely generated from GLCM and GLDM statistics. The addition of PEV to radiomics features (BTI4mm) enhanced model discrimination of invasive meningiomas.ConclusionsThe combined model (radiomics classifier with BTI4mm ROI + PEV) had greater diagnostic performance than other models and its clinical application may positively contribute to the management of meningioma patients.

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Brain invasion in meningiomas: does surgical sampling impact specimen characteristics and histology?

Cited by 14 publications

References 13 publications

Meningioma sampling: how much is enough for the accurate grading of atypical meningiomas?

Meningioma sampling: how much is enough for the accurate grading of atypical meningiomas?

The prognostic significance of clinicopathological features in meningiomas: Microscopic brain invasion can predict patient outcome in otherwise benign meningiomas

Diagnosis of Invasive Meningioma Based on Brain-Tumor Interface Radiomics Features on Brain MR Images: A Multicenter Study

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