Are the clinical manifestations of CT scan and location associated with World Health Organization histopathological grades of meningioma?: A retrospective study

Meningioma grading via diagnostic imaging: A systematic review and meta-analysis

PURPOSE

Meningioma is the most common intracranial tumor, graded on pathology using WHO criteria to predict tumor course and treatment. However, pathological grading via biopsy may not be possible in cases with poor surgical access due to tumor location. Therefore, our systematic review aims to evaluate whether diagnostic imaging features can differentiate high grade (HG) from low grade (LG) meningiomas as an alternative to pathological grading.

METHODS

Three databases were searched for primary studies that either use routine magnetic resonance imaging (MRI) or computed tomography (CT) to assess pathologically WHO-graded meningiomas. Two investigators independently screened and extracted data from included studies.

RESULTS

24 studies met our inclusion criteria with 12 significant (p < 0.05) CT and MRI features identified for differentiating HG from LG meningiomas. Cystic changes in the tumor had the highest specificity (93.4%) and irregular tumor-brain interface had the highest positive predictive value (65.0%). Mass effect had the highest sensitivity (81.0%) and negative predictive value (90.7%) of all imaging features. Imaging feature with the highest accuracy for identifying HG disease was irregular tumor-brain interface (79.7%). Irregular tumor-brain interface and heterogenous tumor enhancement had the highest AUC values of 0.788 and 0.703, respectively.

CONCLUSION

Our systematic review highlight imaging features that can help differentiate HG from LG meningiomas.

AhR and HIF-1α Signaling Pathways in Benign Meningioma under Hypoxia

The role of hypoxia in benign meningiomas is less clear than that in the malignant meningiomas. Hypoxia-induced transcription factor 1 subunit alpha (HIF-1α) and its downstream signaling pathways play a central role in the mechanism of hypoxia. HIF-1α forms a complex with the aryl hydrocarbon receptor nuclear translocator (ARNT) protein and can compete for ARNT with aryl hydrocarbon receptor (AhR). In this work, the status of HIF-1α- and AhR-dependent signaling pathways was investigated in World Health Organization (WHO) grade 1 meningioma and patient-derived tumor primary cell culture under hypoxic conditions. mRNA levels of HIF-1α, AhR, and of their target genes as well as of ARNT and nuclear receptor coactivator NCOA2 were determined in tumor tissues from patients in whom the tumor was promptly removed either with or without prior endovascular embolization. Using the patient-derived nonembolized tumor primary cell culture, the effects of a hypoxia mimetic cobalt chloride (CoCl₂) and an activator of the AhR signaling pathway benzo(α)pyrene (B[a]P) on mRNA levels of HIF-1α, AhR, and their target genes were investigated. Our findings show active functioning of AhR signaling in meningioma tissue of patients with tumor embolization and crosstalk between HIF-1α and AhR signaling in meningeal cells under hypoxia.

Principal component analysis of texture features for grading of meningioma: not effective from the peritumoral area but effective from the tumor area

PURPOSE

To investigate whether texture features from tumor and peritumoral areas based on sequence combinations can differentiate between low- and non-low-grade meningiomas.

METHODS

Consecutive patients diagnosed with meningioma by surgery (77 low-grade and 28 non-low-grade meningiomas) underwent preoperative magnetic resonance imaging including T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI). Manual segmentation of the tumor area was performed to extract texture features. Segmentation of the peritumoral area was performed for peritumoral high-signal intensity (PHSI) on T2WI. Principal component analysis was performed to fuse the texture features to principal components (PCs), and PCs of each sequence of the tumor and peritumoral areas were compared between low- and non-low-grade meningiomas. Only PCs with statistical significance were used for the model construction using a support vector machine algorithm. k-fold cross-validation with receiver operating characteristic curve analysis was used to evaluate diagnostic performance.

RESULTS

Two, one, and three PCs of T1WI, apparent diffusion coefficient (ADC), and CE-T1WI, respectively, for the tumor area, were significantly different between low- and non-low-grade meningiomas, while PCs of T2WI for the tumor area and PCs for the peritumoral area were not. No significant differences were observed in PHSI. Among models of sequence combination, the model with PCs of ADC and CE-T1WI for the tumor area showed the highest area under the curve (0.84).

CONCLUSION

The model with PCs of ADC and CE-T1WI for the tumor area showed the highest diagnostic performance for differentiating between low- and non-low-grade meningiomas. Neither PHSI nor PCs in the peritumoral area showed added value.