Predicting the grade of meningiomas by clinical-radiological features: A comparison of precontrast and postcontrast MRI

T1 Pre- and Post-contrast Delta Histogram Parameters in Predicting the Grade of Meningioma and Their Relationship to Ki-67 Proliferation Index

RATIONALE AND OBJECTIVES

To explore the feasibility of delta histogram parameters (including absolute delta histogram parameters (AdHP) and relative delta histogram parameters (RdHP)) in predicting the grade of meningioma and to further investigate whether delta histogram parameters correlate with the Ki-67 proliferation index.

METHODS

92 patients with meningioma who underwent MRI examination (including T1-weighted (T1) and contrast-enhanced T1-weighted images (T1C)) were enrolled in this retrospective study. A total of 46 low-grade cases formed the low-grade group (grade 1, LGM), and a total of 46 high-grade cases formed the high-grade group (38 grade 2, 8 grade 3, HGM). Histogram parameters (HP) of T1 and T1C were extracted. Subsequently, morphological MRI features, AdHP (AdHP=T1CHP-T1HP), and RdHP (RdHP=(T1CHP-T1HP)/T1HP) were recorded and compared, respectively. Binary logistic regression analysis was used to obtain combined performance of the significant parameters. Diagnostic performance was identified by ROC. Spearman's correlation coefficients were taken to assess the relationship between delta histogram parameters and the Ki-67 proliferation index.

RESULTS

In morphological MRI features, HGM is more prone to lobulation and necrosis/cystic changes (all p < 0.05). In delta histogram parameters, HGM exhibits higher mean, Perc.01, Perc.25, Perc.50, Perc.75, Perc.99, SD, and variance of AdHP, maximum, mean, Perc.25, Perc.50, Perc.75, and Perc.99 of RdHP, compared to LGM (all p < 0.00357). The optimal predictive performance was obtained by combining morphological MRI features and delta histogram parameters with an AUC of 0.945. Significant correlations were observed between significant delta histogram parameters and the Ki-67 proliferation index (all p < 0.05).

CONCLUSION

Delta histogram parameter is a promising potential biomarker, which may be helpful in noninvasive predicting the grade and proliferative activity of meningioma.

Brain Tumor Imaging without Gadolinium-based Contrast Agents: Feasible or Fantasy?

Gadolinium-based contrast agents (GBCAs) form the cornerstone of current primary brain tumor MRI protocols at all stages of the patient journey. Though an imperfect measure of tumor grade, GBCAs are repeatedly used for diagnosis and monitoring. In practice, however, radiologists will encounter situations where GBCA injection is not needed or of doubtful benefit. Reducing GBCA administration could improve the patient burden of (repeated) imaging (especially in vulnerable patient groups, such as children), minimize risks of putative side effects, and benefit costs, logistics, and the environmental footprint. On the basis of the current literature, imaging strategies to reduce GBCA exposure for pediatric and adult patients with primary brain tumors will be reviewed. Early postoperative MRI and fixed-interval imaging of gliomas are examples of GBCA exposure with uncertain survival benefits. Half-dose GBCAs for gliomas and T2-weighted imaging alone for meningiomas are among options to reduce GBCA use. While most imaging guidelines recommend using GBCAs at all stages of diagnosis and treatment, non-contrast-enhanced sequences, such as the arterial spin labeling, have shown a great potential. Artificial intelligence methods to generate synthetic postcontrast images from decreased-dose or non-GBCA scans have shown promise to replace GBCA-dependent approaches. This review is focused on pediatric and adult gliomas and meningiomas. Special attention is paid to the quality and real-life applicability of the reviewed literature.

Imaging meningioma biology: Machine learning predicts integrated risk score in WHO grade 2/3 meningioma

Background

Meningiomas are the most common primary brain tumors. While most are benign (WHO grade 1) and have a favorable prognosis, up to one-fourth are classified as higher-grade, falling into WHO grade 2 or 3 categories. Recently, an integrated risk score (IRS) pertaining to tumor biology was developed and its prognostic relevance was validated in a large, multicenter study. We hypothesized imaging data to be reflective of the IRS. Thus, we assessed the potential of a machine learning classifier for its noninvasive prediction using preoperative magnetic resonance imaging (MRI).

Methods

In total, 160 WHO grade 2 and 3 meningioma patients from 2 university centers were included in this study. All patients underwent surgery with histopathological workup including methylation analysis. Preoperative MRI scans were automatically segmented, and radiomic parameters were extracted. Using a random forest classifier, 3 machine learning classifiers (1 multiclass classifier for IRS and 2 binary classifiers for low-risk and high-risk prediction, respectively) were developed in a training set (120 patients) and independently tested in a hold-out test set (40 patients).

Results

Multiclass IRS classification had a test set area under the curve (AUC) of 0.7, mostly driven by the difficulties in clearly separating medium-risk from high-risk patients. Consequently, a classifier predicting low-risk IRS versus medium-/high-risk showed a very high test accuracy of 90% (AUC 0.88). In particular, "sphericity" was associated with low-risk IRS classification.

Conclusion

The IRS, in particular molecular low-risk, can be predicted from imaging data with high accuracy, making this important prognostic classification accessible by imaging.

Association of quantitative radiomic shape features with functional outcome after surgery for primary sporadic dorsal spinal meningiomas

Objective

Spinal meningiomas (SM) account for 25%-46% of all primary spinal tumors and show an excellent long-term disease control in case of complete resection. Therefore, the postoperative functional outcome is of high importance. To date, reports on dorsally located SM are scarce. Moreover, the impact of radiomics shape features on the functional outcome after surgery for primary dorsal SMs has not been analyzed yet.

Methods

We retrospectively performed an analysis of shape-based radiomic features in 3D slicer software and quantified the tumor volume, surface area, sphericity, surface area to volume ratio and tumor canal ratio. Subsequently, we evaluated the correlation between the radinomic parameters and the postoperative outcome according to Modified Japanese Orthopedic Association (mJOA) score.

Results

Between 2010 and 2022, we identified 24 Females and 2 Males operated on dorsal SMs in our institutional database. The most common SM localization was thoracic spine (n = 20), followed by cervical (n = 4), and lumbar (n = 2). The univariate analysis and the receiver operating characteristic (ROC) analysis showed a strong diagnostic performance of sphericity in the prediction of postoperative functional outcome based on mJOA score (AUC of 0.79, sphericity cut-of value 0.738; p = 0.01). Subsequently, the patients were divided into two groups (mJOA improved vs. mJOA stable/worsened). Patients with improved mJOA score showed significantly higher sphericity (0.79 ± 0.1 vs. 0.70 ± 1.0; p = 0.03). Finally, we divided the cohort based on sphericity (<0.738 and ≥0.738). The group with higher sphericity exhibited a significantly higher positive mJOA difference 3 months postoperatively (16.6 ± 1.4 vs. 14.8 ± 3.7; p = 0.03).

Conclusion

In our study investigating primary sporadic dorsal SMs, we demonstrated that a higher degree of sphericity may be a positive predictor of postoperative improvement, as indicated by the mJOA score.

Domestic Animal Models of Central Nervous System Tumors: Focus on Meningiomas

Intracranial primary tumors (IPTs) are aggressive forms of malignancies that cause high mortality in both humans and domestic animals. Meningiomas are frequent adult IPTs in humans, dogs, and cats, and both benign and malignant forms cause a decrease in life quality and survival. Surgery is the primary therapeutic approach to treat meningiomas, but, in many cases, it is not resolutive. The chemotherapy and targeted therapy used to treat meningiomas also display low efficacy and many side effects. Therefore, it is essential to find novel pharmacological approaches to increase the spectrum of therapeutic options for meningiomas. This review analyzes the similarities between human and domestic animal (dogs and cats) meningiomas by evaluating the molecular and histological characteristics, diagnosis criteria, and treatment options and highlighting possible research areas to identify novel targets and pharmacological approaches, which are useful for the diagnosis and therapy of this neoplasia to be used in human and veterinary medicine.

Impact of Shape Irregularity in Medial Sphenoid Wing Meningiomas on Postoperative Cranial Nerve Functioning, Proliferation, and Progression-Free Survival

Medial sphenoid wing meningiomas (MSWM) are surgically challenging skull base tumors. Irregular tumor shapes are thought to be linked to histopathology. The present study aims to investigate the impact of tumor shape on postoperative functioning, progression-free survival, and neuropathology. This monocentric study included 74 patients who underwent surgery for primary sporadic MSWM (WHO grades 1 and 2) between 2010 and 2021. Furthermore, a systematic review of the literature regarding meningioma shape and the MIB-1 index was performed. Irregular MSWM shapes were identified in 31 patients (41.9%). Multivariable analysis revealed that irregular shape was associated with postoperative cranial nerve deficits (OR: 5.75, 95% CI: 1.15-28.63, p = 0.033). In multivariable Cox regression analysis, irregular MSWM shape was independently associated with tumor progression (HR:8.0, 95% CI: 1.04-62.10, p = 0.046). Multivariable regression analysis showed that irregular shape is independently associated with an increased MIB-1 index (OR: 7.59, 95% CI: 2.04-28.25, p = 0.003). A systematic review of the literature and pooled data analysis, including the present study, showed that irregularly shaped meningiomas had an increase of 1.98 (95% CI: 1.38-2.59, p < 0.001) in the MIB-1 index. Irregular MSWM shape is independently associated with an increased risk of postoperative cranial nerve deficits and a shortened time to tumor progression. Irregular MSWM shapes might be caused by highly proliferative tumors.