Prevalence of incidental meningiomas and gliomas on MRI: a meta-analysis and meta-regression analysis

The outcomes measured and reported in observational studies of incidental and untreated intracranial meningioma: A systematic review

Background

The clinical management of patients with incidental intracranial meningioma varies markedly and is often based on clinician choice and observational data. Heterogeneous outcome measurement has likely hampered knowledge progress by preventing comparative analysis of similar cohorts of patients. This systematic review aimed to summarize the outcomes measured and reported in observational studies.

Methods

A systematic literature search was performed to identify published full texts describing active monitoring of adult cohorts with incidental and untreated intracranial meningioma (PubMed, EMBASE, MEDLINE, and CINAHL via EBSCO, completed January 24, 2022). Reported outcomes were extracted verbatim, along with an associated definition and method of measurement if provided. Verbatim outcomes were de-duplicated and the resulting unique outcomes were grouped under standardized outcome terms. These were classified using the taxonomy proposed by the "Core Outcome Measures in Effectiveness Trials" (COMET) initiative.

Results

Thirty-three published articles and 1 ongoing study were included describing 32 unique studies: study designs were retrospective n = 27 and prospective n = 5. In total, 268 verbatim outcomes were reported, of which 77 were defined. Following de-duplication, 178 unique verbatim outcomes remained and were grouped into 53 standardized outcome terms. These were classified using the COMET taxonomy into 9 outcome domains and 3 core areas.

Conclusions

Outcome measurement across observational studies of incidental and untreated intracranial meningioma is heterogeneous. The standardized outcome terms identified will be prioritized through an eDelphi survey and consensus meeting of key stakeholders (including patients), in order to develop a Core Outcome Set for use in future observational studies.

Incidence, management, and outcome of incidental meningioma: what has happened in 10 years?

PURPOSE

The aim of this study was to study the use of brain scanning, and the subsequent findings of presumed incidental meningioma in two time periods, and to study differences in follow-up, treatment, and outcome.

METHODS

Records of all performed CT and MRI of the brain during two time periods were retrospectively reviewed in search of patients with presumed incidental meningioma. These patients were further analyzed using medical health records, with the purpose to study clinical handling and outcome during a 3 year follow up.

RESULTS

An identical number of unique patients underwent brain imaging during the two time periods (n = 22 259 vs. 22 013). In 2018-2019, 25% more incidental meningiomas were diagnosed compared to 2008-2009 (n = 161 vs. 129, p = 0.052). MRI was used more often in 2018-2019 (26.1 vs. 12.4%, p = 0.004), and the use of contrast enhancement, irrespective of modality, also increased (26.8 vs. 12.2%, p < 0.001). In the most recent cohort, patients were older (median 79 years vs. 73 years, p = 0.03). Indications showed a significant increase of cancer without known metastases among scanned patients. 29.5 and 35.4% of patients in the cohorts were deceased 3 years after diagnosis for causes unrelated to their meningioma.

CONCLUSIONS

Despite the same number of unique patients undergoing brain scans in the time periods, there was a trend towards more patients diagnosed with an incidental asymptomatic meningioma in the more recent years. This difference may be attributed to more contrast enhanced scans and more scans among the elderly but needs to be further studied. Patients in the cohort from 2018 to 2019 more often had non-metastatic cancer, with their cause of scan screening for metastases. There was no significant difference in management decision at diagnosis, but within 3 years of follow up significantly more patients in the latter cohort had been re-scanned. Almost a third of all patients were deceased within 3 years after diagnosis, due to causes other than their meningioma.

Incidental findings on brain magnetic resonance imaging (MRI) in adults: a review of imaging spectrum, clinical significance, and management

Utilization of brain MRI has dramatically increased in recent decades due to rapid advancement in imaging technology and improving accessibility. As a result, radiologists increasingly encounter findings incidentally discovered on brain MRIs which are performed for unrelated indications. Some of these findings are clinically significant, necessitating further investigation or treatment and resulting in increased costs to healthcare systems as well as increased patient anxiety. Moreover, management of these incidental findings poses a significant challenge for referring physicians. Therefore, it is important for interpreting radiologists to know the prevalence, clinical consequences, and appropriate management of these findings. There is a wide spectrum of incidental findings on brain MRI such as asymptomatic brain infarct, age-related white matter changes, microhemorrhages, intracranial tumors, intracranial cystic lesions, and anatomic variants. This article provides a narrative review of important incidental findings encountered on brain MRI in adults with a focus on prevalence, clinical implications, and recommendations on management of these findings based on current available data.

Growth risk classification and typical growth speed of convexity, parasagittal, and falx meningiomas: a retrospective cohort study

OBJECTIVE

Meningiomas are the most common primary intracranial tumors, and their clinical and biological characteristics vary by location. Convexity, parasagittal, and falx meningiomas account for approximately 50%-65% of intracranial meningiomas. Focusing only on these locations, the aim of this study was to determine the typical speed of tumor growth, to assess the growth risk, and to show the possible tumor volume that many lesions can reach after 5 years.

METHODS

Patients with radiologically suspected convexity, parasagittal, or falx meningiomas at the authors' institution were studied retrospectively. The relative growth rate (RGR) and annual volume change (AVC) were calculated from MRI at more than 3-month intervals. Based on sex, age, and signal intensity on T2-weighted MRI, the cases were classified into three groups: extremely high-growth, high-growth, and low-growth groups.

RESULTS

The data of 313 cases were analyzed. The median RGR and AVC for this entire cohort were 6.1% (interquartile range [IQR] 2.4%-16.0%) and 0.20 (IQR 0.04-1.18) cm3/year, respectively. There were significant differences in sex (p = 0.018) and T2-weighted MRI signal intensity (p < 0.001) for RGR, and T2-weighted MRI signal intensity (p < 0.001), tumor location (p = 0.025), and initial tumor volume (p < 0.001) for AVC. The median RGR and AVC were 17.5% (IQR 8.3%-44.1%) and 1.05 (IQR 0.18-3.53) cm3/year, 8.2% (IQR 2.9%-18.6%) and 0.33 (IQR 0.06-1.66) cm3/year, and 3.4% (IQR 1.2%-5.8%) and 0.04 (IQR 0.02-0.21) cm3/year for the extremely high-growth, high-growth, and low-growth groups, respectively, with a significant difference among the groups (p < 0.001). A 2.24-times, or 5.24 cm3, increase in tumor volume over 5 years was typical in the extremely high-growth group, whereas the low-growth group showed little change in tumor volume even over a 5-year follow-up period.

CONCLUSIONS

For the first time, the typical speed of tumor growth was calculated, focusing only on patients with convexity, parasagittal, and falx meningiomas. In addition, the possible tumor volume that many lesions in these locations can reach after 5 years was shown based on objective indicators. These results may allow clinicians to easily detect lesions that require frequent follow-up or early treatment by determining whether they deviate from the typical range of the growth rate, similar to a growth chart for children.

Prevalence of Brain Incidental Lesions Detected by 68Ga-DOTA Peptides PET/CT

Background and Objectives: ⁶⁸Ga-DOTA peptides positron emission tomography/computed tomography (PET/CT) is usually applied for the study of neuroendocrine tumours, but other tumours such as meningioma may also have an increased radiopharmaceutical uptake. The aim of this retrospective study was to establish the prevalence and the meaning of brain incidental uptake among patients who performed ⁶⁸Ga-DOTA peptides PET/CT for other reasons. Materials and Methods: Overall, 510 ⁶⁸Ga-DOTA peptides PET/CT scans performed between January 2018 and February 2022 from 430 patients were reviewed for the analysis of incidental brain radiopharmaceutical uptake. All brain incidentalomas were compared with brain magnetic resonance imaging (MRI) and/or contrast-enhanced CT performed within an average time interval of ±60 days from PET/CT scan. Results: A total of 48 patients (14%) presented incidental focal intracranial radiotracer uptake. Thirty-eight (11%) of them had a suspected meningioma confirmed by MRI or contrast-enhanced CT imaging features. The remaining 10 had a final diagnosis different from meningioma (5 as brain metastases and 2 as venous anomalies) or were lost during the follow-up without performing MRI (n = 3). The average maximal standardized uptake value (SUVmax) of the suspected meningioma was 16.5 (range 5-33), and the average lesion to brain SUVmax ratio was 351 (range 80-550). Conclusions: Brain incidental uptake from the ⁶⁸Ga-DOTA peptides PET/CT is not so rare, and meningioma is the most frequent cause.

Malignant Progression of Diffuse Low-grade Gliomas: A Systematic Review and Meta-analysis on Incidence and Related Factors

Malignant progression of diffuse low-grade glioma (LGG) is a critical event affecting patient survival; however, the incidence and related factors have been inconsistent in literature. According to the PRISMA guidelines, we systematically reviewed articles from 2009, meta-analyzed the incidence of malignant progression, and clarified factors related to the transformation. Forty-one articles were included in this study (n = 7,122; n, number of patients). We identified two definitions of malignant progression: histologically proven (Htrans) and clinically defined (Ctrans). The malignant progression rate curves of Htrans and Ctrans were almost in parallel when constructed from the results of meta-regression by the mean follow-up time. The true transformation rate was supposed to lie between the two curves, approximately 40% at the 10-year mean follow-up. Risk of malignant progression was evaluated using hazard ratio (HR). Pooled HRs were significantly higher in tumors with a larger pre- and postoperative tumor volume, lower degree of resection, and notable preoperative contrast enhancement on magnetic resonance imaging than in others. Oligodendroglial histology and IDH mutation (IDHm) with 1p/19q codeletion (Codel) also significantly reduced the HRs. Using Kaplan-Meier curves from eight studies with molecular data, we extracted data and calculated the 10-year malignant progression-free survival (10yMPFS). The 10yMPFS in patients with IDHm without Codel was 30.4% (95% confidence interval [95% CI]: 22.2-39.0) in Htrans and 38.3% (95% CI: 32.3-44.3) in Ctrans, and that with IDHm with Codel was 71.7% (95% CI: 61.7-79.5) in Htrans and 62.5% (95% CI: 55.9-68.5) in Ctrans. The effect of adjuvant radiotherapy or chemotherapy could not be determined.

Deep Learning-Based Segmentation of Various Brain Lesions for Radiosurgery

Semantic segmentation of medical images with deep learning models is rapidly being developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset. The dataset consists of 1688 patients with various brain lesions (pituitary tumors, meningioma, schwannoma, brain metastases, arteriovenous malformation, and trigeminal neuralgia), and we divided the dataset into a training set (1557 patients) and test set (131 patients). This study demonstrates the strengths and weaknesses of deep-learning algorithms in a fairly practical scenario. We compared the model performances concerning their sampling method, model architecture, and the choice of loss functions, identifying suitable settings for their applications and shedding light on the possible improvements. Evidence from this study led us to conclude that deep learning could be promising in assisting the segmentation of brain lesions even if the training dataset was of high heterogeneity in lesion types and sizes.