Common Extra-axial Tumors Research Articles

Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization.

Simple SummaryMeningiomas are typically benign, common extra-axial tumors of the central nervous system. Routine clinical assessment by radiologists presents some limitations regarding long-term patient outcome prediction and risk stratification. Given the exponential growth of interest in radiomics and artificial intelligence in medical imaging, numerous studies have evaluated the potential of these tools in the setting of meningioma imaging. These were aimed at the development of reliable and reproducible models based on quantitative data. Although several limitations have yet to be overcome for their routine use in clinical practice, their innovative potential is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.Meningiomas are the most common extra-axial tumors of the central nervous system (CNS). Even though recurrence is uncommon after surgery and most meningiomas are benign, an aggressive behavior may still be exhibited in some cases. Although the diagnosis can be made by radiologists, typically with magnetic resonance imaging, qualitative analysis has some limitations in regard to outcome prediction and risk stratification. The acquisition of this information could help the referring clinician in the decision-making process and selection of the appropriate treatment. Following the increased attention and potential of radiomics and artificial intelligence in the healthcare domain, including oncological imaging, researchers have investigated their use over the years to overcome the current limitations of imaging. The aim of these new tools is the replacement of subjective and, therefore, potentially variable medical image analysis by more objective quantitative data, using computational algorithms. Although radiomics has not yet fully entered clinical practice, its potential for the detection, diagnostic, and prognostic characterization of tumors is evident. In this review, we present a wide-ranging overview of radiomics and artificial intelligence applications in meningioma imaging.

Incidence, Clinicopathological Profile and Location - Based Outcome of Intracranial Meningiomas: 10-Year Institutional Study with Review of Literature

Abstract Intracranial meningiomas are the most common extra-axial tumors, representing 15% of all brain tumors. Arising from the arachnoid cells, and common in middle-aged women, 90% meningiomas are benign. We conducted a 10-year study on 183 cases of intracranial meningiomas and observed a lower and decreasing trend; the mean age was 43.3 years but there was also a significant incidence in young females. Parasagittal/falx (29%), sphenoid ridge, convexity meningiomas and middle cranial fossa locations were more common. Histopathologically, meningothelial meningioma was the most common. Benign (WHO I) tumors were found in above 90%, atypical (WHO II) in 5% cases, and malignant (WHO III) in < 4% patients. Most patients underwent Simpsons Grade I excision (35.6%) with dural reconstruction because of late presentations. Posterior fossa meningiomas were mostly benign, while intraventricular ones were mostly malignant with highest postoperation mortality. Mortality in operated patients was 9.8% but was highest in anterior fossa tumors (12.5%).

BIOM-62 SENSITIVE DETECTION AND DISCRIMINATION OF INTRACRANIAL TUMORS BY BLOOD

Abstract BACKGROUND The diagnosis of intracranial tumors relies on tissue specimens obtained by invasive surgery. Non-invasive diagnostic approaches, particularly for patients with brain tumours, provide an opportunity to avoid surgery and mitigate unnecessary risk to patients. We reasoned that DNA methylation profiles of circulating tumor DNA in blood can be used as a clinically useful biomarker for patients with brain tumors, given the specificity of DNA methylation profiles for cell-of-origin. METHODS We generated methylation profiles on the plasma of 608 patients with cancer (219 intracranial, 388 extracranial) and 60 healthy controls using a cell-free methylated DNA immunoprecipitation combined with deep sequencing (cfMeDIP-seq) approach. Using machine-learning approaches we generated and evaluated models to distinguish brain tumors from extracranial cancers that may metastasize to the brain, as well as additional models to discriminate common brain tumors included in the differential diagnosis of solitary extra-axial and intra-axial tumors. RESULTS We observed high sensitivity and discriminative capacity for our models to distinguish gliomas from other cancerous and healthy patients (AUC=0.99, 95%CI 0.96–1), with similar performance in IDH mutant and wildtype gliomas as well as in lower- and high-grade gliomas. Excluding non-malignant contributors to plasma methylation did not change model performance (AUC=0.982, 95%CI 0.93–1). Models generated to discriminate intracranial tumors from each other also demonstrated high accuracy for common extra-axial tumors (AUCmeningioma=0.89, 95%CI 0.80–0.97; AUChemangiopericytoma=0.95, 95%CI 0.73–1) as well as intra-axial tumors ranging from low-grade indolent glial-neuronal tumors (AUC 0.93, 95%CI 0.80 – 1) to diffuse intra-axial gliomas with distinct molecular composition (AUCIDH-mutant glioma = 0.82, 95%CI 0.66 -0.98; AUCIDH-wildtype-glioma = 0.71, 95%CI 0.53 – 0.9). Plasma cfMeDIP-seq signals originated from corresponding tumor tissue DNA methylation signals (r=0.37, p< 2.2e-16). CONCLUSIONS These results demonstrate the potential for cfMeDIP-seq profiles to not only detect circulating tumor DNA, but to accurately discriminate common intracranial tumors that share cell-of-origin lineages.

Hormone Receptor Status and KI67 Proliferation Index in Meningiomas

Background: Meningiomas are the most common extra-axial tumours of central nervous system arising from arachnoidal cap cells. The biological behaviour of these tumours is unpredictable because of the tendency of these tumours to recur irrespective of the grade. However, WHO grading is the most useful morphological predictor of recurrence. Additional prognostic parameters like Ki67 proliferation index and progesterone receptor status are useful in predicting the risk of recurrence. This study was undertaken to evaluate the spectrum of Ki67 proliferation index in various grades of meningiomas and to correlate these findings with the hormone receptor status. Methods: In this study 225 cases of meningiomas, over 6 yrs period, were studied which included grade 1 and grade 2 meningiomas. The relation between Grade, Ki67 proliferation index and progesterone receptor status was studied. Result: We found a significant positive correlation between grade and Ki67 proliferation index at 0.01 level and significant negative correlation between Grade and progesterone receptor status at 0.05 level. There was also significant negative correlation between progesterone receptor status and Ki67 proliferation index in cases with progesterone receptor staining score of 6 to 12. Many of the grade 1 meningiomas also showed a higher Ki67 proliferation index Conclusion: In this study we conclude that, PR and Ki67 Proliferation index are useful supplements of routine histopathological assessment of meningiomas and can be used as prognostic indicators regarding behavior and response to treatment.

Chapter 8 - Convexity meningioma resection in the modern neurosurgical era

Convexity meningiomas are among the most common extra-axial tumors encountered in neurosurgery. Advances in diagnostic imaging, intraoperative technology, and nonsurgical treatment modalities have changed the face of neurosurgical oncology. In this chapter we describe the modern neurosurgeon's approach to convexity meningiomas in terms of diagnosis, treatment, and follow-up care.

Magnetic Resonance Imaging of Extra-axial Tumor

The first step in making decision of intra-cranial tumors is the location of tumor, whether intra- or extra-axial. After localized the lesion we make differential diagnosis that relevant to the location. Once we made the decision, we make the characterization of the tumors. With MRI it is easier to make this decision compared to CT.Meningiomas constitute the most common extra-axial tumors of the brain. Contrast-enhanced MRI can easily detect the location of the tumor, the full extension of the tumor, sinus invasion and/or thrombosis, vascularity, intra-cranial edema, and intra-osseous extension. WHO grades meningiomas in 3 types which are typical, atypical, and malignant meningioma. With structural MRI, MR Spectroscopy, MR perfusion and some methods we can grade this type.Tumors of neurogenic origin such as schwannomas, neurofibromas, neuromas may be similar in appearance. MRI can help distinguishing these tumors with meningiomas. Another extra-axial lesion located in bone or arachnoid is metastases. Contrast-enhanced T2-FLAIR can easily detect these lesions, but inflammatory lesions may also simulate dural metastase. Other extra-axial tumors are choroid plexus masses, non-neoplastic masses (epidermoids, dermoids, teratomas, lipomas). The location as well as specific appearances on imaging will guide us to a specific diagnosis.

Typical Meningioma with Atypical MR Imaging Features Masquerading Malignancy

Meningiomas are the most common extraaxial tumors of intracranial neoplasms. They are usually benign with characteristic pathologic and imaging features. However, a histologically typical meningioma can have unusual or misleading imaging features that may not be suggestive of meningioma. Unusual imaging features such as large meningeal cysts, ring enhancement, and various metaplastic changes can be particularly misleading. Here we present a fibrous meningioma with atypical magnetic resonance (MR) imaging features in a 76-year-old woman.