Imaging and diagnostic advances for intracranial meningiomas

Huang, Raymond Y.; Bi, Wenya Linda; Griffith, Brent; Kaufmann, Timothy J.; Fougère, Christian la; Schmidt, Nils Ole; Tonn, Joerg C.; Vogelbaum, Michael A.; Wen, Patrick Y.; Aldape, Kenneth; Nassiri, Farshad; Zadeh, Gelareh; Dunn, Ian F.

doi:10.1093/neuonc/noy143

Cited by 111 publications

(107 citation statements)

References 135 publications

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“…A preliminary diagnosis of meningioma typically relies on MRI and computed tomography (CT); further diagnosis includes histological classification, grading, and molecular features (1,3,7). However, because the results of CT and MRI are sometimes ambiguous and because biopsy carries potential risks of bleeding, additional approaches (Figure 1) for the diagnosis of meningioma are needed (37).…”

Section: Sstr2-related Diagnosis Approaches For Meningiomamentioning

confidence: 99%

Clinical Significance of Somatostatin Receptor (SSTR) 2 in Meningioma

Zhou

Wang

et al. 2020

Front. Oncol.

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Somatostatin receptor (SSTR) 2, widely expressed in meningioma, is a G-proteincoupled receptor and can be activated by somatostatin or its synthetic analogs. SSTR2 is therefore extensively studied as a marker and target for the diagnosis and treatment of meningioma. Accumulating studies have revealed the crucial clinical significance of SSTR2 in meningioma. Summarizing the progress of these studies is urgently needed as it may not only provide novel and better management for patients with meningioma but also indicate the direction of future research. Pertinent literature is reviewed to summarize the recent collective knowledge and understanding of SSTR2's clinical significance in meningioma in this review. SSTR2 offers novel ideas and approaches in the diagnosis, treatment, and prognostic prediction for meningioma, but more and further studies are required.

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Section: Sstr2-related Diagnosis Approaches For Meningiomamentioning

confidence: 99%

Clinical Significance of Somatostatin Receptor (SSTR) 2 in Meningioma

Zhou

Wang

et al. 2020

Front. Oncol.

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“…In the neurosurgical community, it is very well accepted that consistency (also referred to as hardness, rigidity, or stiffness) is a major limitation to achieve an optimal or complete resection of intracranial meningiomas. Therefore, many studies have focused on MR imaging techniques for the presurgical prediction of tumor consistency (reviewed in 7 , 14 ). However, the results of these studies are conflicting and did not provide data about the impact of meningioma consistency on surgical outcome parameters.…”

Section: Discussionmentioning

confidence: 99%

“…Features of tumor texture include consistency (also referred to as hardness, rigidity, or stiffness), adherence, or infiltration of neurovascular structures and tumor vascularization. 7 Meningiomas display a highly variable inter- and intratumoral heterogeneity in this regard. Meningioma with a hard consistency and high adherence to blood vessels, cranial nerves, and/or to the surrounding brain parenchyma represents a significant challenge for surgical removal.…”

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confidence: 99%

Features of tumor texture influence surgery and outcome in intracranial meningioma

Sauvigny

Hoffmann

Schwarz

et al. 2020

Neuro-Oncology Advances

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Objective Texture-related factors such as consistency, vascularity and adherence vary considerably in meningioma and are thought to be linked with surgical resectibility and morbidity. However, data analyzing the true impact of menigioma texture on the surgical management is sparse Methods Patients with intracranial meningioma treated between 08/2014-04/2018 at our institution were prospectively collected for demographics, clinical presentation, histology and surgical treatment with related morbidity and extend of resection (EOR). Tumor characteristics were reported by the surgeon using a standardized questionnaire including items such as tumor consistency, homogeneity, vascularization and adherence to surrounding neurovascular structure and analyzed for their impact surgical outcome parameters using univariate and logistic regression analyses Results Tumor-texture related parameters of 300 patients (72.3% female) with meningioma were analyzed. Meningioma localizations were grouped into 3 different cohorts namely convexity, skull base and posterior. Postoperativ occurrence of a neurological deficit (transient 23.0%; permanent 6.1%) was associated with the duration of surgery (p=0.001), size of tumor (p=0.046), tumor vascularization (p=0.015) and adherence to neurovascular structures (p=0.002). Coherently, the duration of surgery (mean 230.99 ± 101.33 min) was associated with size of tumor (p<0.0001), vascularization (p<0.0001) and adherence (p<0.0001). Similar associations were recapitulated in subgroup analyses of different tumor localizations. Noteworthy, tumor rigidity had no significant impact on time of surgery and neurological outcome Conclusions Our analysis demontrates that tumor texture has an impact on the surgical management of meningioma and provides data that tumor vascularization and adherence are significant factors influencing surgical outcome whereas the influence of tumor consistency has less impact than previously thought

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“…Notably, there are several common radiomic features across different studies regardless of their nonidentical nomenclatures (47). One radiomic feature is the sphericity, evaluating how a tumor is morphologically similar to a sphere (19,36,39), or spherical disproportion, rating the deviation of a lesion's morphology from a sphere of the similar volume (34,40).…”

Section: Predicting Pathological Grade Of Meningiomasmentioning

confidence: 99%

The Current State of Radiomics for Meningiomas: Promises and Challenges

Zhang

Russo

et al. 2020

Front. Oncol.

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Meningiomas are the most common primary tumors of the central nervous system. Given the fact that the majority of meningiomas are benign, the preoperative risk stratification and treatment strategy decision-making highly rely on the conventional subjective radiologic evaluation. However, this traditional diagnostic and treatment modality may not be effective in patients with aggressive-growing tumors or symptomatic patients with potential risk of recurrence after surgical resection or radiotherapy, as this passive "wait and see" strategy could miss the optimal opportunity of intervention. Radiomics, a new rising discipline, translates high-dimensional image information into abundant mathematical data by multiple computational algorithms. It provides an objective and quantitative approach to interpret the imaging data, rather than the subjective and qualitative interpretation from relatively limited human visual observation. In fact, the enormous amount of information generated by radiomics analyses provides radiological to histopathological tumor information, which are visually imperceptible, and offers technological basis to its applications amid diagnosis, treatment, and prognosis. Here, we review the latest advancements of radiomics and its applications in the prediction of the pathological grade, pathological subtype, recurrence possibility, and differential diagnosis of meningiomas, and the potential and challenges in general clinical applications. In this review, we highlight the generalization of shared radiomic features among different studies and compare different performances of popular algorithms. At last, we discuss several possible aspects of challenges and future directions in the development of radiomic applications in meningiomas.

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Imaging and diagnostic advances for intracranial meningiomas

Cited by 111 publications

References 135 publications

Clinical Significance of Somatostatin Receptor (SSTR) 2 in Meningioma

Clinical Significance of Somatostatin Receptor (SSTR) 2 in Meningioma

Features of tumor texture influence surgery and outcome in intracranial meningioma

The Current State of Radiomics for Meningiomas: Promises and Challenges

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