Differentiation Researches on the Meningioma Subtypes by Radiomics from Contrast-Enhanced Magnetic Resonance Imaging: A Preliminary Study

Niu, Lei; Zhou, Xiaoming; Duan, Chongfeng; Zhao, Jian; Qinglan, Sui; Liu, Xuejun; Zhang, Xuexi

doi:10.1016/j.wneu.2019.02.109

Cited by 36 publications

(29 citation statements)

References 30 publications

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“…In the latest 2016 edition of the World Health Organization (WHO) classification of central nervous system (CNS) tumours [3] , microscopic examination of brain invasion was added as an independent grading criterion for the diagnosis of WHO grade II atypical meningioma. Brain invasion is getting the highest clinical attention [4] , and the main reasons are as follows: firstly, microsurgical resection is the most widely used treatment for the vast majority of meningiomas [5] . The choice of surgical technique is closely related to brain invasion, such as application of intraoperative navigation, expansion of surgical excision range, etc.…”

Section: Introductionmentioning

confidence: 99%

“…Secondly, brain tumours now explicitly list brain invasion as a criterion of atypia [3] . Grading of meningiomas only depends on histopathological criteria, and higher grades are associated with worse prognosis, including higher rates of tumour recurrence and worse survival [6] , but brain invasion may not be detected by histopathology due to a lack of brain tissue samples [ 5 , 6 ]. The authors reported 85% of samples as ‘unassessable’ pathologically [7] .…”

Section: Introductionmentioning

confidence: 99%

“…It is also a non-invasive way to provide a quantitative method for the evaluation of tumour heterogeneity. Recent studies have shown several applications of radiomics in meningiomas, such as different diagnoses, prediction of the grade and histological subtype, and prediction recurrence-free survival in meningioma [ 5 , [15] , [16] , [17] ]. Moreover, a recent study showed that radiomics might predict preoperative cavernous sinus invasion by pituitary adenomas [18] .…”

Section: Introductionmentioning

confidence: 99%

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A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

Zhang

Yao

et al. 2020

EBioMedicine

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Background Prediction of brain invasion pre-operatively rather than postoperatively would contribute to the selection of surgical techniques, predicting meningioma grading and prognosis. Here, we aimed to predict the risk of brain invasion in meningioma pre-operatively using a nomogram by incorporating radiomic and clinical features. Methods In this case-control study, 1728 patients from Beijing Tiantan Hospital (training cohort: n = 1070) and Lanzhou University Second Hospital (external validation cohort: n = 658) were diagnosed with meningiomas by histopathology. Radiomic features were extracted from the T1-weighted post-contrast and T2-weighted magnetic resonance imaging. The least absolute shrinkage and selection operator was used to select the most informative features of different modalities. The support vector machine algorithm was used to predict the risk of brain invasion. Furthermore, a nomogram was constructed by incorporating radiomics signature and clinical risk factors, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Findings Sixteen features were significantly correlated with brain invasion. The clinicoradiomic model derived from the fusing MRI sequences and sex resulted in the best discrimination ability for risk prediction of brain invasion, with areas under the curves (AUCs) of 0•857 (95% CI, 0•831–0•887) and 0•819 (95% CI, 0•775–0•863) and sensitivities of 72•8% and 90•1% in the training and validation cohorts, respectively. Interpretation Our clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma, and can be applied in patients with meningiomas. Funding This work was supported by the (81772006, 81922040); the CAS (grant numbers 2019136); special fund project for doctoral training program of Second Hospital (grant numbers YJS-BD-33).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

Zhang

Yao

et al. 2020

EBioMedicine

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“…Recent studies on the use of radiomic analysis for meningiomas suggest that they can be used to estimate WHO grades [ 12 , 13 , 14 ], with close to 90% accuracy [ 15 ]. Radiomics could also be applied for the preoperative differentiation of meningioma subtypes [ 16 ]. Furthermore, the combination of both radiomic and visual identification of macroscopic (or semantic) characteristics has allowed for meningioma classifications based on WHO grading [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features

Kalasauskas

Kronfeld

Renovanz

et al. 2020

Cancers

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Up to 60% of atypical meningiomas (World Health Organization (WHO) grade II) reoccur within 5 years after resection. However, no clear radiological criteria exist to identify tumors with higher risk of relapse. In this study, we aimed to assess the association of certain radiomic and semantic features of atypical meningiomas in MRI with tumor recurrence. We identified patients operated on primary atypical meningiomas in our department from 2007 to 2017. An analysis of 13 quantitatively defined radiomic and 11 qualitatively defined semantic criteria was performed based on preoperative MRI scans. Imaging characteristics were assessed along with clinical and survival data. The analysis included 76 patients (59% women, mean age 59 years). Complete tumor resection was achieved in 65 (86%) cases, and tumor relapse occurred in 17 (22%) cases. Mean follow-up time was 41.6 (range 3–168) months. Cystic component was significantly associated with tumor recurrence (odds ratio (OR) 21.7, 95% confidence interval (CI) 3.8–124.5) and shorter progression-free survival (33.2 vs. 80.7 months, p < 0.001), whereas radiomic characteristics had no predictive value in univariate analysis. However, multivariate analysis demonstrated significant predictive value of high cluster prominence (hazard ratio (HR) 5.89 (1.03–33.73) and cystic component (HR 20.21 (2.46–166.02)) for tumor recurrence. The combination of radiomic and semantic features might be an effective tool for identifying patients with high-risk atypical meningiomas. The presence of a cystic component in these tumors is associated with a high risk of tumor recurrence.

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“…Radiomics analysis has also shown to be predictive in other aspects of meningiomas, like subtypes identification, differential diagnosis, recurrence prediction and brain invasion. Niu et al have extracted 385 radiomic features from the T1C images of 241 patients and built a Fisher discriminant analysis model which successfully distinguished subtypes of meningothelial, fibrous, and transitional meningiomas yielding a perfect accuracy of 100% with an as high accuracy of the validation model as 94.2% (41). Another study also reported that there were significant differences in various texture features derived from the T1C, ADC, and FA parameters between the fibroblastic and nonfibroblastic pathological subtypes, without establishing a radiomic model (40).…”

Section: Other Applications In 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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Differentiation Researches on the Meningioma Subtypes by Radiomics from Contrast-Enhanced Magnetic Resonance Imaging: A Preliminary Study

Cited by 36 publications

References 30 publications

A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study

Identification of High-Risk Atypical Meningiomas According to Semantic and Radiomic Features

The Current State of Radiomics for Meningiomas: Promises and Challenges

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