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

Zhang, Jing; Yao, Kuan; Li, Panpan; Liu, Zhenyu; Han, Tao; Zhao, Zhiyong; Cao, Yuntai; Zhang, Guojin; Zhang, Junting; Tian, Jie; Zhang, Junlin

doi:10.1016/j.ebiom.2020.102933

Cited by 77 publications

(64 citation statements)

References 42 publications

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“…[60][61][62][63][64] A few studies have addressed the prognosis of meningiomas. 65,66 Morin et al enrolled 314 patients with meningiomas and reasonably included many cases of higher grades of meningiomas (57% grade I, 35% grade II, and 8% grade III). 67 They demonstrated an accuracy of up to 77% for overall survival using a random forest classifier incorporating both MRTA and non-MRTA features.…”

Section: Texture Analysis In Other Brain Tumorsmentioning

confidence: 99%

Texture Analysis in Brain Tumor MR Imaging

Kunimatsu

Yasaka

Akai

et al. 2022

MRMS

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Texture analysis, as well as its broader category radiomics, describes a variety of techniques for image analysis that quantify the variation in surface intensity or patterns, including some that are imperceptible to the human visual system. Cerebral gliomas have been most rigorously studied in brain tumors using MR-based texture analysis (MRTA) to determine the correlation of various clinical measures with MRTA features. Promising results in cerebral gliomas have been shown in the previous MRTA studies in terms of the correlation with the World Health Organization grades, risk stratification in gliomas, and the differentiation of gliomas from other brain tumors. Multiple MRTA studies in gliomas have repeatedly shown high performance of entropy, a measure of the randomness in image intensity values, of either histogram-or gray-level co-occurrence matrix parameters. Similarly, researchers have applied MRTA to other brain tumors, including meningiomas and pediatric posterior fossa tumors. However, the value of MRTA in the clinical use remains undetermined, probably because previous studies have shown only limited reproducibility of the result in the real world. The low-to-modest generalizability may be attributed to variations in MRTA methods, sampling bias that originates from single-institution studies, and overfitting problems to a limited number of samples. To enhance the reliability and reproducibility of MRTA studies, researchers have realized the importance of standardizing methods in the field of radiomics. Another advancement is the recent development of a comprehensive assessment system to ensure the quality of a radiomics study. These two-way approaches will secure the validity of upcoming MRTA studies. The clinical use of texture analysis in brain MRI will be accelerated by these continuous efforts.

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Section: Texture Analysis In Other Brain Tumorsmentioning

confidence: 99%

Texture Analysis in Brain Tumor MR Imaging

Kunimatsu

Yasaka

Akai

et al. 2022

MRMS

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“…This novel method has the potential to increase molecular knowledge of the tumor in a noninvasive manner, which is beneficial given the tumor's hard-to-access location. Several studies showed a potential role of radiomics in predicting the pathological grade, subtypes, recurrence and brain invasion of meningiomas, [32][33][34][35][36] and could also be helpful for differential diagnosis [37][38][39][40]. For example, a study on 175 meningioma patients, of which 103 were low grade and 72 high grade, showed a strong association between 12 MRI radiographic features and histopathological grade [32].…”

Section: Reviewmentioning

confidence: 99%

“…The results of this study evidenced that four radiomics features (roundness of FLAIR-shape, cluster shades of FLAIR/T1CE gray level, DWI/ADC gray level variability and FLAIR/T1CE gray level energy) have a strong predictive value for higher tumor grades. Furthermore, a multicenter study on 1728 patients with grade I, II and III meningiomas showed that 16 clinicoradiomic features present a high sensitivity for risk prediction of brain invasion in meningioma [ 40 ]. The development of radiomics in meningiomas can be of great relevance in clinical practice, considering the potential role of imaging-derived features in deepening the knowledge on tumor biology and pathology and the potential prognostic or predictive implication.…”

Section: Radiological Featuresmentioning

confidence: 99%

Meningioma: not always a benign tumor. A review of advances in the treatment of meningiomas

et al. 2021

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Meningiomas are the most common primary intracranial tumors. The majority of meningiomas are benign, but they can present different grades of dedifferentiation from grade I to grade III (anaplastic/malignant) that are associated with different outcomes. Radiological surveillance is a valid option for low-grade asymptomatic meningiomas. In other cases, the treatment is usually surgical, aimed at achieving a complete resection. The use of adjuvant radiotherapy is the gold standard for grade III, is debated for grade II and is not generally indicated for radically resected grade I meningiomas. The use of systemic treatments is not standardized. Here we report a review of the literature on the clinical, radiological and molecular characteristics of meningiomas, available treatment strategies and ongoing clinical trials.

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“…More recently, a multicenter study has shown that radiomic features have the potential of preoperatively predicting brain invasion in meningioma (45). They have built a SVM model derived from the T1C and T2 MRI sequences and yielded an AUC of 0.819.…”

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

Cited by 77 publications

References 42 publications

Texture Analysis in Brain Tumor MR Imaging

Texture Analysis in Brain Tumor MR Imaging

Meningioma: not always a benign tumor. A review of advances in the treatment of meningiomas

The Current State of Radiomics for Meningiomas: Promises and Challenges

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