Meningiomas are the most common primary benign intracranial brain tumors (
1
). While the majority of meningiomas are classified World Health Organization (WHO)
grade 1 tumors, a minority is classified as grade 2 or 3 lesions, based on features
of cellular atypia and invasive growth (
2
). Surgical resection remains the treatment of choice for progressive, space-occupying
and/ or symptomatic lesions, especially in favourable locations such as convexity
meningiomas (
1
). While the aim of surgery is gross total resection (GTR), the extent of resection
depends on factors such as tumor location, size and involvement of critical tissue
(
1
). Gross total resection and subtotal resection (STR) are defined according to the
Simpson classification: Simpson grade I – III constitute gross total resection and
Simpson grades IV – V form the group of subtotal resection. (
3
,
4
)Keywords
Abbreviations:
AI (Artificial Intelligence), CNN (Convolutional Neural Network), CNS (Central Nervous System), DL (Deep Learning), FU (Follow up), GTR (Gross Total Resection), IRB (Institutional Review Board), ML (Machine Learning), MRI (Magnetic Resonance Imaging), NPV (Negative Predictive Value), PPV (Positive Predictive Value), STR (Subtotal Resection), WHO (World Health Organization)To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Academic RadiologyAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- EANO guideline on the diagnosis and management of meningiomas.Neuro Oncol. 2021; 23: 1821-1834https://doi.org/10.1093/neuonc/noab150
- The 2021 WHO Classification of Tumors of the Central Nervous System: a summary.Neuro Oncol. 2021; 23: 1231-1251https://doi.org/10.1093/neuonc/noab106
- The ROAM/EORTC-1308 trial: radiation vs. observation following surgical resection of Atypical Meningioma: study protocol for a randomised controlled trial.Trials. 2015; 16: 519https://doi.org/10.1186/s13063-015-1040-3
- The recurrence of intracranial meningiomas after surgical treatment.J Neurol Neurosurg Psychiatry. 1957; 20: 22-39https://doi.org/10.1136/jnnp.20.1.22
- The relevance of simpson grade resections in modern neurosurgical treatment of World Health Organization Grade1, 2, and 3Meningiomas.World Neurosurg. 2018; 109: e588-e593https://doi.org/10.1016/j.wneu.2017.10.028
- Gross total resection and adjuvant radiotherapy most significant predictors of improved survival in patients with atypical meningioma.Cancer. 2018; 124: 734-742https://doi.org/10.1002/cncr.31088
- The applicability of established clinical and histopathological risk factors for tumor recurrence during long-term postoperative care in meningioma patients.Neurosurg Rev. 2021; https://doi.org/10.1007/s10143-021-01697-w
- Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.Nat Commun. 2014; 5: 4006https://doi.org/10.1038/ncomms5006
- Radiographic prediction of meningioma grade by semantic and radiomic features.PLoS One. 2017; 12e0187908https://doi.org/10.1371/journal.pone.0187908
- Machine learning-based radiomics analysis in predicting the meningioma grade using multiparametric MRI.Eur J Radiol. 2020; 131109251.https://doi.org/10.1016/j.ejrad.2020.109251
- Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging.Eur Radiol. 2019; 29: 4068-4076https://doi.org/10.1007/s00330-018-5830-3
- A deep learning radiomics model for preoperative grading in meningioma.Eur J Radiol. 2019; 116: 128-134https://doi.org/10.1016/j.ejrad.2019.04.022
- Radiomics approach for prediction of recurrence in skull base meningiomas.Neuroradiology. 2019; 61: 1355-1364https://doi.org/10.1007/s00234-019-02259-0
- Fully automated detection and segmentation of meningiomas using deep learning on routine multiparametric MRI.Eur Radiol. 2019; 29: 124-132https://doi.org/10.1007/s00330-018-5595-8
- Brain tumor classification using deep CNN features via transfer learning.Comput Biol Med. 2019; 111103345.https://doi.org/10.1016/j.compbiomed.2019.103345
- Brain tumor classification for MR images using transfer learning and fine-tuning.Comput Med Imaging Graph. 2019; 75: 34-46https://doi.org/10.1016/j.compmedimag.2019.05.001
- Automated meningioma segmentation in multiparametric MRI: comparable effectiveness of a deep learning model and manual segmentation.Clin Neuroradiol. 2021; 31: 357-366https://doi.org/10.1007/s00062-020-00884-4
- Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: a preliminary study.J Magn Reson Imaging. 2019; 50: 1152-1159https://doi.org/10.1002/jmri.26723
- Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging.Neurosurg Focus. 2018; 45: E4https://doi.org/10.3171/2018.8.FOCUS18191
- Extensive peritumoral edema and brain-to-tumor interface MR imaging features enable prediction of brain invasion in meningioma: development and validation.Neuro Oncol. 2020; https://doi.org/10.1093/neuonc/noaa190
- Presurgical detection of brain invasion status in meningiomas based on first-order histogram based texture analysis of contrast enhanced imaging.Clin Neurol Neurosurg. 2020; 198106205.https://doi.org/10.1016/j.clineuro.2020.106205
- Differentiation researches on the meningioma subtypes by radiomics from contrast-enhanced magnetic resonance imaging: apreliminary study.World Neurosurg. 2019; 126: e646-e652https://doi.org/10.1016/j.wneu.2019.02.109
- The potential value of preoperative MRI texture and shape analysis in grading meningiomas: apreliminary investigation.Transl Oncol. 2017; 10: 570-577https://doi.org/10.1016/j.tranon.2017.04.006
- A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: amulticentre study.EBioMedicine. 2020; 58102933.https://doi.org/10.1016/j.ebiom.2020.102933
- Meningiomas: knowledge base, treatment outcomes, and uncertainties. A RANO review.J Neurosurg. 2015; 122: 4-23https://doi.org/10.3171/2014.7.JNS131644
- Primary vs. postoperative gamma knife radiosurgery for intracranial benign meningiomas: a matched cohort retrospective study.BMC Cancer. 2022; 22: 206https://doi.org/10.1186/s12885-022-09321-w
- Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study.Neurosurg Focus. 2018; 45: E12https://doi.org/10.3171/2018.8.FOCUS18243
- Assessing preoperative risk of STR in skull meningiomas using MR radiomics and machine learning.Sci Rep. 2022; 12: 14043https://doi.org/10.1038/s41598-022-18458-4
Article Info
Publication History
Published online: October 30, 2022
Accepted:
October 3,
2022
Received in revised form:
September 20,
2022
Received:
August 4,
2022
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2022 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.
