====== Meningioma Radiomics ======
The [[quality]] of [[radiomics]] studies for [[meningioma]] is insufficient. Acknowledgment of [[radiomics quality score]] (RQS), (TRIPOD) [[Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis]] [[checklist]], and [[The Image Biomarker Standardization Initiative]] (IBSI) [[guideline]]s may improve the quality of meningioma radiomics studies and enable their clinical application
((Won SY, Park YW, Ahn SS, Moon JH, Kim EH, Kang SG, Chang JH, Kim SH, Lee SK. Quality assessment of meningioma radiomics studies: Bridging the gap between exploratory research and clinical applications. Eur J Radiol. 2021 May;138:109673. doi: 10.1016/j.ejrad.2021.109673. Epub 2021 Mar 20. PMID: 33774441.)).
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For Park et al. [[Meningioma]] [[Radiomics]] significantly contribute added value in predicting recurrence when integrated with the clinicopathological features in patients with [[World health organization grade 2 meningioma]]. Furthermore, the combined model can be applied to identify high-risk patients who require adjuvant radiotherapy
((Park CJ, Choi SH, Eom J, Byun HK, Ahn SS, Chang JH, Kim SH, Lee SK, Park YW, Yoon HI. An interpretable [[radiomics]] model to select patients for radiotherapy after surgery for WHO grade 2 meningiomas. Radiat Oncol. 2022 Aug 22;17(1):147. doi: 10.1186/s13014-022-02090-7. PMID: 35996160.)).
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In 2022 Ugga et al. presented a wide-ranging overview of radiomics and [[artificial intelligence]] applications in meningioma imaging
((Ugga L, Spadarella G, Pinto L, Cuocolo R, Brunetti A. Meningioma Radiomics: At the Nexus of Imaging, Pathology and Biomolecular Characterization. Cancers (Basel). 2022 May 25;14(11):2605. doi: 10.3390/cancers14112605. PMID: 35681585; PMCID: PMC9179263.)).
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Gu et al. reviewed in 2020 the latest advancements of radiomics and its applications in the prediction of the pathological [[meningioma grade]], [[meningioma histological subtype]], [[meningioma recurrence]] possibility, and [[meningioma differential diagnosis]], and the potential and challenges in general clinical applications. In this review, they highlighted the generalization of shared radiomic features among different studies and compare different performances of popular [[algorithm]]s
((Gu H, Zhang X, di Russo P, Zhao X, Xu T. The Current State of Radiomics for Meningiomas: Promises and Challenges. Front Oncol. 2020 Oct 27;10:567736. doi: 10.3389/fonc.2020.567736. PMID: 33194649; PMCID: PMC7653049.))
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In 2020 a clinicoradiomic model showed good performance and high sensitivity for risk prediction of brain invasion in meningioma
((Zhang J, Yao K, Liu P, Liu Z, Han T, Zhao Z, Cao Y, Zhang G, Zhang J, Tian J, Zhou J. A radiomics model for preoperative prediction of brain invasion in meningioma non-invasively based on MRI: A multicentre study. EBioMedicine. 2020 Aug;58:102933. doi: 10.1016/j.ebiom.2020.102933. Epub 2020 Jul 30. PMID: 32739863; PMCID: PMC7393568.)).