Banzato T, Causin F, Della Puppa A, Cester G, Mazzai L, Zotti A (2019) Accuracy of deep learning to differentiate the histopathological grading of meningiomas on MR images: a preliminary study. J Magn Reson Imaging 50(4):1152–1159. https://doi.org/10.1002/JMRI.26723
Article PubMed PubMed Central Google Scholar
Brunasso L, Ferini G, Bonosi L, Costanzo R, Musso S, Benigno UE, Gerardi RM, Giammalva GR, Paolini F, Umana GE, Graziano F, Scalia G, Sturiale CL, Di Bonaventura R, Iacopino DG, Maugeri R (2022) A spotlight on the role of radiomics and machine-learning applications in the management of intracranial meningiomas: a new perspective in neuro-oncology. Rev Life 12(4). https://doi.org/10.3390/LIFE12040586/S1
Burrell RA, McGranahan N, Bartek J, Swanton C (2013) The causes and consequences of genetic heterogeneity in cancer evolution. Nature 501(7467):338–345. https://doi.org/10.1038/NATURE12625
Article PubMed CAS Google Scholar
Carson JL, Spence RK, Poses RM, Bonavita G (1988) Severity of anaemia and operative mortality and morbidity. Lancet (London England) 1(8588):727–729. https://doi.org/10.1016/S0140-6736(88)91536-X
Article PubMed CAS Google Scholar
Chen C, Guo X, Wang J, Guo W, Ma X, Xu J (2019) The diagnostic value of radiomics-based machine learning in predicting the grade of meningiomas using conventional magnetic resonance imaging: a preliminary study. Front Oncol. https://doi.org/10.3389/FONC.2019.01338
Article PubMed PubMed Central Google Scholar
Duan C, Li N, Liu X, Cui J, Wang G, Xu W (2023) Performance comparison of 2D and 3D MRI radiomics features in meningiomas grade prediction: a preliminary study. Front Oncol. https://doi.org/10.3389/FONC.2023.1157379
Article PubMed PubMed Central Google Scholar
Duke M, Abelmann WH (1969) The hemodynamic response to chronic anemia. Circulation 39(4):503–515. https://doi.org/10.1161/01.CIR.39.4.503
Article PubMed CAS Google Scholar
Gu H, Zhang X, di Russo P, Zhao X, Xu T (2020) The current state of radiomics for meningiomas: promises and challenges. Front Oncol. https://doi.org/10.3389/FONC.2020.567736
Article PubMed PubMed Central Google Scholar
Hsu SY, Huang YH (2016) Characterization and prognostic implications of significant blood loss during intracranial meningiomas surgery. Transl Cancer Res 5(6):797–804. https://doi.org/10.21037/TCR.2016.11.72
Izaks GJ, Westendorp RGJ, Knook DL (1999) The definition of anemia in older persons. JAMA 281(18):1714–1717. https://doi.org/10.1001/JAMA.281.18.1714
Article PubMed CAS Google Scholar
James RF, Kramer DR, Page PS, Gaughen JR, Martin LB, Mack WJ (2016) Strategic and technical considerations for the endovascular embolization of intracranial meningiomas. Neurosurg Clin North Am 27(2):155–166. https://doi.org/10.1016/J.NEC.2015.11.005
Joo L, Park JE, Park SY, Nam SJ, Kim YH, Kim JH, Kim HS (2021) Extensive peritumoral edema and brain-to-tumor interface MRI features enable prediction of brain invasion in meningioma: development and validation. Neurooncol 23(2):324–333. https://doi.org/10.1093/NEUONC/NOAA190
Lichtenbaum R, De Souza AA, Jafar JJ (2006) Intratumoral hydrogen peroxide injection during meningiomas resection. Neurosurgery 59(4 Suppl 2). https://doi.org/10.1227/01.NEU.0000233908.69004.95
Mayercik V, Ma M, Holdsworth S, Heit J, Iv M (2019) Arterial spin-labeling MRI identifies hypervascular meningiomas. AJR Am J Roentgenol 213(5):1124–1128. https://doi.org/10.2214/AJR.18.21026
Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, Cook G (2020a) Introduction to radiomics. J Nucl Med 61(4):488. https://doi.org/10.2967/JNUMED.118.222893
Article PubMed PubMed Central CAS Google Scholar
Mayerhoefer ME, Materka A, Langs G, Häggström I, Szczypiński P, Gibbs P, Cook G (2020b) Introduction to radiomics. J Nucl Med 61(4):488–495. https://doi.org/10.2967/JNUMED.118.222893
Article PubMed PubMed Central CAS Google Scholar
Murphy MC, Huston J, Glaser KJ, Manduca A, Meyer FB, Lanzino G, Morris JM, Felmlee JP, Ehman RL (2012) Preoperative assessment of meningiomas stiffness by magnetic resonance elastography. J Neurosurg 118(3):643. https://doi.org/10.3171/2012.9.JNS12519
Article PubMed PubMed Central Google Scholar
Niu L, Zhou X, Duan C, Zhao J, Sui Q, Liu X, Zhang X (2019) Differentiation researches on the meningiomas subtypes by radiomics from contrast-enhanced magnetic resonance imaging: a preliminary study. World Neurosurg 126:e646–e652. https://doi.org/10.1016/J.WNEU.2019.02.109
Ostrom QT, Gittleman H, Fulop J, Liu M, Blanda R, Kromer C, Wolinsky Y, Kruchko C, Barnholtz-Sloan JS (2015) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2008–2012. Neuro-Oncol 17(Suppl 4):iv1–iv62. https://doi.org/10.1093/NEUONC/NOV189
Article PubMed PubMed Central Google Scholar
Park YW, Oh J, You SC, Han K, Ahn SS, Choi YS, Chang JH, Kim SH, Lee SK (2019a) Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol 29(8):4068–4076. https://doi.org/10.1007/S00330-018-5830-3
Park YW, Oh J, You SC, Han K, Ahn SS, Choi YS, Chang JH, Kim SH, Lee SK (2019b) Radiomics and machine learning may accurately predict the grade and histological subtype in meningiomas using conventional and diffusion tensor imaging. Eur Radiol 29(8):4068–4076. https://doi.org/10.1007/S00330-018-5830-3
Phuttharak W, Boonrod A, Thammaroj J, Kitkhuandee A, Waraasawapati S (2018) Preoperative MRI evaluation of meningiomas consistency: a focus on detailed architectures. Clin Neurol Neurosurg 169:178–184. https://doi.org/10.1016/J.CLINEURO.2018.04.025
Raper DMS, Starke RM, Henderson F, Ding D, Simon S, Evans AJ, Jane JA, Liu KC (2014) Preoperative embolization of intracranial meningiomas: efficacy, technical considerations, and complications. AJNR Am J Neuroradiol 35(9):1798–1804. https://doi.org/10.3174/AJNR.A3919
Article PubMed PubMed Central CAS Google Scholar
Rosen CL, Ammerman JM, Sekhar LN, Bank WO (2002) Outcome analysis of preoperative embolization in cranial base surgery. Acta Neurochir 144(11):1157–1164. https://doi.org/10.1007/S00701-002-0965-Y
Article PubMed CAS Google Scholar
Ugga L, Spadarella G, Pinto L, Cuocolo R, Brunetti A (2022) Meningiomas radiomics: at the nexus of imaging, pathology and biomolecular characterization. Cancers 14(11). https://doi.org/10.3390/CANCERS14112605
Wang C, Li P (2023) Risk factors for intraoperative blood loss in resection of intracranial meningioma: analysis of 530 cases. PLoS ONE. https://doi.org/10.1371/JOURNAL.PONE.0291171
Article PubMed PubMed Central Google Scholar
Watts J, Box G, Galvin A, Brotchie P, Trost N, Sutherland T (2014) Magnetic resonance imaging of meningiomas: a pictorial review. Insights into Imaging 5(1):113–122. https://doi.org/10.1007/S13244-013-0302-4/FIGURES/21
Article PubMed PubMed Central CAS Google Scholar
Whittle IR, Smith C, Navoo P, Collie D (2004) Meningiomas. Lancet. (London England) 363(9420):1535–1543. https://doi.org/10.1016/S0140-6736(04)16153-9
Wu WC, Smith TS, Henderson WG, Eaton CB, Poses RM, Uttley G, Mor V, Sharma SC, Vezeridis M, Khuri SF, Friedmann PD (2010) Operative blood loss, blood transfusion, and 30-day mortality in older patients after major noncardiac surgery. Ann Surg 252(1):11–17. https://doi.org/10.1097/SLA.0B013E3181E3E43F
Wu WC, Trivedi A, Friedmann PD, Henderson WG, Smith TS, Poses RM, Uttley G, Vezeridis M, Eaton CB, Mor V (2012) Association between hospital intraoperative blood transfusion practices for surgical blood loss and hospital surgical mortality rates. Ann Surg 255(4):708–714. https://doi.org/10.1097/SLA.0B013E31824A55B9
Yi X, Wei L, Liu Y, Long Q, Liu W, Fei Z, Liu Y, Yan L, He G, Zhang M, Zhou X (2015) Efficacy of radio frequency thermocoagulation in surgery for giant supratentorial meningiomas: a historical control study. Clin Neurol Neurosurg 130:26–32. https://doi.org/10.1016/J.CLINEURO.2014.12.008
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