Next Article in Journal
Clinicopathological Profiles Associated with Discordant RAS Mutational Status between Liquid and Tissue Biopsies in a Real-World Cohort of Metastatic Colorectal Cancer
Previous Article in Journal
Special Issue “Current Management of Early and Advanced Rectal Cancer”
Previous Article in Special Issue
Matched Paired Primary and Recurrent Meningiomas Points to Cell-Death Program Contributions to Genomic and Epigenomic Instability along Tumor Progression
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Editorial

Update on the Diagnosis and Management of Meningiomas

by
Francesco Maiuri
1,* and
Marialaura Del Basso de Caro
2,*
1
Department of Neurosciences and Reproductive and Odontostomatological Sciences, Neurosurgical Clinic, 80131 Naples, Italy
2
Department of Advanced Biomedical Sciences, Section of Pathology, School of Medicine, University “Federico II” of Naples, 80131 Naples, Italy
*
Authors to whom correspondence should be addressed.
Cancers 2023, 15(14), 3575; https://doi.org/10.3390/cancers15143575
Submission received: 28 June 2023 / Revised: 29 June 2023 / Accepted: 10 July 2023 / Published: 12 July 2023
(This article belongs to the Special Issue Meningiomas: Update on the Diagnosis and Management)
This series of five articles (one original article and four reviews) focuses on the most recent and interesting research studies on the biomolecular and radiological diagnosis and the surgical and medical management of meningiomas.
The WHO Classification of 2021 [1] defines the criteria for inclusion into the three histological grades (I, II, III) and identifies 15 different meningioma subtypes. However, it is well known in clinical practice that meningiomas of the same histological grade, mainly grade I meningiomas, show different biological and clinical behavior; this is the case for grade I meningiomas with aggressive behavior and early recurrence.
Many studies on the molecular aspect of meningiomas have disclosed several molecular alterations [2,3,4], most frequently the loss of the neurofibromin 2 (NF2) gene on chromosome 22 [5,6] and TERT promoter mutations [7,8]. The inclusion of these biomolecular markers in diagnostic assessment may allow the identification of patients with a higher risk of progression or recurrence who require close follow-up imaging studies and more aggressive treatment.
Magnetic resonance imaging (MRI) is the modality of choice for assessing meningiomas. In recent years, radiomics applications have been shown to provide additional information. Radiomics is an emerging technique which collects and analyzes high-dimensional quantitative features derived from an explored region. The radiomics process starts with image acquisition and preprocessing, followed by lesion segmentation; then, the extraction of reliable features can be performed. This technique allows for the correlation of quantifiable images of heterogeneous areas within a lesion with previously established pathological and genotypic characteristics. In this way, it is possible to detect tumor features that cannot be identified through traditional analyses [9,10,11,12,13,14,15]. Although radiomics programs for tumor segmentation and characterization are commercially available, those providing predictive and prognostic assessment are not still available in clinical practice. However, radiomics must be considered a valid technique with significant development possibilities.
Although the surgical management of meningiomas is well codified, several still-controversial aspects remain, such as how to define the extent of tumor resection, how to manage invasive meningiomas (aggressive versus more conservative resection) and when to use the endonasal transbasal approaches for midline skull base meningiomas. The Simpson classification, published in 1957 [16], is still used for grading the extent of meningioma resection. However, it was introduced in the pre-microsurgical era; additionally, the evaluation of residual tumor in grade IV is subjective and does not consider the meningioma location. For these reasons, many studies, mainly those in the last 10 years [17,18,19,20,21,22,23], have questioned the Simpson classification and its value in predicting meningioma recurrence. Of the utmost importance is defining the size of tumor remnants in grade IV resections, from small tissue tumor remnants left on the cortex to significant residual nodules. Thus, the Simpson classification is today insufficient and should be modified according to the data of postoperative MRI studies that assess the gross total versus subtotal resection and quantify the size of the residual tumor. Although complete tumor resection with resection or wide coagulation of the dural attachment is the goal of meningioma surgery, this is sometimes difficult or even impossible for some invasive meningiomas. In these instances, an aggressive resection must be balanced with the risk of injury to the neurological and vascular structures [19]. Thus, when complete resection cannot be achieved, it is advisable that the residual tumor be reduced as much as possible, with the aim of increasing the effect of the postoperative radiosurgery. In fact, it has been shown that Simpson grade II and III resections show similar recurrence-free survival rates compared to grade IV recession with radiotherapy [21].
Skull base meningiomas have always represented a challenge for neurosurgeons due to their proximity to important nervous and vascular structures. They have traditionally been operated on through transcranial approaches, which carry the risk of brain damage. In recent decades, midline skull base meningiomas have been treated with increasing frequency through endoscopic endonasal approaches [24], which prevent brain retraction and obtain safe maximal resection with better clinical outcomes. Tuberculum sellae meningiomas grant access to the tumor attachment and vascular feeders [25,26,27]. Selected cases of olfactory groove meningiomas [28,29,30,31] and clivus meningiomas [32,33] may also be treated through extended endoscopic endonasal approaches, while large-size tumors with a hard consistency and close proximity to the vascular structures are contraindications to transabasal approaches. Postoperative cerebrospinal fluid leak is the main surgical problem. The recurrence rates of endoscopic basal approaches will better be defined in the next few years after studies with a longer follow-up period are conducted.
Medical therapy for more aggressive and recurrent meningiomas is still limited in its efficacy, although different cytotoxic agents have been used. In the last decade, the identification of molecular alterations in more aggressive meningiomas has suggested the need for a biomolecular classification with the aim of defining tailored medical treatments [34,35,36]. However, their efficacy must be confirmed by larger studies in the next few years.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Louis, D.N.; Perry, A.; Wesseling, P.; Brat, D.J.; Cree, I.A.; Figarella-Branger, D.; Hawkins, C.; Ng, H.K.; Pfister, S.M.; Reifenberger, G.; et al. The 2021 WHO Classification of Tumors of the Central Nervous System: A summary. Neuro Oncol. 2021, 23, 1231–1251. [Google Scholar] [CrossRef]
  2. Olar, A.; Goodman, L.D.; Wani, K.M.; Boehling, N.S.; Sharma, D.S.; Mody, R.R.; Gumin, J.; Claus, E.B.; Lang, F.F.; Cloughesy, T.F.; et al. A gene expression signature predicts recurrence-free survival in meningioma. Oncotarget 2018, 9, 16087–16098. [Google Scholar] [CrossRef] [Green Version]
  3. San-Miguel, T.; Navarro, L.; Megías, J.; Muñoz-Hidalgo, L.; Gil-Benso, R.; Roldán, P.; López-Ginés, C.; Cerdá-Nicolás, M. Epigenetic changes underlie the aggressiveness of histologically benign meningiomas that recur. Hum. Pathol. 2019, 84, 105–114. [Google Scholar] [CrossRef] [PubMed]
  4. Berghoff, A.S.; Hielscher, T.; Ricken, G.; Furtner, J.; Schrimpf, D.; Widhalm, G.; Rajky, U.; Marosi, C.; Hainfellner, J.A.; von Deimling, A.; et al. Prognostic impact of genetic alterations and methylation classes in meningioma. Brain Pathol. 2022, 32, e12970. [Google Scholar] [CrossRef] [PubMed]
  5. Mawrin, C.; Perry, A. Pathological classification and molecular genetics of meningiomas. J. Neuro-Oncol. 2010, 99, 379–391. [Google Scholar] [CrossRef]
  6. Petrilli, A.M.; Fernández-Valle, C. Role of Merlin/NF2 inactivation in tumor biology. Oncogene 2016, 35, 537–548. [Google Scholar] [CrossRef] [Green Version]
  7. Sahm, F.; Schrimpf, D.; Olar, A.; Koelsche, C.; Reuss, D.; Bissel, J.; Kratz, A.; Capper, D.; Schefzyk, S.; Hielscher, T.; et al. TERT Promoter Mutations and Risk of Recurrence in Meningioma. J. Natl. Cancer Inst. 2016, 108, djv370. [Google Scholar] [CrossRef] [PubMed]
  8. Mirian, C.; Grell, K.; Juratli, T.A.; Sahm, F.; Spiegl-Kreinecker, S.; Peyre, M.; Biczok, A.; Tonn, J.C.; Goutagny, S.; Bertero, L.; et al. Implementation of TERT promoter mutations improve prognostication of the WHO classification in meningioma. Neuropathol. Appl. Neurobiol. 2022, 48, e12773. [Google Scholar] [CrossRef]
  9. Lambin, P.; Rios-Velazquez, E.; Leijenaar, R.; Carvalho, S.; van Stiphout, R.G.P.M.; Granton, P.; Zegers, C.M.L.; Gillies, R.; Boellard, R.; Dekker, A.; et al. Radiomics: Extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 2012, 48, 441–446. [Google Scholar] [CrossRef] [Green Version]
  10. Chen, C.; Guo, X.; Wang, J.; Guo, W.; Ma, X.; Xu, J. The Diagnostic Value of Radiomics-Based Machine Learning in Predicting the Grade of Meningiomas Using Conventional Magnetic Resonance Imaging: A Preliminary Study. Front. Oncol. 2019, 9, 1338. [Google Scholar] [CrossRef] [Green Version]
  11. Wei, J.; Li, L.; Han, Y.; Gu, D.; Chen, Q.; Wang, J.; Li, R.; Zhan, J.; Tian, J.; Zhou, D. Accurate Preoperative Distinction of Intracranial Hemangiopericytoma from Meningioma Using a Multihabitat and Multisequence-Based Radiomics Diagnostic Technique. Front. Oncol. 2020, 10, 534. [Google Scholar] [CrossRef]
  12. Zhu, H.; Fang, Q.; He, H.; Hu, J.; Jiang, D.; Xu, K. Automatic Prediction of Meningioma Grade Image Based on Data Amplification and Improved Convolutional Neural Network. Comput. Math. Methods Med. 2019, 2019, 7289273. [Google Scholar] [CrossRef]
  13. Zhang, Y.; Shang, L.; Chen, C.; Ma, X.; Ou, X.; Wang, J.; Xia, F.; Xu, J. Machine-Learning Classifiers in Discrimination of Lesions Located in the Anterior Skull Base. Front. Oncol. 2020, 10, 752. [Google Scholar] [CrossRef]
  14. Morin, O.; Chen, W.C.; Nassiri, F.; Susko, M.; Magill, S.T.; Vasudevan, H.N.; Wu, A.; Vallières, M.; Gennatas, E.D.; Valdes, G.; et al. Integrated models incorporating radiologic and radiomic features predict meningioma grade, local failure, and overall survival. Neurooncol. Adv. 2019, 1, vdz011. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Fan, Y.; Huo, X.; Li, X.; Wang, L.; Wu, Z. Non-invasive preoperative imaging differential diagnosis of pineal region tumor: A novel developed and validated multiparametric MRI-based clinicoradiomic model. Radiother. Oncol. 2022, 167, 277–284. [Google Scholar] [CrossRef] [PubMed]
  16. Simpson, D. The recurrence of intracranial meningiomas after surgical treatment. J. Neurol. Neurosurg. Psychiatry 1957, 20, 22–39. [Google Scholar] [CrossRef] [Green Version]
  17. Sughrue, M.E.; Kane, A.J.; Shangari, G.; Rutkowski, M.J.; McDermott, M.W.; Berger, M.S.; Parsa, A.T. The relevance of Simpson Grade I and II resection in modern neurosurgical treatment of World Health Organization Grade I meningiomas. J. Neurosurg. 2010, 113, 1029–1035. [Google Scholar] [CrossRef] [PubMed]
  18. Alvernia, J.E.; Dang, N.D.; Sindou, M.P. Convexity meningiomas: Study of recurrence factors with special emphasis on the cleavage plane in a series of 100 consecutive patients. J. Neurosurg. 2011, 115, 491–498. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Gousias, K.; Schramm, J.; Simon, M. The Simpson grading revisited: Aggressive surgery and its place in modern meningioma management. J. Neurosurg. 2016, 125, 551–560. [Google Scholar] [CrossRef] [Green Version]
  20. Otero-Rodriguez, A.; Tabernero, M.D.; Munoz-Martin, M.C.; Sousa, P.; Orfao, A.; Pascual-Argente, D.; Gonzalez-Tablas, M.; Ruiz-Martin, L. Re-Evaluating Simpson Grade I, II, and III Resections in Neurosurgical Treatment of World Health Organization Grade I Meningiomas. World Neurosurg. 2016, 96, 483–488. [Google Scholar] [CrossRef]
  21. Przybylowski, C.J.; Hendricks, B.K.; Frisoli, F.A.; Zhao, X.; Cavallo, C.; Moreira, L.B.; Gandhi, S.; Sanai, N.; Almefty, K.K.; Lawton, M.T.; et al. Prognostic value of the Simpson grading scale in modern meningioma surgery: Barrow Neurological Institute experience. J. Neurosurg. 2020, 1–9. [Google Scholar] [CrossRef]
  22. Rapoport, B.I.; McDermott, M.W.; Schwartz, T.H. Letter to the Editor. Time to move beyond the Simpson scale in meningioma surgery. J. Neurosurg. 2021, 1–2. [Google Scholar] [CrossRef] [PubMed]
  23. Schwartz, T.H.; McDermott, M.W. The Simpson grade: Abandon the scale but preserve the message. J. Neurosurg. 2020, 1–8. [Google Scholar] [CrossRef]
  24. Cavallo, L.M.; Somma, T.; Solari, D.; Iannuzzo, G.; Frio, F.; Baiano, C.; Cappabianca, P. Endoscopic Endonasal Transsphenoidal Surgery: History and Evolution. World Neurosurg. 2019, 127, 686–694. [Google Scholar] [CrossRef] [PubMed]
  25. de Divitiis, E.; Cavallo, L.M.; Esposito, F.; Stella, L.; Messina, A. Extended endoscopic transsphenoidal approach for tuberculum sellae meningiomas. Neurosurgery 2008, 62, 1192–1201. [Google Scholar] [CrossRef] [PubMed]
  26. Bander, E.D.; Singh, H.; Ogilvie, C.B.; Cusic, R.C.; Pisapia, D.J.; Tsiouris, A.J.; Anand, V.K.; Schwartz, T.H. Endoscopic endonasal versus transcranial approach to tuberculum sellae and planum sphenoidale meningiomas in a similar cohort of patients. J. Neurosurg. 2018, 128, 40–48. [Google Scholar] [CrossRef]
  27. Mallari, R.J.; Thakur, J.D.; Rhee, J.H.; Eisenberg, A.; Krauss, H.; Griffiths, C.; Sivakumar, W.; Barkhoudarian, G.; Kelly, D.F. Endoscopic Endonasal and Supraorbital Removal of Tuberculum Sellae Meningiomas: Anatomic Guides and Operative Nuances for Keyhole Approach Selection. Oper. Neurosurg. 2021, 21, E71–E81. [Google Scholar] [CrossRef]
  28. Fernandez-Miranda, J.C.; Gardner, P.A.; Prevedello, D.M.; Kassam, A.B. Expanded endonasal approach for olfactory groove meningioma. Acta Neurochir. 2009, 151, 287–288; author reply 289–290. [Google Scholar] [CrossRef]
  29. Koutourousiou, M.; Fernandez-Miranda, J.C.; Wang, E.W.; Snyderman, C.H.; Gardner, P.A. Endoscopic endonasal surgery for olfactory groove meningiomas: Outcomes and limitations in 50 patients. Neurosurg. Focus 2014, 37, E8. [Google Scholar] [CrossRef] [Green Version]
  30. Abbassy, M.; Woodard, T.D.; Sindwani, R.; Recinos, P.F. An Overview of Anterior Skull Base Meningiomas and the Endoscopic Endonasal Approach. Otolaryngol. Clin. N. Am. 2016, 49, 141–152. [Google Scholar] [CrossRef]
  31. Schroeder, H.W. Indications and Limitations of the Endoscopic Endonasal Approach for Anterior Cranial Base Meningiomas. World Neurosurg. 2014, 82, S81–S85. [Google Scholar] [CrossRef] [PubMed]
  32. Koutourousiou, M.; Fernandez-Miranda, J.C.; Filho, F.V.-G.; de Almeida, J.R.; Wang, E.W.; Snyderman, C.H.; Gardner, P.A. Outcomes of Endonasal and Lateral Approaches to Petroclival Meningiomas. World Neurosurg. 2017, 99, 500–517. [Google Scholar] [CrossRef] [PubMed]
  33. Freeman, J.L.; Sampath, R.; Quattlebaum, S.C.; Casey, M.A.; Folzenlogen, Z.A.; Ramakrishnan, V.R.; Youssef, A.S. Expanding the endoscopic transpterygoid corridor to the petroclival region: Anatomical study and volumetric comparative analysis. J. Neurosurg. 2018, 128, 1855–1864. [Google Scholar] [CrossRef] [PubMed]
  34. Preusser, M.; Brastianos, P.; Mawrin, C. Advances in meningioma genetics: Novel therapeutic opportunities. Nat. Rev. Neurol. 2018, 14, 106–115. [Google Scholar] [CrossRef]
  35. Kaley, T.; Barani, I.; Chamberlain, M.; McDermott, M.; Panageas, K.; Raizer, J.; Rogers, L.; Schiff, D.; Vogelbaum, M.; Weber, D.; et al. Historical benchmarks for medical therapy trials in surgery- and radiation-refractory meningioma: A RANO review. Neuro Oncol. 2014, 16, 829–840. [Google Scholar] [CrossRef] [Green Version]
  36. Huang, R.Y.; Bi, W.L.; Weller, M.; Kaley, T.; Blakeley, J.; Dunn, I.; Galanis, E.; Preusser, M.; McDermott, M.; Rogers, L.; et al. Proposed response assessment and endpoints for meningioma clinical trials: Report from the Response Assessment in Neuro-Oncology Working Group. Neuro Oncol. 2019, 21, 26–36. [Google Scholar] [CrossRef] [Green Version]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Maiuri, F.; Del Basso de Caro, M. Update on the Diagnosis and Management of Meningiomas. Cancers 2023, 15, 3575. https://doi.org/10.3390/cancers15143575

AMA Style

Maiuri F, Del Basso de Caro M. Update on the Diagnosis and Management of Meningiomas. Cancers. 2023; 15(14):3575. https://doi.org/10.3390/cancers15143575

Chicago/Turabian Style

Maiuri, Francesco, and Marialaura Del Basso de Caro. 2023. "Update on the Diagnosis and Management of Meningiomas" Cancers 15, no. 14: 3575. https://doi.org/10.3390/cancers15143575

APA Style

Maiuri, F., & Del Basso de Caro, M. (2023). Update on the Diagnosis and Management of Meningiomas. Cancers, 15(14), 3575. https://doi.org/10.3390/cancers15143575

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop