Аrtificial intelligence technologies in the personalized treatment of patients with recurrent intracranial meningiomas
https://doi.org/10.18705/2782-3806-2025-5-1-79-86
EDN: XBRVKP
Abstract
The most common primary tumors of the central nervous system in the adult population are meningiomas. There is a group of patients with aggressive meningiomas with a recurrent type of disease, even after radical removal of the tumor and radiotherapy. Recurrence of meningiomas occur in every 4th patient. Repeated neurosurgical treatment of these patients is associated with a high risk of developing or exacerbating neurological deficits, and radical removal is not always possible. Currently, there are no generally accepted standards for the treatment of patients with recurrent meningiomas. The decision to choose the right treatment strategy is made in about half of patients with recurrent meningiomas, the process leading to such a decision remains complex and often relies on simple logical and empirical approaches of specialist doctors based on available data, which, as a rule, have a large volume. The constant growth of the volume of multimodal data in neuro-oncology outstrips the possibilities of their analysis by experts using traditional approaches. It is quite difficult for a neurosurgeon to predict how the neoplastic process in the central nervous system will behave. Thus, neurosurgeons need to seek help from modern artificial intelligence (AI) technologies.
About the Authors
K. K. KukanovRussian Federation
Kukanov Konstantin K., MD, PhD, Neurosurgeon, Senior Researcher at the Institute of Neuro-Oncology
Mayakovskaya str., 12, Saint Petersburg, 191014
A. N. Kalinichenko
Russian Federation
Kalinichenko Aleksandr N., Doctor of Science, Professor of the Department of Bioengineering Systems
Saint Petersburg
K. E. Agapova
Russian Federation
Agapova Kseniya E., undergraduate student of the Department of Bioengineering Systems
Saint Petersburg
M. A. Bolozia
Russian Federation
Bolozya Mariya A., undergraduate student of the Department of Bioengineering Systems
Saint Petersburg
N. E. Voinov
Russian Federation
Voinov Nikita E., neurosurgeon; specialist in scientific and analytical work of the World-Class Research Centre for Personalized Medicine
Mayakovskaya str., 12, Saint Petersburg, 191014
A. Z. Gagiev
Russian Federation
Gagiev Alexander Z., The resident of the Department of Neurosurgery
Mayakovskaya str., 12, Saint Petersburg, 191014
S. S. Sklyar
Russian Federation
Sklyar Sofia S., MD, PhD, Neurosurgeon, oncologist, Senior Researcher at the Institute of Neuro-Oncology
Mayakovskaya str., 12, Saint Petersburg, 191014
K. A. Samochernykh
Russian Federation
Samochernykh Konstantin A., Doctor of Medical Sciences, Professor of the Russian Academy of Sciences, neurosurgeon of the highest category, the Director
Mayakovskaya str., 12, Saint Petersburg, 191014
References
1. Ostrom QT, Patil N, Cioffi G, et al. CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2013–2017. Neuro-Oncology. 2020;22(1):1–96. https://doi.org/10.1093/neuonc/noaa200
2. Goldbrunner R, Stavrinou P, Jenkinson MD, et al. EANO guideline on the diagnosis and management of meningiomas. Neurooncol. 2021;23(11):1821–1834. https://doi.org/10.1093/neuonc/noab150
3. Mair MJ, Berghoff AS, Brastianos PK, Preusser M. Emerging systemic treatment options in meningioma. J Neuro-oncolog. 2023;161(2):245–258. http://doi.org/10.1007/s11060-022-04148-8.
4. Kukanov KK, Sklyar SS, Sitovskaya DA, et al. Chemotherapy in the structure of complex treatment of patients with recurrent intracranial meningiomas. Russian Neurosurgical Journal named after Professor A. L. Polenov. 2024;16(2):57–68.
5. Kukanov KK, Vorobyova OM, Zabrodskaya YuM, et al. Intracranial meningiomas: clinical, intrascopic and pathomorphological causes of recurrence (literature review). Siberian journal of oncology. 2022; 21(4):110–123.
6. Kukanov KK, Ushanov VV, Zabrodskaya YuM, et al. Ways to personalize the treatment of patients with relapse and continued growth of intracranial meningiomas. Russian Journal for Personalized Medicine. 2023;3(3):48–63.
7. Violaris K, Katsarides V, Sakellariou P. The Recurrence Rate in Meningiomas: Analysis of Tumor Location, Histological Grading, and Extent of Resection. Open J Modern Neurosurg. 2012;2:6–10. https://doi.org/10.4236/ojmn.2012.21002.
8. Huntoon K, Toland AMS, Dahiya S. Meningioma: a review of clinicopathological and molecular aspects. Front Oncol. 2020;10(10):1-14. https://doi.org/10.3389/fonc.2020.579599.
9. Commins D, Atkinson R, Burnett M. Review of meningioma histopathology. Neurosurg Focus. 2007;23(4):1–9. https://doi.org/10.3171/FOC-07/10/E3.
10. Cao X, Hao S, Wu Z, et al. Treatment Response and Prognosis After Recurrence of Atypical Meningiomas. World Neurosurg. 2015;84(4):1014–1019. https://doi.org/10.1016/j.wneu.2015.05.032.
11. Brastianos P, Galanis E, Butowski N, et al. Advances in multidisciplinary therapy for meningiomas. Neuro Oncol. 2019;21(1):118–131. http://doi.org/10.1093/neuonc/noy136.
12. Kukanov KK, Nechaeva AS, Sitovskaya DA, et al. The first experience of intraoperative photodynamic therapy in the structure of complex treatment of patients suffering from recurrence and continued growth of intracranial meningiomas. Bulletin of the Russian Military Medical Academy. 2024 June 10; 26(2):243–258.
13. Danilov GV, Ishankulov TA, Kotik KV, et al. Artificial intelligence technologies in clinical neurooncology. Zhurnal voprosy neirokhirurgii imeni N. N. Burdenko=Burdenko’s Journal of Neurosurgery. 2022;86(6):127–133.
14. Kukanov KK, Ushanov VV, Suxoparov PD, et al. The basic principles and features of surgical treatment for recurrence and continued growth of intracranial meningiomas. Russian Neurosurgical Journal named after Professor A. L. Polenov. 2024;16(1):54–68.
15. Olyushin VE, Kukanov KK, Nechaeva AS, et al. Photodynamic therapy in neurooncology. Biomedical Photonics. 2023;12(3):25–35. https://doi.org/10.24931/2413-9432-2023-12-3-25-35
16. Gadzhiev Ya, Shalbuzova KI. Application of machine learning methods in cancer prediction and early detection. Sciences of Europe. 2022;108:48.
17. Breiman L. Random Forests. Machine Learning. 2001;45:5–32. https://doi.org/10.1023/A:1010933404324.
Review
For citations:
Kukanov K.K., Kalinichenko A.N., Agapova K.E., Bolozia M.A., Voinov N.E., Gagiev A.Z., Sklyar S.S., Samochernykh K.A. Аrtificial intelligence technologies in the personalized treatment of patients with recurrent intracranial meningiomas. Russian Journal for Personalized Medicine. 2025;5(1):79-86. (In Russ.) https://doi.org/10.18705/2782-3806-2025-5-1-79-86. EDN: XBRVKP