BOSH MIYA O'SMALARINI DIAGNOSTIKASIDA MAGNIT-REZONANS TOMOGRAFIYANING AHAMIYATI

Authors

  • Mohinur Xamidova Author
  • Amonova Rayhona Umidillo qizi Author

Keywords:

Kalit so'zlar: magnit-rezonans tomografiya, miya o'smalari, glioma, meningioma, diagnostik aniqlik, DWI, MRS, WHO 2021 tasnifi.

Abstract

Ushbu maqolada miya o‘smalarini aniqlash va baholashda magnit-rezonans tomografiya (MRT) usulining diagnostik ahamiyati yoritilgan. Zamonaviy tibbiyotda MRT yuqori aniqlikdagi tasvir olish imkoniyati bilan ajralib turib, miya to‘qimalarining tuzilishi, o‘smalarning joylashuvi, hajmi va tarqalish darajasini aniq ko‘rsatib beradi. Maqolada MRTning boshqa diagnostik usullarga nisbatan afzalliklari, xususan,  nurlanishsiz ishlashi, yumshoq to‘qimalarni yuqori kontrastda aks ettirishi va erta bosqichdagi patologik o‘zgarishlarni aniqlashdagi roli tahlil qilingan. Shuningdek, kontrast modda qo‘llanilgan MRT tekshiruvlarining o‘smalarni differensial diagnostika qilishdagi ahamiyati ham ko‘rib chiqilgan. Tadqiqot natijalari MRT usuli miya o‘smalarini erta aniqlash, to‘g‘ri tashxis qo‘yish va davolash taktikasini belgilashda muhim o‘rin tutishini ko‘rsatadi.

References

1. Assessment of the emerging role of AI in diagnosing gliomas using MRI: Systematic review and meta-analysis. Alsaedi A, Alsharif W, Gareeballah A, et al. Neuro-Oncology Advances. 2025;7(1):vdaf162. doi:10.1093/noajnl/vdaf162

2. Advances of MR imaging in glioma: what the neurosurgeon needs to know. Falk Delgado A. Acta Neurochirurgica. 2025. doi:10.1007/s00701-025-06593-6

3. Advanced MRI, Radiomics and Radiogenomics in Unravelling Incidental Glioma Grading and Genetic Status. Marchesini N, et al. Medicina. 2025;61(8):1453. doi:10.3390/medicina61081453

4. Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic Review on Glioma Segmentation and Classification on Multiparametric MRI. medRxiv. 2025. doi:10.64898/2026.01.13.26344038

5. Advancing Brain Tumor Analysis: Current Trends in Deep Learning-Based Brain MRI Tumor Diagnosis. MDPI Bioengineering. 2025. doi:10.3390/bioengineering6050082

6. Advanced imaging techniques for neuro-oncologic tumor diagnosis. Current Oncology Reports. 2025. doi:10.1007/s11912-025-01020-2

7. An advanced hybrid deep learning framework for high-precision brain tumor detection and classification in MRI scans. Shivahare BD, Subramaniam SK, Dafik, et al. Scientific Reports. 2026. doi:10.1038/s41598-026-50194-x

8. Artificial Intelligence Detection and Segmentation Models: A Systematic Review and Meta-Analysis of Brain Tumors in MRI. PMC. 2025. PMID: PMC11976016

9. A foundation model for brain tumor MRI analysis: WHO grading and subtype classification. ScienceDirect. 2025. doi:10.1016/j.radonc.2025.053010

10. A comprehensive evaluation of MRI-based radiogenomics and prognosis prediction in glioma. Astaraki M, Lazzeroni M, Toma-Dasu I. Frontiers in Oncology. 2026. doi:10.3389/fonc.2025.1679634

11. Exploring the Role of Advanced MRI in Understanding Glioblastoma Biology: A Scoping Review. Branco P, et al. Cancers. 2026;18(4):645. doi:10.3390/cancers18040645

12. Global, regional, and national burden of brain and central nervous system cancer: a systematic analysis of incidence, deaths, and DALYs with predictions to 2040. Zhang Q, Yu H, Zhong J, et al. International Journal of Surgery. 2025;111(6):4033–4038. doi:10.1097/JS9.0000000000002359. PMID: PMC12165519

13. Imaging the WHO 2021 Brain Tumor Classification: Fully Automated Analysis of Imaging Features of Newly Diagnosed Gliomas. PMC. 2025. PMID: PMC10136825

14. Improved brain tumor diagnostics and follow-up with novel magnetic resonance imaging methods: A single center study protocol. PMC. 2025. doi:10.1186/s12885-025-14046-x

15. Lightweight Transfer Learning Models for Multi-Class Brain Tumor Classification. Journal of Imaging Informatics in Medicine. 2025. doi:10.1007/s10278-025-01686-1

16. MRI-Based Radiomics for Non-Invasive Prediction of Molecular Biomarkers in Gliomas. PMC. 2026. PMID: PMC12897266. doi:10.3390/cancers18030001

17. Performance Evaluation of Artificial Intelligence Techniques in the Diagnosis of Brain Tumors: A Systematic Review and Meta-Analysis. Alhazmi F, et al. PMC. 2025. PMID: PMC12306512

18. Rethinking MRI Protocols for Pituitary Microadenomas: Prioritizing Non-Contrast Imaging for Safe Follow-Up. Tomography. 2025;11(9):105. doi:10.3390/tomography11090105

19. Tang PLY, Romero AM, Nout RA, et al. Amide proton transfer-weighted CEST MRI for radiotherapy target delineation of glioblastoma. European Radiology Experimental. 2025;9(1):12. doi:10.1186/s41747-025-00512-3

20. Utilizing Deep Learning to Improve Diagnostic Accuracy in Glioma, Pituitary Tumors, and Meningiomas. medRxiv. 2025. doi:10.1101/2024.12.10.24318709

21. Congress of Neurological Surgeons guidelines for imaging in WHO grade II diffuse glioma. Journal of Neuro-Oncology. 2025. doi:10.1007/s11060-025-05043-8

Published

2026-05-10

How to Cite

Mohinur Xamidova, & Amonova Rayhona Umidillo qizi. (2026). BOSH MIYA O’SMALARINI DIAGNOSTIKASIDA MAGNIT-REZONANS TOMOGRAFIYANING AHAMIYATI. JOURNAL OF NEW CENTURY INNOVATIONS, 100(2), 247-255. https://journalss.org/index.php/new/article/view/29048