MRI DIAGNOSIS OF BONE TUMORS: CONCEPTUAL APPROACH TO IMPROVE DIAGNOSTIC ACCURACY
Keywords:
MRI, bone tumors, diagnostic protocols, imaging standardization, tumor characterizationAbstract
The history and current state of MRI diagnostics in bone tumors are characterized. The key participants in the diagnostic process are outlined, basic principles are formulated. Challenges in differentiating benign, intermediate, and malignant lesions are discussed, and approaches to improve diagnostic accuracy are proposed.
References
Kransdorf MJ, Murphey MD. Imaging of Bone Tumors and Tumorlike Lesions. Springer, 2016.
Bloem JL, Reidsma II. “Bone and soft tissue tumor imaging: role of MRI.” Eur Radiol, 2019.
Baur A et al. “Diffusion-weighted MRI for bone marrow: differentiation of benign and malignant lesions.” Radiology, 2014.
Rajpurkar P, et al. “Deep learning for radiology: applications and limitations.” Radiology: AI, 2022.
Cho J, et al. “Artificial intelligence in musculoskeletal imaging: advances, limitations, and clinical applications.” Skeletal Radiology, 2021.
Murphey MD, et al. Imaging of osteosarcoma: MRI, CT, and radiographic features. Radiographics. 1992;12(4):703–719.
Kwee TC, et al. “DCE-MRI in oncology: physiological basis and clinical applications.” Insights into Imaging, 2019.
Vanel D. “MRI of bone tumors and tumor-like lesions.” European Journal of Radiology, 2020.
Azad, H., Ahmed, A., Zafar, I., Bhutta, M. R., Rabbani, M. A., Kc, H. R., & Bhutta, M. R. (2022). X-ray and MRI correlation of Bone tumors using histopathology as gold Standard. Cureus, 14(7).
Bentaieb A., Hamarneh G. Adversarial Stain Transfer for Histopathology Image Analysis. IEEE Trans. Med. Imaging. 2017;37:792–802.
Eweje F.R., Bao B., Wu J., Dalal D., Liao W.-H., He Y., Luo Y., Lu S., Zhang P., Peng X., et al. Deep Learning for Classification of Bone Lesions on Routine MRI. EBioMedicine. 2021;68:103402.
Schoot R.A., McHugh K., Van Rijn R.R., Kremer L.C.M., Chisholm J.C., Caron H.N., Merks
J.H.M. Response Assessment in Pediatric Rhabdomyosarcoma: Can Response Evaluation Criteria in Solid Tumors Replace Three-dimensional Volume Assessments? Radiology. 2013;269:870–878.
He Y., Pan I., Bao B., Halsey K., Chang M., Liu H., Peng S., Sebro R.A., Guan J., Yi T., et al. Deep learning-based classification of primary bone tumors on radiographs: A preliminary study. EBioMedicine. 2020;62:103121.
Gemescu, I. N., Thierfelder, K. M., Rehnitz, C., & Weber, M. A. (2019). Imaging features of bone tumors: conventional Radiographs and MR imaging correlation. Magnetic Resonance Imaging Clinics, 27(4), 753-767.
Saadh, M. J., Hussain, Q. M., Albadr, R. J., Doshi, H., Rekha, M. M., Kundlas, M., Rizaev, J.,
... & Farhood, B. (2025). Advanced feature fusion of radiomics and deep learning for accurate detection of wrist fractures on X-ray images. BMC Musculoskeletal Disorders, 26(1), 498.
Ризаев Ж. А., Гайбуллаев Э. А. РЕНТГЕНОЛОГИЧЕСКАЯ ДИАГНОСТИКА АГРЕССИВНОГО ПАРОДОНТИТА НА ФОНЕ ПРОВОДИМОГО ЛЕЧЕНИЯ //Журнал
гуманитарных и естественных наук. – 2025. – №. 20. – С. 14-19.
Гайбуллаев Э. А., Акрамова Н. А., Ризаев Ж. А. ОПТИМИЗАЦИЯ КЛИНИКО- РЕНТГЕНОЛОГИЧЕСКОЙ ДИАГНОСТИКИ АГРЕССИВНОГО ПАРОДОНТИТА
//Журнал гуманитарных и естественных наук. – 2025. – №. 18. – С. 170-173.
Rakhimov N. M. et al. Buyrak saratonini tarqalish darajasini baxolashda noinvaziv visualizasiya usullarini diagnostic imkoniyatlari // Journal of Reproductive Health and Urogenital Research . – 2021. – T. 2. – No. 1.
Rakhimov N. M. et al. Clinical, radiological and computed tomographic characteristics of thymoma // Journal of Biomedicine and Practice. – 2022. – T. 7. – No. 2.