Diagnostic Performance of Magnetic Resonance Imaging for Vertebral Metastases in Routine Clinical Practice

MRI Diagnostic Performance for Vertebral Metastases in Routine Practice

Authors

  • Laila Khan Department of Radiology, Bacha Khan Medical College, Medical Teaching Institute, Mardan Medical Complex, Mardan, Pakistan
  • Zubair Janan Orakzai Department of Radiology, Bacha Khan Medical College, Medical Teaching Institute, Mardan Medical Complex, Mardan, Pakistan
  • Tabassum Begum Department of Radiology, Bacha Khan Medical College, Medical Teaching Institute, Mardan Medical Complex, Mardan, Pakistan
  • Hina Baig Department of Radiology, Bacha Khan Medical College, Medical Teaching Institute, Mardan Medical Complex, Mardan, Pakistan
  • Sumaira Noureen Department of Radiology, Bacha Khan Medical College, Medical Teaching Institute, Mardan Medical Complex, Mardan, Pakistan
  • Muhammad Sadiq Department of Radiology, Bacha Khan Medical College, Medical Teaching Institute, Mardan Medical Complex, Mardan, Pakistan

DOI:

https://doi.org/10.54393/pjhs.v7i4.3830

Keywords:

Spine MRI, Vertebral Metastasis, Diagnostic Accuracy, Histopathology, Tumor Board Consensus, ROC Curve

Abstract

Vertebral metastasis is a frequent complication of systemic malignancies and may cause pain, pathological fractures, and neurological compromise. MRI is commonly used for suspected spinal metastases; however, the diagnostic performance of routine 1.5-Tesla MRI interpreted using visual criteria in everyday practice remains unclear. Objectives: To determine the diagnostic accuracy of conventional MRI for the detection of vertebral metastases using histopathology and/or multidisciplinary clinic radiologic consensus as reference standards. Methods: This prospective cross-sectional study at MTI Bacha Khan Medical College and Mardan Medical Complex, Pakistan (15 Sept–15 Dec 2025), included 106 adults with suspected vertebral neoplastic lesions who underwent 1.5-Tesla MRI. Two blinded radiologists independently interpreted scans, resolving discrepancies by consensus. Histopathology and/or tumor board consensus served as the reference standard. Sensitivity, specificity, predictive values, accuracy, and ROC-AUC were calculated. Results: Of 106 patients, 64 (60.4%) had vertebral metastases by the reference standard. MRI sensitivity was 42.2% (95% CI: 30.1–54.3), specificity 54.8% (95% CI: 39.7–69.9), PPV 58.7% (95% CI: 44.5–72.9), NPV 38.3% (95% CI: 26.1–50.5), and overall accuracy 47.2% (95% CI: 37.7–56.7). The AUC was 0.515 (95% CI: 0.40–0.63). False positives were commonly due to hemangioma and infection, whereas false negatives were mainly related to poor image quality, small/early lesions, and reader variability. Conclusions: Conventional 1.5-Tesla MRI using routine visual criteria showed low sensitivity, modest specificity, and limited accuracy for vertebral metastases. Clinical correlation and reference-standard confirmation remain essential for equivocal cases.

Author Biographies

Zubair Janan Orakzai, Department of Radiology, Bacha Khan Medical College, Medical Teaching Institute, Mardan Medical Complex, Mardan, Pakistan

 

Muhammad Sadiq, Department of Radiology, Bacha Khan Medical College, Medical Teaching Institute, Mardan Medical Complex, Mardan, Pakistan

   

 

References

Zahra SB, Majeed AI, Ehsan J. Positive Findings on Magnetic Resonance Imaging (MRI) of the Patients Diagnosed with Vertebral Metastases on Bone Scintigraphy. Breast Cancer. 2024 Oct; 11: 36-37.

Ahmed T, Jahan N, Sharmin F, Jahan N. Pattern of Skeletal Metastasis in Breast Cancer Patients Referred in Institute of Nuclear Medicine and Allied Sciences, Barishal. Bangladesh Journal of Nuclear Medicine. 2020; 23(2): 37-39. doi: 10.3329/bjnm.v23i1-2.57707. DOI: https://doi.org/10.3329/bjnm.v23i1-2.57707

Faiella E, Santucci D, Vertulli D, Russo F, Vadalà G, Papalia R et al. Preoperative Embolization of Vertebral Metastasis: Comprehensive Review of The Literature. Diseases. 2023 Aug; 11(3): 109. doi: 10.3390/diseases11030109. DOI: https://doi.org/10.3390/diseases11030109

Alfonso M, Llombart R, Gil L, Martinez I, Rodríguez C, Álvarez L et al. Tumor Ablation and Vertebral Augmentation in The Treatment of Vertebral Metastases: A Multicenter Study. Revista Espanola De Cirugia Ortopedica Y Traumatologia. 2023 Nov; 67(6): 480-486. doi: 10.1016/j.recot.2023.08.003. DOI: https://doi.org/10.1016/j.recot.2023.08.003

Ong W, Zhu L, Zhang W, Kuah T, Lim DS, Low XZ et al. Application of Artificial Intelligence Methods for Imaging of Spinal Metastasis. Cancers. 2022 Aug; 14(16): 4025. doi: 10.3390/cancers14164025. DOI: https://doi.org/10.3390/cancers14164025

Zhan K, Chen K, Gao G, Xiang Y. A Retrospective Cohort Study on the Efficacy and Safety of Percutaneous Vertebroplasty Combined with Bone-Filling Mesh Container in Vertebral Metastases with Posterior Wall Defect. Frontiers in Oncology. 2024 Jan; 13: 1312491. doi: 10.3389/fonc.2023.1312491. DOI: https://doi.org/10.3389/fonc.2023.1312491

Tsuchiya K, Gomyo M, Katase S, Hiraoka S, Tateishi H. Magnetic Resonance Bone Imaging: Applications to Vertebral Lesions. Japanese Journal of Radiology. 2023 Nov; 41(11): 1173-1185. doi: 10.1007/s11604-023-01449-4. DOI: https://doi.org/10.1007/s11604-023-01449-4

Faiella E, Santucci D, Calabrese A, Russo F, Vadalà G, Zobel BB et al. Artificial Intelligence in Bone Metastases: A Magnetic Resonance Imaging and Computed Tomography Scan Imaging Review. International Journal of Environmental Research and Public Health. 2022 Feb; 19(3): 1880. doi: 10.3390/ijerph19031880. DOI: https://doi.org/10.3390/ijerph19031880

Yang HL, Liu T, Wang XM, Xu Y, Deng SM. Diagnosis of Bone Metastases: A Meta-Analysis Comparing 18F-Fluoro-2-Deoxy-D-Glucose, Positron Emission Tomography, Computed Tomography, Magnetic Resonance Imaging, and Bone Scintigraphy. European Radiology. 2011 Dec; 21(12): 2604-2617. doi: 10.1007/s00330-011-2221-4. DOI: https://doi.org/10.1007/s00330-011-2221-4

Harlianto NI, van der Star S, Suelmann BB, de Jong PA, Verlaan JJ et al. Diagnostic Accuracy of Imaging Modalities for Detection of Spinal Metastases: A Systematic Review and Meta-Analysis. Clinical and Translational Oncology. 2025 May; 27(5): 2316-2326. doi: 10.1007/s12094-024-03765-1. DOI: https://doi.org/10.1007/s12094-024-03765-1

Ong W, Lee A, Tan WC, Fong KT, Lai DD, Tan YL et al. Oncologic Applications of Artificial Intelligence and Deep Learning Methods in Computed Tomography Spine Imaging—A Systematic Review. Cancers. 2024 Aug; 16(17): 2988. doi: 10.3390/cancers16172988. DOI: https://doi.org/10.3390/cancers16172988

Mijaljevic MB, Milosevic ZC, Lavrnic SĐ, Jokovic ZM, Ninkovic DI, Tubic RM et al. Assessment of Chemical-Shift and Diffusion-Weighted Magnetic Resonance Imaging in Differentiating Malignant and Benign Vertebral Lesions in Oncologic Patients. A Single Institution Experience. Radiology and Oncology. 2024 Oct; 58(4): 527-534. doi: 10.2478/raon-2024-0049. DOI: https://doi.org/10.2478/raon-2024-0049

Zhang S, Liu M, Li S, Cui J, Zhang G, Wang X. An MRI-Based Radiomics Nomogram for Differentiating Spinal Metastases from Multiple Myeloma. Cancer Imaging. 2023 Jul; 23(1): 72. doi: 10.1186/s40644-023-00585-4. DOI: https://doi.org/10.1186/s40644-023-00585-4

Sanker V, Gowda P, Thaller A, Li Z, Heesen P, Qiang Z et al. Applications and Performance of Artificial Intelligence in Spinal Metastasis Imaging: A Systematic Review. Journal of Clinical Medicine. 2025 Aug; 14(16): 5877. doi: 10.3390/jcm14165877. DOI: https://doi.org/10.3390/jcm14165877

Cao J, Li Q, Zhang H, Wu Y, Wang X, Ding S et al. Radiomics Model Based on MRI To Differentiate Spinal Multiple Myeloma from Metastases: A Two-Center Study. Journal of Bone Oncology. 2024 Apr; 45: 100599. doi: 10.1016/j.jbo.2024.100599. DOI: https://doi.org/10.1016/j.jbo.2024.100599

Kim DH, Seo J, Lee JH, Jeon ET, Jeong D, Chae HD et al. Automated Detection and Segmentation of Bone Metastases on Spine MRI Using U-Net: A Multicenter Study. Korean Journal of Radiology. 2024 Feb; 25(4): 363. doi: 10.3348/kjr.2023.0671. DOI: https://doi.org/10.3348/kjr.2023.0671

Motohashi M, Funauchi Y, Adachi T, Fujioka T, Otaka N, Kamiko Y et al. A New Deep Learning Algorithm for Detecting Spinal Metastases on Computed Tomography Images. Spine. 2024 Mar; 49(6): 390-397. doi: 10.1097/BRS.0000000000004889. DOI: https://doi.org/10.1097/BRS.0000000000004889

Daneshvar K, Shahrbaf M, Heverhagen J, Bryjova K, Aebersold DM, Maralani PJ et al. Radiological Response Assessment After Stereotactic Body Radiotherapy for Spine Metastases Using Magnetic Resonance Imaging: A Systematic Review. Physics and Imaging in Radiation Oncology. 2025 Sep: 100840. doi: 10.1016/j.phro.2025.100840. DOI: https://doi.org/10.1016/j.phro.2025.100840

Brage K, Pedersen MR, Lauridsen CA, Paulo C, Hansen P, Precht H et al. Reporting Radiographers in CT and MRI: A Literature Review with A Systematic Approach. Radiography. 2025 Mar; 31(2): 102901. doi: 10.1016/j.radi.2025.102901. DOI: https://doi.org/10.1016/j.radi.2025.102901

Murphy L, Nightingale J, Calder P. Difficulties Associated with Reporting Radiographer Working Practices–A Narrative Evidence Synthesis. Radiography. 2022 Nov; 28(4): 1101-1109. doi: 10.1016/j.radi.2025.102901. DOI: https://doi.org/10.1016/j.radi.2022.08.007

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Published

2026-04-30
CITATION
DOI: 10.54393/pjhs.v7i4.3830
Published: 2026-04-30

How to Cite

Khan, L., Orakzai, Z. J., Begum, T., Baig, H., Noureen, S., & Sadiq, M. (2026). Diagnostic Performance of Magnetic Resonance Imaging for Vertebral Metastases in Routine Clinical Practice: MRI Diagnostic Performance for Vertebral Metastases in Routine Practice. Pakistan Journal of Health Sciences, 7(4), 111–118. https://doi.org/10.54393/pjhs.v7i4.3830

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