Concordance and Discordance between Radiology Resident’s and Radiologist’s Interpretation of Brain MRI in Patients with Head Masses
Brain MRI in Patients with Head Masses
DOI:
https://doi.org/10.54393/pjhs.v4i10.1078Keywords:
Brain Masses, MRI Brain, Concordance, DiscordanceAbstract
Diagnosis of head masses involves clinical examination, neurological signs, and radiological imaging. MRI is the preferred imaging tool for detailed assessment of tumor, its extent and treatment plan. Objective: To find the level of concordance and discordance between radiology resident’s and consultant’s interpretation of MRI (Magnetic Resonance Imaging) done for brain masses. Methods: A cross sectional study was conducted at the radiology department of Rehman Medical Institute, Peshawar. Simple random sampling was done and sample size was calculated using kappa coefficients (Donner and Rotondi) n=100. 100 patients who visited department of Radiology over a period of two years were assessed by prospective analysis of their radiology reports. Senior resident’s and consultant’s reports were compared. All pre-op patients were included irrespective of age or gender. Data were collected and recorded on a specially designed proforma and entered into Microsoft excel and analyzed using SPSS (Version 22.0. IBM Corp., Armonk, NY). Results: MRI brain reports of 58 male and 42 female patients were evaluated. The most common tumors were gliomas, making up 52% of the total tumors. Metastasis being second most common tumor making 16%, meningiomas in 15%, pituitary tumors in 10% and vestibular schwannomas in 7% of the patients. Concordance, discordance, and Cohen’s Kappa values in different masses were gliomas. (Concordance=88.46%, Discordance=11.54%, k=0.336), Meningiomas (Concordance=86.66%, Discordance=13.34%, k=0.423), Metastasis (Concordance=81.25%, Discordance=18.75%, k=0.294), Pituitary Tumors (Concordance=80%) Discordance=20%, k=0.375) and Vestibular Schwannomas (Concordance=85.71%, Discordance= 14.29% k=0.588). Conclusions: There was no statistically significant difference between senior resident’s and consultant radiologist’s report of MRI brain masses.
References
Louis DN, Perry A, Wesseling P, Brat DJ, Cree IA, Figarella-Branger D, et al. The 2021 WHO classification of tumors of the central nervous system: A summary. Neuro-Oncology. 2021 Aug; 23(8): 1231–51. doi: 10.1093/neuonc/noab106. DOI: https://doi.org/10.1093/neuonc/noab106
Barnholtz-Sloan JS, Ostrom QT, Cote D. Epidemiology of Brain Tumors. Neurologic Clinics. Elsevier; 2018 Aug; 36(3): 395–419. doi: 10.1016/j.ncl.2018.04.001. DOI: https://doi.org/10.1016/j.ncl.2018.04.001
Farmanfarma KK, Mohammadian M, Shahabinia Z, Hassanipour S, Salehiniya H. Brain cancer in the world: an epidemiological review. World Cancer Research Journal. 2019 Jan; 6(5): 1-5.
Zhang AS, Ostrom QT, Kruchko C, Rogers L, Peereboom DM, Barnholtz-Sloan JS. Complete prevalence of malignant primary brain tumors registry data in the United States compared with other common cancers, 2010. Neuro Oncology. 2017 May; 19(5): 726–35. doi: 10.1093/neuonc/now252. DOI: https://doi.org/10.1093/neuonc/now252
Alentorn A, Hoang-Xuan K, Mikkelsen T. Presenting signs and symptoms in brain tumors. Handbook of Clinical Neurology. 2016 Jan; 134: 19–26. doi: 10.1016/B978-0-12-802997-8.00002-5. DOI: https://doi.org/10.1016/B978-0-12-802997-8.00002-5
Tiwari A, Srivastava S, Pant M. Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters. 2020 Mar; 131: 244–60. doi: 10.1016/j.patrec.2019.11.020. DOI: https://doi.org/10.1016/j.patrec.2019.11.020
Villanueva-Meyer JE, Mabray MC, Cha S. Current clinical brain tumor imaging. Clinical Neurosurgery. 2017 Sep; 81(3): 397–415. doi: 10.1093/neuros/nyx103. DOI: https://doi.org/10.1093/neuros/nyx103
Dixon L, Jandu GK, Sidpra J, Mankad K. Diagnostic accuracy of qualitative MRI in 550 paediatric brain tumours: evaluating current practice in the computational era. Quantitative Imaging in Medicine and Surgery. 2022 Jan; 12(1): 131. doi: 10.21037/qims-20-1388. DOI: https://doi.org/10.21037/qims-20-1388
Watts J, Box G, Galvin A, Brotchie P, Trost N, Sutherland T. Magnetic resonance imaging of meningiomas: a pictorial review. Insights into Imaging. 2014 Feb; 5(1): 113-22. doi: 10.1007/s13244-013-0302-4. DOI: https://doi.org/10.1007/s13244-013-0302-4
Martucci M, Russo R, Schimperna F, D’Apolito G, Panfili M, Grimaldi A, et al. Magnetic resonance imaging of primary adult brain tumors: state of the art and future perspectives. Biomedicines. 2023 Jan; 11(2): 364. doi: 10.3390/biomedicines11020364. DOI: https://doi.org/10.3390/biomedicines11020364
Sharpe RE, Surrey D, Gorniak RJT, Nazarian L, Rao VM, Flanders AE. Radiology report comparator: A novel method to augment resident education. Journal of Digital Imaging. 2012 Jun; 25(3): 330–6. doi: 10.1007/s10278-011-9419-5. DOI: https://doi.org/10.1007/s10278-011-9419-5
Cooper VF, Goodhartz LA, Nemcek Jr AA, Ryu RK. Radiology resident interpretations of on-call imaging studies: the incidence of major discrepancies. Academic Radiology. 2008 Sep; 15(9): 1198-204. doi: 10.1016/j.acra.2008.02.011. DOI: https://doi.org/10.1016/j.acra.2008.02.011
Filippi CG, Schneider B, Burbank HN, Alsofrom GF, Linnell G, Ratkovits B. Discrepancy rates of radiology resident interpretations of on-call neuroradiology MR imaging studies. Radiology. 2008 Dec; 249(3): 972–9. doi: 10.1148/radiol.2493071543. DOI: https://doi.org/10.1148/radiol.2493071543
Salca D, Lersy F, Willaume T, Stoessel M, Lefèvre A, Ardellier F-D, et al. Evaluation of neuroradiology emergency MRI interpretations: low discrepancy rates between on-call radiology residents’ preliminary interpretations and neuroradiologists’ final reports. European Radiology. 2022 Apr; 32(10): 7260–9. doi: 10.1007/s00330-022-08789-1. DOI: https://doi.org/10.1007/s00330-022-08789-1
Weinberg BD, Richter MD, Champine JG, Morriss MC, Browning T. Radiology resident preliminary reporting in an independent call environment: Multiyear assessment of volume, timeliness, and accuracy. Journal of the American College of Radiology. 2015 Jan; 12(1): 95–100. doi: 10.1016/j.jacr.2014.08.005. DOI: https://doi.org/10.1016/j.jacr.2014.08.005
Müller SJ, Khadhraoui E, Neef NE, Riedel CH, Ernst M. Differentiation of brain metastases from small and non-small lung cancers using apparent diffusion coefficient (ADC) maps. BMC Med Imaging. 2021 Dec; 21(1): 1-8. doi: 10.1186/s12880-021-00602-7. DOI: https://doi.org/10.1186/s12880-021-00602-7
Visser M, Müller DM, van Duijn RJ, Smits M, Verburg N, Hendriks EJ, et al. Inter-rater agreement in glioma segmentations on longitudinal MRI. NeuroImage: Clinical. 2019 Jan; 22: 101727. doi: 10.1016/j.nicl.2019.101727. DOI: https://doi.org/10.1016/j.nicl.2019.101727
Raban D, Patel SH, Honce JM, Rubinstein D, DeWitt PE, Timpone VM. Intracranial meningioma surveillance using volumetrics from T2-weighted MRI. Journal of Neuroimaging. 2022 Jan; 32(1): 134–40. doi: 10.1111/jon.12926. DOI: https://doi.org/10.1111/jon.12926
Bruno MA, Duncan JR, Bierhals AJ, Tappouni R. Overnight resident versus 24-hour attending radiologist coverage in academic medical centers. Radiology. 2018 Dec; 289(3): 809–13. doi: 10.1148/radiol.2018180690. DOI: https://doi.org/10.1148/radiol.2018180690
Derakhshani A, Ding J, Vijayasarathi A. On-call radiology 2020: Where trainees look for help in a high stakes and time sensitive environment. Clinical Imaging. 2021 Sep; 77: 219–23. doi: 10.1016/j.clinimag.2021.05.003. DOI: https://doi.org/10.1016/j.clinimag.2021.05.003
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