NEUROIMAGING PITFALLS: WHEN TUBERCULOMAS MASQUERADE AS GLIOMAS ON MRI
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Keywords
Artificial intelligence, Gliomas, Magnetic resonance imaging, Misdiagnosis, Tuberculomas, Neuroimaging pitfalls
Abstract
The clinical and radiological features of intracranial tuberculomas and gliomas share many similarities which creates a difficult diagnostic task for healthcare providers. This review examines the intricate neuroimaging errors which result in tuberculoma misdiagnosis as glioma on MRI examinations in endemic and non-endemic areas. MRI features of ring enhancement along with central necrosis and perilesional edema exist in both lesions which creates confusion during diagnosis. Additional diagnostic information comes from advanced imaging technologies DWI and MRS together with perfusion imaging and PET but these methods provide limited certainty. The application of artificial intelligence and radiomics technologies faces restrictions from current availability challenges and validation needs to boost their specificity potential. This review examines the research literature and retrospective studies with reported cases to establish the diagnostic imaging markers and determine clinical mistakes and diagnostic errors' implications which result in superfluous surgeries and delayed therapy with systemic adverse effects. The authors recommend using a structured diagnostic system which combines multiparametric imaging results with clinical evaluation and pathological verification. The article devotes particular attention to developing strategies for areas with limited resources and high tuberculosis burdens. The article provides support to clinicians and neuroradiologists for minimizing diagnostic errors while enhancing treatment decisions and patient outcomes during evaluations of CNS lesions with malignant-like characteristics.
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Dr. Kunj Shah
Assistant Professor Radiology, Sirt Hosoital and Government medical College, Bhavnagar (Gujarat), drkunjsip@gmail.com