Back

Misdiagnosis and underdiagnosis of glioma: Systematic review

Ilchenko, D.; Zurrer, W. E.; Cannon, A.; Piccirelli, M.; Kulcsar, Z.; Winklhofer, S.; Ineichen, B. V.

2025-04-04 neurology
10.1101/2025.04.03.25325091 medRxiv
Show abstract

BackgroundDiagnostic errors in gliomas, a major group of brain tumors originating from glial cells, can have severe consequences for patients and healthcare systems. Despite the serious implications of a glioma diagnosis, there is a lack systematic evidence on the frequency of diagnostic errors. This study aimed to assess the prevalence of misdiagnosis and underdiagnosis of gliomas and their potential impact. MethodsWe conducted a systematic review of English-language original studies, including those with more than 10 participants, reporting diagnostic errors in gliomas. A search of Medline and Embase was performed from inception until October 28, 2024. We evaluated the proportions of diagnostic errors and their potential consequences, and assessed the risk of bias using a modified Newcastle-Ottawa Scale. ResultsOf 1,860 studies screened, 22 met the inclusion criteria. Our analysis indicates that gliomas are frequently underdiagnosed (i.e., the correct diagnosis is missed) and, though less extensively reported, also misdiagnosed (i.e., assigned incorrect diagnoses). Overall, diagnostic errors ranged from 1.9 to 11.6% with a median of 5% (mis-/underdiagnosis: 5%, range 1.9-20%, misgrading: 11.6% range: 0.6-47%). While diagnosing gliomas is complex, evidence suggests that clinical tools such as MRI and pathological methods, including neuroendoscopic biopsy and specific cytology techniques, can achieve high diagnostic accuracy. Diagnostic errors were linked to relevant patient consequences, with both overtreatment and undertreatment commonly reported. ConclusionDiagnostic errors in gliomas are common and can have serious implications for patients. More rigorous data are needed to better understand the causes of these errors, which is key for reducing misdiagnosis and underdiagnosis in the future.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.