Features of diffuse gliomas that are misdiagnosed on initial neuroimaging: a case control study

Diffuse midline gliomas, H3 K27M-mutant are associated with less peritumoral edema and contrast enhancement in comparison to glioblastomas, H3 K27M-wildtype of midline structures

Purpose

The entity ‘diffuse midline glioma, H3 K27M-mutant (DMG)’ was introduced in the revised 4^th edition of the 2016 WHO classification of brain tumors. However, there are only a few reports on magnetic resonance imaging (MRI) of these tumors. Thus, we conducted a retrospective survey focused on MRI features of DMG compared to midline glioblastomas H3 K27M-wildtype (mGBM-H3wt).

Methods

We identified 24 DMG cases and 19 mGBM-H3wt patients as controls. After being retrospectively evaluated for microscopic evidence of microvascular proliferations (MVP) and tumor necrosis by two experienced neuropathologists to identify the defining histological criteria of mGBM-H3wt, the samples were further analyzed by two experienced readers regarding imaging features such as shape, peritumoral edema and contrast enhancement.

Results

The DMG were found in the thalamus in 37.5% of cases (controls 63%), in the brainstem in 50% (vs. 32%) and spinal cord in 12.5% (vs. 5%). In MRI and considering MVP, DMG were found to be by far less likely to develop peritumoral edema (OR: 0.13; 95%-CL: 0.02–0.62) (p = 0.010). They, similarly, were associated with a significantly lower probability of developing strong contrast enhancement compared to mGBM-H3wt (OR: 0.10; 95%-CL: 0.02–0.47) (P = 0.003).

Conclusion

Despite having highly variable imaging features, DMG exhibited markedly less edema and lower contrast enhancement in MRI compared to mGBM-H3wt. Of these features, the enhancement level was associated with evidence of MVP.

Imaging Mimics of Brain Tumors

Nonneoplastic entities may closely resemble the imaging findings of primary or metastatic intracranial neoplasia, posing diagnostic challenges for the referring provider and radiologist. Prospective identification of brain tumor mimics is an opportunity for the radiologist to add value to patient care by decreasing time to diagnosis and avoiding unnecessary surgical procedures and medical therapies, but requires familiarity with mimic entities and a high degree of suspicion on the part of the interpreting radiologist. This article provides a framework for the radiologist to identify "brain tumor mimics," highlighting imaging and laboratory pearls and pitfalls, and illustrating unique and frequently encountered lesions.

Stroke Mimics and Chameleons from the Radiological Viewpoint of Glioma Diagnosis

Gliomas are sometimes difficult to differentiate from strokes and are often misdiagnosed on magnetic resonance imaging (MRI); thus, the terms “stroke mimics” and “stroke chameleons” have been introduced. In this study, we analyzed stroke mimics and stroke chameleons in glioma and discussed the diagnostic perplexity. We retrospectively reviewed cases that were removed from lesions that were considered to be brain tumors. This study enrolled 214 patients who underwent tumor resection for suspected glioma. Clinical characteristics and radiological findings of the patients were compared between the masquerade findings group, which was further divided into two groups: the stroke chameleons and stroke mimics according to their final diagnosis, and the intelligible findings group. Stroke chameleons and stroke mimics were significantly higher in age and smaller in lesion size than the intelligible findings group. In the multivariate analysis, the predictive factor of the masquerade finding group was higher age and smaller size. Stroke mimics group has a tendency to be higher rate of hyperintensity lesion on diffusion-weighted imaging (DWI) compared with stroke chameleons group. The average period from initial diagnosis to pathological diagnosis was 13.50 days in the stroke chameleons and 61.50 days in the stroke mimics, which proved significantly different. Proper diagnosis of glioma and stroke affects a patient’s prognosis, and should be diagnosed as soon as possible. However, stroke mimics and stroke chameleons caused by glioma can occur. Thus, the diagnosis of a stroke should take into consideration the possibility of a glioma in real clinical situations.

Risk Factors for Perceptual-versus-Interpretative Errors in Diagnostic Neuroradiology

BACKGROUND AND PURPOSE

Diagnostic errors in radiology are classified as perception or interpretation errors. This study determined whether specific conditions differed when perception or interpretation errors occurred during neuroradiology image interpretation.

MATERIALS AND METHODS

In a sample of 254 clinical error cases in diagnostic neuroradiology, we classified errors as perception or interpretation errors, then characterized imaging technique, interpreting radiologist's experience, anatomic location of the abnormality, disease etiology, time of day, and day of the week. Interpretation and perception errors were compared with hours worked per shift, cases read per shift, average cases read per shift hour, and the order of case during the shift when the error occurred.

RESULTS

Perception and interpretation errors were 74.8% (n = 190) and 25.2% (n = 64) of errors, respectively. Logistic regression analyses showed that the odds of an interpretation error were 2 times greater (OR, 2.09; 95% CI, 1.05-4.15; P = .04) for neuroradiology attending physicians with ≤5 years of experience. Interpretation errors were more likely with MR imaging compared with CT (OR, 2.10; 95% CI, 1.09-4.01; P = .03). Infectious/inflammatory/autoimmune diseases were more frequently associated with interpretation errors (P = .04). Perception errors were associated with faster reading rates (6.01 versus 5.03 cases read per hour; P = .004) and occurred later during the shift (24th-versus-18th case; P = .04).

CONCLUSIONS

Among diagnostic neuroradiology error cases, interpretation-versus-perception errors are affected by the neuroradiologist's experience, technique, and the volume and rate of cases read. Recognition of these risk factors may help guide programs for error reduction in clinical neuroradiology services.