UCSF

Background and purpose

We aimed to develop a diagnostic algorithm for differentiation of cerebellar hemisphere tumors, combining Apparent Diffusion Coefficient (ADC) histogram analysis and structural imaging features.

Methods

Pretreatment MRI of patients with pathologically proven cerebellar hemisphere neoplasms were reviewed. Voxel-wise volumetric ADC histograms of tumor solid components were determined. Histogram variables, patients' age, and structural imaging features were applied to develop a differential diagnosis algorithm.

Results

Among 142 patients, the most common tumors were metastasis (n = 54), hemangioblastoma (n = 39), pilocytic astrocytoma (n = 18), and medulloblastoma (n = 9). On ADC histogram analysis, medulloblastomas had the lowest, and pilocytic astrocytomas had the highest ADC values. An ADC 15th percentile value < 580 × 10^-6 mm² /s could differentiate medulloblastomas from other cerebellar hemisphere tumors with receiver operating characteristic (ROC) area under the curve (AUC) of .98 (P < .001). Comparing the two most common adult cerebellar hemisphere tumors (metastases and hemangioblastomas), hemangioblastomas had higher ADC values; and an ADC 90th percentile value > 2000 × 10^-6 mm² /s could identify hemangioblastomas with ROC AUC of .82 (P < .001). Along with ADC histogram and patients' age, structural imaging features including enhancement pattern, prominent vascular flow voids, surrounding FLAIR hyperintensity, and solid component T2 signal hyperintensity were independent differentiation variables in the diagnostic algorithm.

Conclusion

In pretreatment differentiation of cerebellar tumors, the ADC histogram analysis is particularly helpful for distinction of medulloblastomas from other subtypes, and differentiation of the two most common adult tumors (ie, metastases and hemangioblastomas) from each other. Combination of structural imaging features and ADC histogram analysis can help with pretreatment differentiation of cerebellar tumors.