Diffusion-weighted MRI combined with susceptibility-weighted MRI: added diagnostic value for four common lateral ventricular tumors

An update on susceptibility-weighted imaging in brain gliomas

Susceptibility-weighted imaging (SWI) has become a standard component of most brain MRI protocols. While traditionally used for detecting and characterising brain hemorrhages typically associated with stroke or trauma, SWI has also shown promising results in glioma assessment. Numerous studies have highlighted SWI's role in differentiating gliomas from other brain lesions, such as primary central nervous system lymphomas or metastases. Additionally, SWI aids radiologists in non-invasively grading gliomas and predicting their phenotypic profiles. Various researchers have suggested incorporating SWI as an adjunct sequence for predicting treatment response and for post-treatment monitoring. A significant focus of these studies is on the detection of intratumoural susceptibility signals (ITSSs) in gliomas, which are indicative of microhemorrhages and vessels within the tumour. The quantity, distribution, and characteristics of these ITSSs can provide radiologists with more precise information for evaluating and characterising gliomas. Furthermore, the potential benefits and added value of performing SWI after the administration of gadolinium-based contrast agents (GBCAs) have been explored. This review offers a comprehensive, educational, and practical overview of the potential applications and future directions of SWI in the context of glioma assessment. CLINICAL RELEVANCE STATEMENT: SWI has proven effective in evaluating gliomas, especially through assessing intratumoural susceptibility signal changes, and is becoming a promising, easily integrated tool in MRI protocols for both pre- and post-treatment assessments. KEY POINTS: • Susceptibility-weighted imaging is the most sensitive sequence for detecting blood and calcium inside brain lesions. • This sequence, acquired with and without gadolinium, helps with glioma diagnosis, characterisation, and grading through the detection of intratumoural susceptibility signals. • There are ongoing challenges that must be faced to clarify the role of susceptibility-weighted imaging for glioma assessment.

Histogram Analysis Parameters ADC for Distinguishing Ventricular Neoplasms of Ependymoma, Choroid Plexus Papilloma, and Central Neurocytoma

Background

To determine if histograms of ADC can be used to differentiate ventricular ependymomas, choroid plexus papillomas (CPPs), and central neurocytomas (CNCs).

Material/Methods

We retrospectively reviewed records from 185 patients from 1 January 2014 to 1 November 2018. We finally included a total of 60 patients: 36 (60.00%) had histologically confirmed ependymomas, 10 (16.67%) had CPPs, and 14 (23.33%) had CNCs, as determined by routine MRI scanning at 3.0T. The ADC histogram features were derived and then compared by Kruskal-Wallis test (they were not normally distributed). Bonferroni test was used to compare the 2 groups and then we determined the ROC.

Results

Ependymomas had significantly higher mean, perc.01%, perc.10%, perc.50%, perc.90%, and perc.99% than CNCs. Ependymomas had significantly lower skewness than CNCs. Histogram metrics derived from mean, perc.01%, perc.10%, perc.50%, and perc.90% were significantly lower in the CNCs group than in the CPPs group. CPPs showed significantly lower skewness than CNCs. A threshold value of 86.50 for perc.50% to predict ependymomas from CNCs was estimated (AUC=0.97, sensitivity=97.20%, specificity=85.70%). Optimal diagnostic performance to predict CPPs from CNCs (AUC=0.96, sensitivity=100.00%, specificity=85.70%) was obtained when setting Perc.50%=84.00 as the threshold value.

Conclusions

The ADC histogram analysis may help to discriminate ependymomas, CPPs, and CNCs.