METHODS::Glioma growth can cause pervasive changes in the functional connectivity (FC) of brain networks, which has been associated with re-organization of brain functions and development of functional deficits in patients. Mechanisms underlying functional re-organization in brain networks are not understood and efforts to utilize functional imaging for surgical planning, or as a biomarker of functional outcomes are confounded by the heterogeneity in available human data. Here we apply multiple imaging modalities in a well-controlled murine model of glioma with extensive validation using human data to explore mechanisms of FC disruption due to glioma growth. We find gliomas cause both local and distal changes in FC. FC changes in networks proximal to the tumor occur secondary to hemodynamic alterations but surprisingly, remote FC changes are independent of hemodynamic mechanisms. Our data strongly implicate hemodynamic alterations as the main driver of local changes in measurements of FC in patients with glioma.

METHODS::Mutations in LZTR1, already known to be causal in familial schwannomatosis type 2, have been recently involved in a small proportion of patients with autosomal dominant and autosomal recessive Noonan syndrome. LZTR1 is also a driver gene in non syndromal glioblastoma. We report a 26-year-old patient with typical Noonan syndrome, and the dominantly transmitted c.850C > T (p.(Arg284Cys)) variant in LZTR1. An oligoastrocytoma was diagnosed in the patient at the age of 22 years; recurrence of the tumor occurred at age 26, as a ganglioblastoma. The patient had been transiently treated with growth hormone between ages 15 and 17. Considering the implication of LZTR1 in sporadic tumors of the nervous system, we hypothesize that gliomas are a possible complication of LZTR1-related Noonan syndrome. This report also supports a possible link between occurrence of a cerebral tumor in Noonan syndrome and a previous treatment with growth hormone.

METHODS:BACKGROUND:Susceptibility weighted imaging (SWI) provides vascular information and plays an important role in improving the diagnostic accuracy of preoperative glioma grading. Intratumoral susceptibility signal intensities (ITSS) obtained from SWI has been used in glioma grading. However, the current method for estimation of ITSS is semiquantitative, manual count-dependent, and includes hemorrhage as well as vasculature. PURPOSE:To develop a quantitative approach that calculates the vasculature volume within tumors by filtering out the hemorrhage from ITSS using R2 * values and connected component analysis-based segmentation algorithm; to evaluate the accuracy of the proposed ITSS vasculature volume (IVV) for differentiating various grades of glioma; and compare it with reported semiquantitative ITSS approach. STUDY TYPE:Retrospective. SUBJECTS:Histopathologically confirmed 41 grade IV, 19 grade III, and 15 grade II glioma patients.Field Strength/Sequence: SWI (four echoes: 5.6, 11.8, 18, 24.2 msec) along with conventional MRI sequences (T2 -weighted, T1 -weighted, 3D-fluid-attenuated inversion recovery [FLAIR], and diffusion-weighted imaging [DWI]) at 3.0T. ASSESSMENT:R2 * relaxation maps were calculated from multiecho SWI. The R2 * cutoff value for hemorrhage ITSS was determined. A segmentation algorithm was designed, based on this R2 * hemorrhage combined with connected component shape analysis, to quantify the IVV from all slices containing tumor by filtering out hemorrhages. Semiquantitative ITSS scoring as well as total ITSS volume (TIV) including hemorrhages were also calculated. STATISTICAL TESTS:One-way analysis of variance (ANOVA) and Tukey-Kramer post-hoc tests were performed to see the difference among the three grades of the tumor (II, III, and IV) in terms of semiquantitative ITSS scoring, TIV, and IVV. Receiver operating characteristic (ROC) curve analysis was used to evaluate the performance of the three methods individually in discriminating between grades of glioma. RESULTS:One-way ANOVA showed that only the proposed IVV significantly differentiated different grades of gliomas having visible ITSS. ROC analysis showed that IVV provided the highest AUC for the discrimination of grade II vs. III (0.93), grade III vs. IV (0.98), and grade II vs. IV glioma (0.94). IVV also provided the highest sensitivity and specificity for differentiating grade II vs. III (87.44, 98.41), grade III vs. IV (97.15, 94.12), and grade II vs. IV (98.72, 92.31). DATA CONCLUSION:The proposed quantitative method segregates hemorrhage from tumor vasculature. It scores above the existing semiquantitative method in terms of ITSS estimation and grading accuracy. LEVEL OF EVIDENCE:4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2020;51:225-233.