Brigatinib in NF2-Related Schwannomatosis with Progressive Tumors.

BACKGROUND: NF2-related schwannomatosis (NF2-SWN, formerly called neurofibromatosis type 2) is a tumor predisposition syndrome that is manifested by multiple vestibular schwannomas, nonvestibular schwannomas, meningiomas, and ependymomas. The condition is relentlessly progressive with no approved therapies. On the basis of preclinical activity of brigatinib (an inhibitor of multiple tyrosine kinases) in NF2-driven nonvestibular schwannoma and meningioma, data were needed on the use of brigatinib in patients with multiple types of progressive NF2-SWN tumors. METHODS: In this phase 2 platform trial with a basket design, patients who were 12 years of age or older with NF2-SWN and progressive tumors were treated with oral brigatinib at a dose of 180 mg daily. A central review committee evaluated one target tumor and up to five nontarget tumors in each patient. The primary outcome was radiographic response in target tumors. Key secondary outcomes were safety, response rate in all tumors, hearing response, and patient-reported outcomes. RESULTS: A total of 40 patients (median age, 26 years) with progressive target tumors (10 vestibular schwannomas, 8 nonvestibular schwannomas, 20 meningiomas, and 2 ependymomas) received treatment with brigatinib. After a median follow-up of 10.4 months, the percentage of tumors with a radiographic response was 10% (95% confidence interval [CI], 3 to 24) for target tumors and 23% (95% CI, 16 to 30) for all tumors; meningiomas and nonvestibular schwannomas had the greatest benefit. Annualized growth rates decreased for all tumor types during treatment. Hearing improvement occurred in 35% (95% CI, 20 to 53) of eligible ears. Exploratory analyses suggested a decrease in self-reported pain severity during treatment (-0.013 units per month; 95% CI, -0.002 to -0.029) on a scale from 0 (no pain) to 3 (severe pain). No grade 4 or 5 treatment-related adverse events were reported. CONCLUSIONS: Brigatinib treatment resulted in radiographic responses in multiple tumor types and clinical benefit in a heavily pretreated cohort of patients with NF2-SWN. (Funded by the Children's Tumor Foundation and others; INTUITT-NF2 ClinicalTrials.gov number, NCT04374305.).

Pseudo-Resting-State Functional MRI Derived from Dynamic Susceptibility Contrast Perfusion MRI Can Predict Cognitive Impairment in Glioma.

BACKGROUND AND PURPOSE: Resting-state functional MRI (rs-fMRI) can be used to estimate functional connectivity (FC) between different brain regions, which may be of value for identifying cognitive impairment in patients with brain tumors. Unfortunately, neither rs-fMRI nor neurocognitive assessments are routinely assessed clinically, mostly due to limitations in examination time and cost. Since DSC perfusion MRI is often used clinically to assess tumor vascularity and similarly uses a gradient-echo-EPI sequence for T2*-sensitivity, we theorized a "pseudo-rs-fMRI" signal could be derived from DSC perfusion to simultaneously quantify FC and perfusion metrics, and these metrics can be used to estimate cognitive impairment in patients with brain tumors. MATERIALS AND METHODS: Twenty-four consecutive patients with gliomas were enrolled in a prospective study that included DSC perfusion MRI, resting-sate functional MRI (rs-fMRI), and neurocognitive assessment. Voxelwise modeling of contrast bolus dynamics during DSC acquisition was performed and then subtracted from the original signal to generate a residual "pseudo-rs-fMRI" signal. Following the preprocessing of pseudo-rs-fMRI, full rs-fMRI, and a truncated version of the full rs-fMRI (first 100 timepoints) data, the default mode, motor, and language network maps were generated with atlas-based ROIs, Dice scores were calculated for the resting-state network maps from pseudo-rs-fMRI and truncated rs-fMRI using the full rs-fMRI maps as reference. Seed-to-voxel and ROI-to-ROI analyses were performed to assess FC differences between cognitively impaired and nonimpaired patients. RESULTS: Dice scores for the group-level and patient-level (mean±SD) default mode, motor, and language network maps using pseudo-rs-fMRI were 0.905/0.689 ± 0.118 (group/patient), 0.973/0.730 ± 0.124, and 0.935/0.665 ± 0.142, respectively. There was no significant difference in Dice scores between pseudo-rs-fMRI and the truncated rs-fMRI default mode (P = .97) or language networks (P = .30), but there was a difference in motor networks (P = .02). A multiple logistic regression classifier applied to ROI-to-ROI FC networks using pseudo-rs-fMRI could identify cognitively impaired patients (sensitivity = 84.6%, specificity = 63.6%, receiver operating characteristic area under the curve (AUC) = 0.7762 ± 0.0954 (standard error), P = .0221) and performance was not significantly different from full rs-fMRI predictions (AUC = 0.8881 ± 0.0733 (standard error), P = .0013, P = .29 compared with pseudo-rs-fMRI). CONCLUSIONS: DSC perfusion MRI-derived pseudo-rs-fMRI data can be used to perform typical rs-fMRI FC analyses that may identify cognitive decline in patients with brain tumors while still simultaneously performing perfusion analyses.

Digital "flipbooks" for enhanced visual assessment of simple and complex brain tumors.

Typical longitudinal radiographic assessment of brain tumors relies on side-by-side qualitative visualization of serial magnetic resonance images (MRIs) aided by quantitative measurements of tumor size. However, when assessing slowly-growing tumors and/or complex tumors, side-by-side visualization and quantification may be difficult or unreliable. Whole-brain, patient-specific "digital flipbooks" of longitudinal scans are a potential method to augment radiographic side-by-side reads in clinical settings by enhancing the visual perception of changes in tumor size, mass effect, and infiltration across multiple slices over time. In this approach, co-registered, consecutive MRI scans are displayed in a slide deck, where one slide displays multiple brain slices of a single timepoint in an array (e.g. 3x5 "mosaic" view of slices). The flipbooks are viewed similar to an animated flipbook of cartoons/photos so that subtle radiographic changes are visualized via perceived motion when scrolling through the slides. Importantly, flipbooks can be created easily with free, open-source software. This article describes the step-by-step methodology for creating flipbooks and discusses clinical scenarios for which flipbooks are particularly useful. Example flipbooks are provided in the Online Supplemental Material.

Quantification of T2-FLAIR Mismatch in Nonenhancing Diffuse Gliomas Using Digital Subtraction.

BACKGROUND AND PURPOSE: The T2-FLAIR mismatch sign on MR imaging is a highly specific imaging biomarker of isocitrate dehydrogenase (IDH)-mutant astrocytomas, which lack 1p/19q codeletion. However, most studies using the T2-FLAIR mismatch sign have used visual assessment. This study quantified the degree of T2-FLAIR mismatch using digital subtraction of fluid-nulled T2-weighted FLAIR images from non-fluid-nulled T2-weighted images in human nonenhancing diffuse gliomas and then used this information to assess improvements in diagnostic performance and investigate subregion characteristics within these lesions. MATERIALS AND METHODS: Two cohorts of treatment-naïve, nonenhancing gliomas with known IDH and 1p/19q status were studied (n = 71 from The Cancer Imaging Archive (TCIA) and n = 34 in the institutional cohort). 3D volumes of interest corresponding to the tumor were segmented, and digital subtraction maps of T2-weighted MR imaging minus T2-weighted FLAIR MR imaging were used to partition each volume of interest into a T2-FLAIR mismatched subregion (T2-FLAIR mismatch, corresponding to voxels with positive values on the subtraction maps) and nonmismatched subregion (T2-FLAIR nonmismatch corresponding to voxels with negative values on the subtraction maps). Tumor subregion volumes, percentage of T2-FLAIR mismatch volume, and T2-FLAIR nonmismatch subregion thickness were calculated, and 2 radiologists assessed the T2-FLAIR mismatch sign with and without the aid of T2-FLAIR subtraction maps. RESULTS: Thresholds of ≥42% T2-FLAIR mismatch volume classified IDH-mutant astrocytoma with a specificity/sensitivity of 100%/19.6% (TCIA) and 100%/31.6% (institutional); ≥25% T2-FLAIR mismatch volume showed 92.0%/32.6% and 100%/63.2% specificity/sensitivity, and ≥15% T2-FLAIR mismatch volume showed 88.0%/39.1% and 93.3%/79.0% specificity/sensitivity. In IDH-mutant astrocytomas with ≥15% T2-FLAIR mismatch volume, T2-FLAIR nonmismatch subregion thickness was negatively correlated with the percentage T2-FLAIR mismatch volume (P < .0001) across both cohorts. The percentage T2-FLAIR mismatch volume was higher in grades 3-4 compared with grade 2 IDH-mutant astrocytomas (P < .05), and ≥15% T2-FLAIR mismatch volume IDH-mutant astrocytomas were significantly larger than <15% T2-FLAIR mismatch volume IDH-mutant astrocytoma (P < .05) across both cohorts. When evaluated by 2 radiologists, the additional use of T2-FLAIR subtraction maps did not show a significant difference in interreader agreement, sensitivity, or specificity compared with a separate evaluation of T2-FLAIR and T2-weighted MR imaging alone. CONCLUSIONS: T2-FLAIR digital subtraction maps may be a useful, automated tool to obtain objective segmentations of tumor subregions based on quantitative thresholds for classifying IDH-mutant astrocytomas using the percentage T2 FLAIR mismatch volume with 100% specificity and exploring T2-FLAIR mismatch/T2-FLAIR nonmismatch subregion characteristics. Conversely, the addition of T2-FLAIR subtraction maps did not enhance the sensitivity or specificity of the visual T2-FLAIR mismatch sign assessment by experienced radiologists.