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Background: Gadolinium-based contrast agents (GBCAs) used to enhance magnetic resonance imaging (MRI) scans have been linked to tissue deposition, including in the brain. The management of indolent tumors such as meningiomas requires frequent MRI scans to monitor for interval growth. Given concern regarding GBCA deposition, we sought to determine if noncontrast MRI scans in patients with asymptomatic meningiomas were equivalent to GBCA-enhanced MRI scans in surveillance monitoring., Methods: This institutional review board-approved retrospective chart review included 106 MRI sequences from 18 patients. Inclusion criteria were adult patients with asymptomatic meningiomas who received baseline contrast-enhanced and noncontrast axial brain MRI scan. Exclusion criteria included the following: 1) baseline or follow-up axial images were not available for review, 2) baseline scan was obtained without contrast, and 3) diagnosis of meningioma was uncertain. Percent tumor growth was measured by comparing cross-sectional area at maximum tumor diameter from the earliest and most recent scans. For each patient, change in tumor size over time was compared using T1 + contrast, T2, and T2 fluid-attenuated inversion recovery (FLAIR) sequences. These were compared with a qualitative consensus reading by a neurosurgeon and a neuroradiologist., Results: Measured change of greater than 10% was taken to represent tumor growth. In 17 out of 18 patients, measurement of noncontrast studies (T2 and T2 FLAIR) matched consensus. For one patient, imaging on T2 suggested 11% growth, whereas T2 FLAIR and overall consensus was stability., Conclusions: Our study provides evidence that noncontrasted MRI scans are equivalent to contrast-weighted MRI scans to follow change in tumor size over time in asymptomatic meningiomas., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Magnetic resonance-guided focused ultrasound (MRgFUS) has been used extensively to ablate brain tissue in movement disorders, such as essential tremor. At a lower energy, MRgFUS can disrupt the blood-brain barrier (BBB) to allow passage of drugs. This focal disruption of the BBB can target systemic medications to specific portions of the brain, such as for brain tumors. Current methods to bypass the BBB are invasive, as the BBB is relatively impermeable to systemically delivered antineoplastic agents. Multiple healthy and brain tumor animal models have suggested that MRgFUS disrupts the BBB and focally increases the concentration of systemically delivered antitumor chemotherapy, immunotherapy, and gene therapy. In animal tumor models, combining MRgFUS with systemic drug delivery increases median survival times and delays tumor progression. Liposomes, modified microbubbles, and magnetic nanoparticles, combined with MRgFUS, more effectively deliver chemotherapy to brain tumors. MRgFUS has great potential to enhance brain tumor drug delivery, while limiting treatment toxicity to the healthy brain.

PURPOSE: Brain metastasis is common in lung cancer, and treatment of brain metastasis can lead to significant morbidity. Although early detection of brain metastasis may improve outcomes, there are no prediction models to identify high-risk patients for brain magnetic resonance imaging (MRI) surveillance. Our goal is to develop a machine learning–based clinicogenomic prediction model to estimate patient-level brain metastasis risk. METHODS: A penalized regression competing risk model was developed using 330 patients diagnosed with lung cancer between January 2014 and June 2019 and followed through June 2021 at Stanford HealthCare. The main outcome was time from the diagnosis of distant metastatic disease to the development of brain metastasis, death, or censoring. RESULTS: Among the 330 patients, 84 (25%) developed brain metastasis over 627 person-years, with a 1-year cumulative brain metastasis incidence of 10.2% (95% CI, 6.8 to 13.6). Features selected for model inclusion were histology, cancer stage, age at diagnosis, primary site, and RB1 and ALK alterations. The prediction model yielded high discrimination (area under the curve 0.75). When the cohort was stratified by risk using a 1-year risk threshold of > 14.2% (85th percentile), the high-risk group had increased 1-year cumulative incidence of brain metastasis versus the low-risk group (30.8% v 6.1%, P <.01). Of 48 high-risk patients, 24 developed brain metastasis, and of these, 12 patients had brain metastasis detected more than 7 months after last brain MRI. Patients who missed this 7-month window had larger brain metastases (58% v 33% largest diameter > 10 mm; odds ratio, 2.80, CI, 0.51 to 13) versus those who had MRIs more frequently. CONCLUSION: The proposed model can identify high-risk patients, who may benefit from more intensive brain MRI surveillance to reduce morbidity of subsequent treatment through early detection. [ABSTRACT FROM AUTHOR]