monitoring-blood-brain-barrier-opening-rats-with-preclinical-focused

Monitoring Blood-Brain Barrier Opening in Rats with a Preclinical Focused Ultrasound System

The brain has a highly selective semipermeable blood barrier, termed the blood-brain barrier (BBB), which prevents the delivery of therapeutic ...

University of Texas Southwestern Medical Center

Isocitrate dehydrogenase (IDH) mutation status has emerged as an important prognostic marker in gliomas. Currently, reliable IDH mutation determination requires invasive surgical procedures. The purpose of this study was to develop a highly accurate, MRI-based, voxelwise deep-learning IDH classification network using T2-weighted (T2w) MR images and compare its performance to a multicontrast network.

A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas.

Although the physical and biologic principles of radiation therapy have remained relatively unchanged, a technologic renaissance has led to continuous and ever-changing growth in the field of radiation oncology. As a result, medical devices, techniques, and indications have changed considerably during the past 20-30 years. For example, advances in CT and MRI have revolutionized the treatment planning process for a variety of central nervous system diseases, including primary and metastatic tumors, vascular malformations, and inflammatory diseases. The resultant improved ability to delineate normal from abnormal tissue has enabled radiation oncologists to achieve more precise targeting and helped to mitigate treatment-related complications. Nevertheless, posttreatment complications still occur and can pose a diagnostic challenge for radiologists. These complications can be divided into acute, early-delayed, and late-delayed complications on the basis of the time that they manifest after radiation therapy and include leukoencephalopathy, vascular complications, and secondary neoplasms. The different irradiation technologies and applications of these technologies in the brain, current concepts used in treatment planning, and essential roles of the radiation oncologist in the setting of brain disease are reviewed. In addition, relevant imaging findings that can be used to delineate the extent of disease before treatment, and the expected posttreatment imaging changes are described. Common and uncommon complications related to radiation therapy and the associated imaging manifestations also are discussed. Familiarity with these entities may aid the radiologist in making the diagnosis and help guide appropriate management. RSNA, 2020.

Fundamentals of Radiation Oncology for Neurologic Imaging.

We developed a fully automated method for brain tumor segmentation using deep learning; 285 brain tumor cases with multiparametric magnetic resonance images from the BraTS2018 data set were used. We designed 3 separate 3D-Dense-UNets to simplify the complex multiclass segmentation problem into individual binary-segmentation problems for each subcomponent. We implemented a 3-fold cross-validation to generalize the network's performance. The mean cross-validation Dice-scores for whole tumor (WT), tumor core (TC), and enhancing tumor (ET) segmentations were 0.92, 0.84, and 0.80, respectively. We then retrained the individual binary-segmentation networks using 265 of the 285 cases, with 20 cases held-out for testing. We also tested the network on 46 cases from the BraTS2017 validation data set, 66 cases from the BraTS2018 validation data set, and 52 cases from an independent clinical data set. The average Dice-scores for WT, TC, and ET were 0.90, 0.84, and 0.80, respectively, on the 20 held-out testing cases. The average Dice-scores for WT, TC, and ET on the BraTS2017 validation data set, the BraTS2018 validation data set, and the clinical data set were as follows: 0.90, 0.80, and 0.78; 0.90, 0.82, and 0.80; and 0.85, 0.80, and 0.77, respectively. A fully automated deep learning method was developed to segment brain tumors into their subcomponents, which achieved high prediction accuracy on the BraTS data set and on the independent clinical data set. This method is promising for implementation into a clinical workflow.

A Fully Automated Deep Learning Network for Brain Tumor Segmentation.

One of the most important recent discoveries in brain glioma biology has been the identification of the isocitrate dehydrogenase (IDH) mutation and 1p/19q co-deletion status as markers for therapy and prognosis. 1p/19q co-deletion is the defining genomic marker for oligodendrogliomas and confers a better prognosis and treatment response than gliomas without it. Our group has previously developed a highly accurate deep-learning network for determining IDH mutation status using T2-weighted (T2w) MRI only. The purpose of this study was to develop a similar 1p/19q deep-learning classification network.

A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas.

Transient disruption of the blood-brain barrier (BBB) with focused ultrasound (FUS) is an emerging clinical method to facilitate targeted drug delivery to the brain. The focal noninvasive disruption of the BBB can be applied to promote the local delivery of hyperpolarized substrates. In this study, we investigated the effects of FUS on imaging brain metabolism using two hyperpolarized C-labeled substrates in rodents: [1-C]pyruvate and [1-C]glycerate. The BBB is a rate-limiting factor for pyruvate delivery to the brain, and glycerate minimally passes through the BBB. First, cerebral imaging with hyperpolarized [1-C]pyruvate resulted in an increase in total C signals ( = 0.05) after disrupting the BBB with FUS. Significantly higher levels of both [1-C]lactate (lactate/total C signals,  = 0.01) and [C]bicarbonate ( = 0.008) were detected in the FUS-applied brain region as compared to the contralateral FUS-unaffected normal-appearing brain region. The application of FUS without opening the BBB in a separate group of rodents resulted in comparable lactate and bicarbonate productions between the FUS-applied and the contralateral brain regions. Second, C imaging with hyperpolarized [1-C]glycerate after opening the BBB showed increased [1-C]glycerate delivery to the FUS-applied region ( = 0.04) relative to the contralateral side, and [1-C]lactate production was consistently detected from the FUS-applied region. Our findings suggest that FUS accelerates the delivery of hyperpolarized molecules across the BBB and provides enhanced sensitivity to detect metabolic products in the brain; therefore, hyperpolarized C imaging with FUS may provide new opportunities to study cerebral metabolic pathways as well as various neurological pathologies.

Probing Cerebral Metabolism with Hyperpolarized C Imaging after Opening the Blood-Brain Barrier with Focused Ultrasound.

The fields of both radiology and radiation oncology have evolved considerably in the past few decades, resulting in an increased ability to delineate between tumor and normal tissue to precisely target and treat vertebral metastases with radiation therapy. These scientific advances have also led to improvements in assessing treatment response and diagnosing toxic effects related to radiation treatment. However, despite technological innovations yielding greatly improved rates of palliative relief and local control of osseous spinal metastases, radiation therapy can still lead to a number of acute and delayed posttreatment complications. Treatment-related adverse effects may include pain flare, esophageal toxic effects, dermatitis, vertebral compression fracture, radiation myelopathy, and myositis, among others. The authors provide an overview of the multidisciplinary approach to the treatment of spinal metastases, indications for surgical management versus radiation therapy, various radiation technologies and techniques (along with their applications for spinal metastases), and current principles of treatment planning for conventional and stereotactic radiation treatment. Different radiologic criteria for assessment of treatment response, recent advances in radiologic imaging, and both common and rare complications related to spinal irradiation are also discussed, along with the imaging characteristics of various adverse effects. Familiarity with these topics will not only assist the diagnostic radiologist in assessing treatment response and diagnosing treatment-related complications but will also allow more effective collaboration between diagnostic radiologists and radiation oncologists to guide management decisions and ensure high-quality patient care. RSNA, 2021.

Fundamentals of Radiation Oncology for Treatment of Vertebral Metastases.

: Deep learning has shown promise for predicting the molecular profiles of gliomas using MR images. Prior to clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. The purpose of this study is to perform a preliminary evaluation on the effects of simulated motion artifact on glioma marker classifier performance and determine if motion correction can restore classification accuracies. : T2w images and molecular information were retrieved from the TCIA and TCGA databases. Simulated motion was added in the k-space domain along the phase encoding direction. Classifier performance for IDH mutation, 1p/19q co-deletion, and MGMT methylation was assessed over the range of 0% to 100% corrupted k-space lines. Rudimentary motion correction networks were trained on the motion-corrupted images. The performance of the three glioma marker classifiers was then evaluated on the motion-corrected images. : Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For isocitrate dehydrogenase (IDH) classification, 99% accuracy was achieved, exceeding the original performance of the network and representing a new benchmark in non-invasive MRI-based IDH classification. : Robust motion correction can facilitate highly accurate deep learning MRI-based molecular marker classification, rivaling invasive tissue-based characterization methods. Motion correction may be able to increase classification accuracy even in the absence of a visible artifact, representing a new strategy for boosting classifier performance.

Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

[This corrects the article DOI: 10.1117/1.JMI.9.1.016001.].

Errata: Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction.

The Archimedes spiral is a clinical tool that aids in the diagnosis and monitoring of essential tremor. However, spiral ratings may vary based on experience and training of the rating physician. This study sought to generate an objective standard model for tremor evaluation using convolutional neural networks. One senior movement disorders neurologist (Neurologist 1) with over 30 years of clinical experience used the Bain and Findley Spirography Rating Scale to rate 1653 Archimedes spiral images from 46 essential tremor patients (mild to severe tremor) and 75 control subjects (no to mild tremor). Neurologist 1's labels were used as the reference standard to train the model. After training the model, a randomly selected subset of spiral testing data was re-evaluated by Neurologist 1, by a second senior movement disorders neurologist (Neurologist 2) with over 27 years of clinical experience, and by our model. Cohen's Weighted Kappa 95% confidence intervals were calculated from all rater comparisons to determine if our model performs with the same proficiency as two senior movement disorders neurologists. The Cohen's Weighted Kappa 95% confidence intervals for the agreement between the reference standard scores and Neurologist 1's rerated scores, for the agreement between the reference standard scores and Neurologist 2's scores, and for the agreement between the reference standard scores and our model's scores were 0.93-0.98, 0.86-0.94, and 0.89-0.96, respectively. With overlapping Cohen's Weighted Kappa 95% confidence intervals for all agreement comparisons, we demonstrate that our model evaluates spirals with the same proficiency as two senior movement disorders neurologists.

ELectronic Archimedes spiral Neural Network (ELANN).

When millimeters matter.

Essential tremor (ET) is one of the most common movement disorders worldwide. In medically refractory ET, deep brain stimulation (DBS) of the ventral intermediate nucleus of the thalamus is the current standard of care. However, DBS carries an inherent 2% to 3% risk of hemorrhage, a risk that can be much higher in patients with concomitant coagulopathy. Magnetic resonance imaging-guided focused ultrasound (MRgFUS) thalamotomy is a surgical alternative that is highly effective in treating ET, with no reports of intracranial hemorrhage to date.

Magnetic resonance imaging-guided focused ultrasound thalamotomy for essential tremor in a patient with von Willebrand disease: perioperative optimization for patients with coagulopathies. Illustrative case.

Article	Year
A novel fully automated MRI-based deep-learning method for classification of IDH mutation status in brain gliomas. Neuro-oncology\| PubMed ID: 31637430	2020
Fundamentals of Radiation Oncology for Neurologic Imaging. Radiographics : a review publication of the Radiological Society of North America, Inc\| PubMed ID: 32216705	2020
A Fully Automated Deep Learning Network for Brain Tumor Segmentation. Tomography (Ann Arbor, Mich.)\| PubMed ID: 32548295	2020
A novel fully automated MRI-based deep-learning method for classification of 1p/19q co-deletion status in brain gliomas. Neuro-oncology advances\| PubMed ID: 32705083	2020
Probing Cerebral Metabolism with Hyperpolarized C Imaging after Opening the Blood-Brain Barrier with Focused Ultrasound. ACS chemical neuroscience\| PubMed ID: 34291630	2021
Fundamentals of Radiation Oncology for Treatment of Vertebral Metastases. Radiographics : a review publication of the Radiological Society of North America, Inc\| PubMed ID: 34623944	2021
Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction. Journal of medical imaging (Bellingham, Wash.)\| PubMed ID: 35118164	2022
Errata: Brain tumor IDH, 1p/19q, and MGMT molecular classification using MRI-based deep learning: an initial study on the effect of motion and motion correction. Journal of medical imaging (Bellingham, Wash.)\| PubMed ID: 36761698	2023
ELectronic Archimedes spiral Neural Network (ELANN). Parkinsonism & related disorders\| PubMed ID: 37683422	2023
When millimeters matter. European radiology\| PubMed ID: 38856783	2024
Magnetic resonance imaging-guided focused ultrasound thalamotomy for essential tremor in a patient with von Willebrand disease: perioperative optimization for patients with coagulopathies. I... Journal of neurosurgery. Case lessons\| PubMed ID: 38857545	2024

Bhavya R. Shah

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