scholarly journals Vascular phenotyping of brain tumors using magnetic resonance microscopy (μMRI)

2011 ◽  
Vol 31 (7) ◽  
pp. 1623-1636 ◽  
Author(s):  
Eugene Kim ◽  
Jiangyang Zhang ◽  
Karen Hong ◽  
Nicole E Benoit ◽  
Arvind P Pathak
Keyword(s):  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Three Dimensional ◽  
Region Of Interest ◽  
Tumor Model ◽  
Magnetic Resonance Microscopy ◽  
Vascular Phenotype ◽  
Brain Tumor Model ◽  
Novel Method

Abnormal vascular phenotypes have been implicated in neuropathologies ranging from Alzheimer's disease to brain tumors. The development of transgenic mouse models of such diseases has created a crucial need for characterizing the murine neurovasculature. Although histologic techniques are excellent for imaging the microvasculature at submicron resolutions, they offer only limited coverage. It is also challenging to reconstruct the three-dimensional (3D) vasculature and other structures, such as white matter tracts, after tissue sectioning. Here, we describe a novel method for 3D whole-brain mapping of the murine vasculature using magnetic resonance microscopy (μMRI), and its application to a preclinical brain tumor model. The 3D vascular architecture was characterized by six morphologic parameters: vessel length, vessel radius, microvessel density, length per unit volume, fractional blood volume, and tortuosity. Region-of-interest analysis showed significant differences in the vascular phenotype between the tumor and the contralateral brain, as well as between postinoculation day 12 and day 17 tumors. These results unequivocally show the feasibility of using μMRI to characterize the vascular phenotype of brain tumors. Finally, we show that combining these vascular data with coregistered images acquired with diffusion-weighted MRI provides a new tool for investigating the relationship between angiogenesis and concomitant changes in the brain tumor microenvironment.

scholarly journals Annexin A1-Binding Carbohydrate Mimetic Peptide Targets Drugs to Brain Tumors

2021 ◽  
Author(s):  
Michiko N. Fukuda ◽  
Misa Suzuki-Anekoji ◽  
Motohiro Nonaka
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
High Efficiency ◽  
Cytotoxic T Cells ◽  
Tumor Model ◽  
Tumor Vasculature ◽  
Cancer Drug ◽  
Annexin A1 ◽  
Mimetic Peptide ◽  
Brain Tumor Model

Annexin A1 (Anxa1) is expressed specifically on the surface of the tumor vasculature. Previously, we demonstrated that a carbohydrate-mimetic peptide, designated IF7, bound to the Anxa1 N-terminal domain. Moreover, intravenously injected IF7 targeted the tumor vasculature in mouse and crossed tumor endothelia cells to stroma via transcytosis. Thus, we hypothesized that IF7 could overcome the blood–brain barrier to reach brain tumors. Our studies in brain tumor model mice showed that IF7 conjugated with the anti-cancer drug SN38 suppressed brain tumor growth with high efficiency. Furthermore IF7-SN38-treated mice mounted an immune response to brain tumors established by injected tumor cells and shrank those tumors in part by recruiting cytotoxic T-cells to the injection site. These results suggest that Anxa1-binding peptide IF7 represents a drug delivery vehicle useful to treat malignant brain tumors. This chapter describes the unique development of IF7-SN38 as a potential breakthrough cancer chemotherapeutic.

Establishment of a brain-tumor model in adult monkeys

1985 ◽  
Vol 63 (6) ◽  
pp. 912-916 ◽  
Author(s):  
Kazuo Tabuchi ◽  
Akira Nishimoto ◽  
Kengo Matsumoto ◽  
Toru Satoh ◽  
Susumu Nakasone ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Cells ◽  
Japanese Monkey ◽  
Protein A ◽  
Tumor Model ◽  
Chromosome Analysis ◽  
Content Type ◽  
Contrast Enhanced Ct ◽  
Brain Tumor Model

✓ A brain-tumor model in adult monkeys may be significant because of the biological similarity to humans as well as the feasibility for surgical manipulation and for sequential computerized tomography (CT) scanning. In the present study, brain tumors were successfully produced in Japanese monkeys (Macaca fuscata), each weighing 2 to 10.8 kg, with an average age of 5.1 years old. Tumor cells were implanted by intracerebral inoculation of 4 × 107 chick embryo fibroblasts infected with the Schmidt-Ruppin strain of Rous sarcoma virus (RSV). With a 15- to 67-day latency, brain tumors were induced in 11 (73.3%) of 15 RSV-inoculated monkeys. Contrast-enhanced CT scans delineated all solitary intracerebral tumors greater than 4 to 6 mm in diameter. The CT images were proved at autopsy to be accurate within 2 mm in determining the size of tumor. Five of the 11 monkeys with intracerebral tumors died, with an average survival time of 26.6 days after RSV inoculation. The induced tumors were classified as either glioma or sarcoma by the presence or absence of glial fibrillary acidic protein (GFAP) and S-100 protein. A chromosome analysis of cultured tumor cells showed a diploid number of 42, indicating monkey origin. It is concluded that the reproducible brain tumor in the adult Japanese monkey inoculated with RSV can serve as a good experimental brain-tumor model for the further study of human malignant brain tumors.

Doxorubicin encapsulated in sterically stabilized liposomes for the treatment of a brain tumor model: biodistribution and therapeutic efficacy

1995 ◽  
Vol 83 (6) ◽  
pp. 1029-1037 ◽  
Author(s):  
Tali Siegal ◽  
Aviva Horowitz ◽  
Alberto Gabizon
Keyword(s):  
Single Dose ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Drug Exposure ◽  
Tumor Model ◽  
Drug Carriers ◽  
Tumor Inoculation ◽  
Drug Levels ◽  
Sterically Stabilized Liposomes ◽  
Brain Tumor Model

✓ Anthracyclines entrapped in small-sized, sterically stabilized liposomes have the advantage of long circulation time, reduced systemic toxicity, increased uptake into systemic tumors, and gradual release of their payload. To date, there is no information on the behavior of these liposomes in brain tumors. The objective of this study was to compare the biodistribution and clinical efficacy of free doxorubicin (F-DOX) and stealth liposome—encapsulated DOX (SL-DOX) in a secondary brain tumor model. Nine days after tumor inoculation Fischer rats with a right parietal malignant sarcoma received an intravenous dose of 6 mg/kg of either F-DOX or SL-DOX for evaluation of drug biodistribution. For therapeutic trials a single dose of 8 mg/kg was given 6 or 11 days after tumor induction, or alternatively, weekly doses (5 mg/kg) were given on Days 6,13, and 20. Liposome—encapsulated DOX was slowly cleared from plasma with a t1/2 of 35 hours. Free-DOX maximum tumor drug levels reached a mean value of 0.8 µg/g and were identical in the adjacent brain and contralateral hemisphere. In contrast, SL-DOX tumor levels were 14-fold higher at their peak levels at 48 hours, declining to ninefold increased levels at 120 hours. A gradual increase in drug levels in the brain adjacent to tumor was noted between 72 and 120 hours (up to 4 µg/g). High-performance liquid chromatography analysis identified a small amount of aglycone metabolites within the tumor mass from 96 hours and beyond, after SL-DOX injection. Cerebrospinal fluid levels were barely detectable in tumor-bearing rats treated with F-DOX up to 120 hours after drug injection (≥ 0.05 µg/ml), whereas the levels found after SL-DOX were 10- to 30-fold higher. An F-DOX single-dose treatment given 6 days after tumor inoculation increased the rats' life span (ILS) by 135% over controls (p < 0.05) but was not effective if given on Day 11. In contrast, SL-DOX treatment resulted in an ILS of 168% (p< 0.0003) with no difference when given after 6 or 11 days. Treatment with three weekly doses of SL-DOX produced an ILS of 189% compared to 126% by F-DOX (p < 0.0002). The authors conclude that the use of long-circulating liposomes as cytotoxic drug carriers in brain tumor results in enhanced drug exposure and improved therapeutic activity, with equal effectiveness against early small- and large-sized brain tumors.

Construction of the three-dimensional brain tumor model for the operative treatment simulation by 3-D active sphere

2003 ◽  
Author(s):  
Shunrou Fujiwara ◽  
Akio Doi ◽  
Kouichi Matsuda ◽  
Masashi Kameda ◽  
Takashi Inoue ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Operative Treatment ◽  
Three Dimensional ◽  
Tumor Model ◽  
Brain Tumor Model

scholarly journals ET-5 Biological effects of simultaneous use of multiple drugs in neutron capture therapy using rat brain tumor model

2021 ◽  
Vol 3 (Supplement_6) ◽  
pp. vi4-vi4
Author(s):  
Shinji Kawabata ◽  
Hideki Kashiwagi ◽  
Kohei Yoshimura ◽  
Yusuke Fukuo ◽  
Ryo Hiramatsu ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Rat Brain ◽  
Neutron Capture ◽  
Biological Effects ◽  
Tumor Model ◽  
Neutron Capture Therapy ◽  
Rat Brain Tumor ◽  
Brain Tumor Model ◽  
Multiple Drugs

Abstract The world’s first clinical trial of boron neutron capture therapy (BNCT), which treats malignant brain tumors with a single dose of neutron irradiation using multiple boron drugs simultaneously, was performed at our institution, and its excellent results have stimulated BNCT research around the world. BNCT is a particle irradiation therapy that biologically targets cancer cells, and is expected to be a “new option for cancer treatment” because it can deliver a dose of radiation at the cellular level. In the case of BNCT using a combination of multiple drugs, a method to appropriately consider the biological effects of the combination in the dose calculation has not been established. At present, BNCT based on an accelerator-based irradiation system and a boron drug (BPA) based on essential amino acids has been approved by the regulatory approval for head and neck cancer and has shown good results in brain tumors. As basic research, we have continued to develop new boron drugs, which will be essential in the future, and have explored the interpretation of the biological effects of multiple boron drugs in combination and the optimal conditions required for drug development. The survival curve of BNCT in a rat brain tumor model showed that the effect of the new drug alone was comparable to that of BPA, and the effect of the combination was improved, but the effect of the combination did not match the prediction of the combined biological effect derived from each drug. However, it has been found that the effect of the combination does not match the prediction based on the combination of biological effects derived from each drug. In other words, even if the equivalent X-ray equivalent dose (Gy-Eq) is calculated, the combined effect of some drugs exceeds the prediction, while the combined effect of other drugs is poor.

Improved treatment of a brain-tumor model

1983 ◽  
Vol 58 (3) ◽  
pp. 368-373 ◽  
Author(s):  
Massimo A. Gerosa ◽  
Dolores V. Dougherty ◽  
Charles B. Wilson ◽  
Mark L. Rosenblum
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Total Dose ◽  
Tumor Cells ◽  
Malignant Gliomas ◽  
Tumor Model ◽  
Content Type ◽  
Cell Kill ◽  
Brain Tumor Model ◽  
Anti Tumor Activity

✓ A combination chemotherapy regimen for brain tumors was developed, based on investigations of the survival of animals harboring the intracerebral 9L rat brain-tumor model and on analyses of their clonogenic tumor cells. Fischer 344 rats harboring 9L brain tumors were treated with 2-day courses of 5-fluorouracil (5-FU), in order to expose all cycling tumor cells to the drug during DNA synthesis and achieve maximum anti-tumor activity for this cell-cycle-specific anti-metabolite. Although a 74% cell kill was obtained for a total dose of 45 mg/kg or greater, animal life span was not increased over that of untreated tumor-bearing controls. However, when 5-FU (48 to 96 mg/kg total dose over 2 days) was administered after a single LD10 dose of BCNU (13.3 mg/kg), additive cell kill was suggested. In three large series, long-term animal survivors and occasional tumor cures were observed with this drug combination, a result never observed following BCNU alone. Schedule dependency was not apparent. A previously published protocol for treating recurrent malignant gliomas with sequential courses of BCNU and 5-FU was partially planned based upon these initial observations. Anti-tumor activity with the combination of drugs was superior to therapy with BCNU alone. Both animal and human studies confirm that, contrary to presently accepted oncological tenets, a chemotherapeutic agent that kills significant numbers of tumor cells but is clinically ineffective when given alone might, nevertheless, be useful in combination therapy regimens.

scholarly journals Differential Deep Convolutional Neural Network Model for Brain Tumor Classification

2021 ◽  
Vol 11 (3) ◽  
pp. 352
Author(s):  
Isselmou Abd El Kader ◽  
Guizhi Xu ◽  
Zhang Shuai ◽  
Sani Saminu ◽  
Imran Javaid ◽  
...  
Keyword(s):  
Neural Network ◽  
Magnetic Resonance Imaging ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Medical Image ◽  
Resonance Imaging ◽  
Feature Maps ◽  
Deep Cnn

The classification of brain tumors is a difficult task in the field of medical image analysis. Improving algorithms and machine learning technology helps radiologists to easily diagnose the tumor without surgical intervention. In recent years, deep learning techniques have made excellent progress in the field of medical image processing and analysis. However, there are many difficulties in classifying brain tumors using magnetic resonance imaging; first, the difficulty of brain structure and the intertwining of tissues in it; and secondly, the difficulty of classifying brain tumors due to the high density nature of the brain. We propose a differential deep convolutional neural network model (differential deep-CNN) to classify different types of brain tumor, including abnormal and normal magnetic resonance (MR) images. Using differential operators in the differential deep-CNN architecture, we derived the additional differential feature maps in the original CNN feature maps. The derivation process led to an improvement in the performance of the proposed approach in accordance with the results of the evaluation parameters used. The advantage of the differential deep-CNN model is an analysis of a pixel directional pattern of images using contrast calculations and its high ability to classify a large database of images with high accuracy and without technical problems. Therefore, the proposed approach gives an excellent overall performance. To test and train the performance of this model, we used a dataset consisting of 25,000 brain magnetic resonance imaging (MRI) images, which includes abnormal and normal images. The experimental results showed that the proposed model achieved an accuracy of 99.25%. This study demonstrates that the proposed differential deep-CNN model can be used to facilitate the automatic classification of brain tumors.

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