Glioblastomas: Molecular Diagnosis and Pathology

Shan, Frank Y.; Zhao, Dan; Tirado, Carlos A; Fonkem, Ekokobe; Zhang, Yi-lu; Feng, Dongxia; Huang, Jason H.

doi:10.5772/intechopen.105472

Cited by 4 publications

(4 citation statements)

References 34 publications

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“…Most GBMs exhibit nuclear atypia, multiple mitotic figures, and a high degree of nuclear pleomorphism. Within the tumor, central necrosis is a signature of GBM, which are in yellow in histopathological images, with a peripheral grayish tumor mass [42]. The characteristic features match with our results, where the GBM group showed higher GLN values, which indicates less similarity in intensity value; a higher LGRE value indicating greater concentration of low gray-level value and a higher LRLGE value indicating a longer consecutive sequence of low gray-level value.…”

Section: The Value Of Glcm and Glrlm Image Features In Hande Imagessupporting

confidence: 87%

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

Cheung,

Wu,

et al. 2023

Cancers

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Background: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60–70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). Method: Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. Results: All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. Conclusion: In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.

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Section: The Value Of Glcm and Glrlm Image Features In Hande Imagessupporting

confidence: 87%

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

Cheung,

Wu,

et al. 2023

Cancers

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“…Following formalin fixation, GBMs become fragmented and soft, displaying a gray to pink border adjacent to the brain tissue [ 13 , 14 ]. Certain tumors also feature macroscopic cysts containing cloudy fluid, representing liquefied necrotic tumor material [ 14 , 15 ]. GBM exhibits extensive morphological variability, reflecting its historical term “glioblastoma multiforme” [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

“…Cellular pleomorphism encompasses a range of types such as small, undifferentiated, giant, epithelioid, spindled, gemistocytic, lipidized, and sarcomatoid cells. Some tumors may prominently display specific patterns, contributing to the characterization of distinct subtypes within the spectrum of GBMs [ 14 , 15 , 17 , 18 , 19 ]. The main cellular feature of malignant glial cells is local tissue invasion, which typically occurs along deep white matter tracts [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Revolutionizing Glioblastoma Treatment: A Comprehensive Overview of Modern Therapeutic Approaches

Sadowski,

Jażdżewska,

Kozłowski

et al. 2024

IJMS

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Glioblastoma is the most common malignant primary brain tumor in the adult population, with an average survival of 12.1 to 14.6 months. The standard treatment, combining surgery, radiotherapy, and chemotherapy, is not as efficient as we would like. However, the current possibilities are no longer limited to the standard therapies due to rapid advancements in biotechnology. New methods enable a more precise approach by targeting individual cells and antigens to overcome cancer. For the treatment of glioblastoma, these are gamma knife therapy, proton beam therapy, tumor-treating fields, EGFR and VEGF inhibitors, multiple RTKs inhibitors, and PI3K pathway inhibitors. In addition, the increasing understanding of the role of the immune system in tumorigenesis and the ability to identify tumor-specific antigens helped to develop immunotherapies targeting GBM and immune cells, including CAR-T, CAR-NK cells, dendritic cells, and immune checkpoint inhibitors. Each of the described methods has its advantages and disadvantages and faces problems, such as the inefficient crossing of the blood–brain barrier, various neurological and systemic side effects, and the escape mechanism of the tumor. This work aims to present the current modern treatments of glioblastoma.

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Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI

Wagner,

Ewald,

Freitag

et al. 2024

J Neurooncol

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Background Quantifying tumor growth and treatment response noninvasively poses a challenge to all experimental tumor models. The aim of our study was, to assess the value of quantitative and visual examination and radiomic feature analysis of high-resolution MR images of heterotopic glioblastoma xenografts in mice to determine tumor cell proliferation (TCP). Methods Human glioblastoma cells were injected subcutaneously into both flanks of immunodeficient mice and followed up on a 3 T MR scanner. Volumes and signal intensities were calculated. Visual assessment of the internal tumor structure was based on a scoring system. Radiomic feature analysis was performed using MaZda software. The results were correlated with histopathology and immunochemistry. Results 21 tumors in 14 animals were analyzed. The volumes of xenografts with high TCP (H-TCP) increased, whereas those with low TCP (L-TCP) or no TCP (N-TCP) continued to decrease over time (p < 0.05). A low intensity rim (rim sign) on unenhanced T1-weighted images provided the highest diagnostic accuracy at visual analysis for assessing H-TCP (p < 0.05). Applying radiomic feature analysis, wavelet transform parameters were best for distinguishing between H-TCP and L-TCP / N-TCP (p < 0.05). Conclusion Visual and radiomic feature analysis of the internal structure of heterotopically implanted glioblastomas provide reproducible and quantifiable results to predict the success of transplantation.

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Glioblastomas: Molecular Diagnosis and Pathology

Cited by 4 publications

References 34 publications

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

Revolutionizing Glioblastoma Treatment: A Comprehensive Overview of Modern Therapeutic Approaches

Radiomics and visual analysis for predicting success of transplantation of heterotopic glioblastoma in mice with MRI

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