Immune Connection in Glioma: Fiction, Fact and Option

Ghosh, Anirban

doi:10.5772/22153

Cited by 2 publications

(3 citation statements)

References 96 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A dECM maintains the organ- or tumor-specific biochemical profile of the original ECM. In a pioneering study, decellularized patient-derived brain tissue mirroring the brain-specific ECM microenvironment was used to create an in vitro GBM model. , The ECMs of brain tissues contain high amounts of HA, glycosaminoglycans (GAGs), and proteoglycans, but lack fibrous collagen; patient-derived ECMs (pdECMs) would thus provide biochemical cues differing from those of other tissues. Patient-derived glioma cells (pdGCs) were embedded in pdECM/collagen hydrogels (10:1 v/v) to establish GBM-specific tumor models.…”

Section: Decellularized Ecmmentioning

confidence: 99%

Extracellular Matrix-Based Hydrogels to Tailoring Tumor Organoids

Lee

Mun

Pham

et al. 2021

ACS Biomater. Sci. Eng.

View full text Add to dashboard Cite

When embedded into a three-dimensional (3D) matrix, cancer stem cells (or cancer-initiating cells) can grow into self-organizing organotypic structures called tumor organoids. During organoid formation, the matrix not only provides structural support but also delivers biochemical signals. Although increasing evidence indicates that the extracellular matrix (ECM) is an essential component of the tumor microenvironment during tumor development and progression, the influence of the ECM on organoid formation has been largely ignored; the ECM has only recently been recognized to play a role in the regulation of cancer cell phenotypes. We reviewed ECM-based hydrogels to tailoring tumor organoids and highlight the potential role of the ECM in the development of recapitulating malignant/invasive tumor organoids with enhanced capacity for in vitro representation of ECM-regulated tumor progression.

show abstract

Section: Decellularized Ecmmentioning

confidence: 99%

Extracellular Matrix-Based Hydrogels to Tailoring Tumor Organoids

Lee

Mun

Pham

et al. 2021

ACS Biomater. Sci. Eng.

View full text Add to dashboard Cite

show abstract

“…One of the biggest obstacles to the use of artificial intelligence techniques in clinical radiology practice is the generalization of models to various datasets. 13 To our understanding, there haven’t been many prior reports involving extensive MGMT methylation datasets and outside validation. Our study’s objective was to test whether the largest dataset (n = 985) could be used to forecast the methylation status of the MGMT promoter using mpMRI.…”

Section: Related Workmentioning

confidence: 99%

“…The most widely used assays for determining MGMT status, define the requirements for standardized testing, and assess the causes for test repeatability issues. 13 MGMT promoter methylation status is being employed as an essential stratification or selection criterion in ongoing clinical studies, despite the fact that treatment decisions in the regular context are not yet based on this marker. 14 Despite the potential benefits of tumor genetic biomarker testing, price and the requirement for direct tissue sample remain barriers to its broad clinical usage.…”

Section: Introductionmentioning

confidence: 99%

Brain Tumor Radiogenomic Classification of O⁶-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning

Sakly

Saïd²,

Seekins

et al. 2023

Cancer Control

View full text Add to dashboard Cite

Artificial Intelligence (AI) is the subject of a challenge and attention in the field of oncology and raises many promises for preventive diagnosis, but also fears, some of which are based on highly speculative visions for the classification and detection of tumors. A brain tumor that is malignant is a life-threatening disorder. Glioblastoma is the most prevalent kind of adult brain cancer and the 1 with the poorest prognosis, with a median survival time of less than a year. The presence of O6 -methylguanine-DNA methyltransferase (MGMT) promoter methylation, a particular genetic sequence seen in tumors, has been proven to be a positive prognostic indicator and a significant predictor of recurrence. This strong revival of interest in AI is modeled in particular to major technological advances which have significantly increased the performance of the predicted model for medical decision support. Establishing reliable forecasts remains a significant challenge for electronic health records (EHRs). By enhancing clinical practice, precision medicine promises to improve healthcare delivery. The goal is to produce improved prognosis, diagnosis, and therapy through evidence-based sub stratification of patients, transforming established clinical pathways to optimize care for each patient’s individual requirements. The abundance of today’s healthcare data, dubbed “big data,” provides great resources for new knowledge discovery, potentially advancing precision treatment. The latter necessitates multidisciplinary initiatives that will use the knowledge, skills, and medical data of newly established organizations with diverse backgrounds and expertise. The aim of this paper is to use magnetic resonance imaging (MRI) images to train and evaluate your model to detect the presence of MGMT promoter methylation in this competition to predict the genetic subtype of glioblastoma based transfer learning. Our objective is to emphasize the basic problems in the developing disciplines of radiomics and radiogenomics, as well as to illustrate the computational challenges from the perspective of big data analytics.

show abstract

Immune Connection in Glioma: Fiction, Fact and Option

Cited by 2 publications

References 96 publications

Extracellular Matrix-Based Hydrogels to Tailoring Tumor Organoids

Extracellular Matrix-Based Hydrogels to Tailoring Tumor Organoids

Brain Tumor Radiogenomic Classification of O⁶-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning

Contact Info

Product

Resources

About

Immune Connection in Glioma: Fiction, Fact and Option

Cited by 2 publications

References 96 publications

Extracellular Matrix-Based Hydrogels to Tailoring Tumor Organoids

Extracellular Matrix-Based Hydrogels to Tailoring Tumor Organoids

Brain Tumor Radiogenomic Classification of O6-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning

Contact Info

Product

Resources

About

Brain Tumor Radiogenomic Classification of O⁶-Methylguanine-DNA Methyltransferase Promoter Methylation in Malignant Gliomas-Based Transfer Learning