Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes

• URL: http://arxiv.org/abs/2010.14068v1
• Date: Tue, 27 Oct 2020 05:31:56 GMT
• Title: Radiogenomics of Glioblastoma: Identification of Radiomics associated with Molecular Subtypes
• Authors: Navodini Wijethilake, Mobarakol Islam, Dulani Meedeniya, Charith Chitraranjan, Indika Perera, Hongliang Ren
• Abstract summary: Glioblastoma is the most malignant type of central nervous system tumor. Subtypes of GBM are predicted with an average accuracy of 79% utilizing radiomics and accuracy over 90% utilizing gene expression profiles.
• Score: 13.21715837712657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Glioblastoma is the most malignant type of central nervous system tumor with GBM subtypes cleaved based on molecular level gene alterations. These alterations are also happened to affect the histology. Thus, it can cause visible changes in images, such as enhancement and edema development. In this study, we extract intensity, volume, and texture features from the tumor subregions to identify the correlations with gene expression features and overall survival. Consequently, we utilize the radiomics to find associations with the subtypes of glioblastoma. Accordingly, the fractal dimensions of the whole tumor, tumor core, and necrosis regions show a significant difference between the Proneural, Classical and Mesenchymal subtypes. Additionally, the subtypes of GBM are predicted with an average accuracy of 79% utilizing radiomics and accuracy over 90% utilizing gene expression profiles.

Related papers

• Towards Generalizable Tumor Synthesis [48.45704270448412]
  Tumor synthesis enables the creation of artificial tumors in medical images, facilitating the training of AI models for tumor detection and segmentation. This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation. We have ascertained that generative AI models, e.g., Diffusion Models, can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ.
  arXiv  Detail & Related papers  (2024-02-29T18:57:39Z)
• Quantifying intra-tumoral genetic heterogeneity of glioblastoma toward precision medicine using MRI and a data-inclusive machine learning algorithm [3.2507684591996036]
  Glioblastoma (GBM) is one of the most aggressive and lethal human cancers. Biopsy is invasive, which motivates the development of non-invasive, MRI-based machine learning (ML) models. We proposed a novel Weakly Supervised Ordinal Support Vector Machine (WSO-SVM) to predict regional genetic alteration status within each GBM tumor using MRI.
  arXiv  Detail & Related papers  (2023-12-30T03:28:51Z)
• Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review [77.34726150561087]
  Cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods.
  arXiv  Detail & Related papers  (2023-01-28T15:03:03Z)
• Novel Local Radiomic Bayesian Classifiers for Non-Invasive Prediction of MGMT Methylation Status in Glioblastoma [0.0]
  Expression of the O6-methylguanine-DNA-methyltransferase (MGMT) gene in glioblastoma tumor tissue is of clinical importance. Currently, MGMT methylation is determined through an invasive brain biopsy and subsequent genetic analysis of the extracted tumor tissue. We present novel Bayesian classifiers that make probabilistic predictions of MGMT methylation status based on radiomic features extracted from FLAIR-sequence magnetic resonance imagery (MRIs)
  arXiv  Detail & Related papers  (2021-11-30T04:53:23Z)
• MAG-Net: Mutli-task attention guided network for brain tumor segmentation and classification [0.9176056742068814]
  This paper proposes multi-task attention guided encoder-decoder network (MAG-Net) to classify and segment the brain tumor regions using MRI images. The model achieved promising results as compared to existing state-of-the-art models.
  arXiv  Detail & Related papers  (2021-07-26T16:51:00Z)
• Learned super resolution ultrasound for improved breast lesion characterization [52.77024349608834]
  Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs.
  arXiv  Detail & Related papers  (2021-07-12T09:04:20Z)
• Radiomic Deformation and Textural Heterogeneity (R-DepTH) Descriptor to characterize Tumor Field Effect: Application to Survival Prediction in Glioblastoma [2.1916334019121537]
  The concept of tumor field effect implies that cancer is a systemic disease with its impact way beyond the visible tumor confines. We present an integrated MRI-based descriptor, radiomic-Deformation and Textural Heterogeneity (r-DepTH) This descriptor comprises measurements of the subtle perturbations in tissue deformations throughout the surrounding normal parenchyma due to mass effect.
  arXiv  Detail & Related papers  (2021-03-12T17:38:54Z)
• Cancer Gene Profiling through Unsupervised Discovery [49.28556294619424]
  We introduce a novel, automatic and unsupervised framework to discover low-dimensional gene biomarkers. Our method is based on the LP-Stability algorithm, a high dimensional center-based unsupervised clustering algorithm. Our signature reports promising results on distinguishing immune inflammatory and immune desert tumors.
  arXiv  Detail & Related papers  (2021-02-11T09:04:45Z)
• Comparison of Machine Learning Classifiers to Predict Patient Survival and Genetics of GBM: Towards a Standardized Model for Clinical Implementation [44.02622933605018]
  Radiomic models have been shown to outperform clinical data for outcome prediction in glioblastoma (GBM) We aimed to compare nine machine learning classifiers to predict overall survival (OS), isocitrate dehydrogenase (IDH) mutation, O-6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation, epidermal growth factor receptor (EGFR) VII amplification and Ki-67 expression in GBM patients. xGB obtained maximum accuracy for OS (74.5%), AB for IDH mutation (88%), MGMT methylation (71,7%), Ki-67 expression (86,6%), and EGFR amplification (81,
  arXiv  Detail & Related papers  (2021-02-10T15:10:37Z)
• Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression-morphology analysis in breast cancer [1.3758771225117674]
  We report the first transcriptome-wide Expression-MOrphology (EMO) analysis in breast cancer. Gene-specific models were optimised and validated for prediction of mRNA expression. Predictions for 9,334 genes were significantly associated with RNA-sequencing estimates.
  arXiv  Detail & Related papers  (2020-09-18T16:27:53Z)
• Segmentation for Classification of Screening Pancreatic Neuroendocrine Tumors [72.65802386845002]
  This work presents comprehensive results to detect in the early stage the pancreatic neuroendocrine tumors (PNETs) in abdominal CT scans. To the best of our knowledge, this task has not been studied before as a computational task. Our approach outperforms state-of-the-art segmentation networks and achieves a sensitivity of $89.47%$ at a specificity of $81.08%$.
  arXiv  Detail & Related papers  (2020-04-04T21:21:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.