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Full-Text Articles in Medicine and Health Sciences
Deep Neural Network Analysis Of Pathology Images With Integrated Molecular Data For Enhanced Glioma Classification And Grading, Linmin Pei, Karra A. Jones, Zeina A. Shboul, James Y. Chen, Khan M. Iftekharuddin
Deep Neural Network Analysis Of Pathology Images With Integrated Molecular Data For Enhanced Glioma Classification And Grading, Linmin Pei, Karra A. Jones, Zeina A. Shboul, James Y. Chen, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel …
Efficacy Of Radiomics And Genomics In Predicting Tp53 Mutations In Diffuse Lower Grade Glioma, Zeina A. Shboul, Khan Iftekharuddin
Efficacy Of Radiomics And Genomics In Predicting Tp53 Mutations In Diffuse Lower Grade Glioma, Zeina A. Shboul, Khan Iftekharuddin
Electrical & Computer Engineering Faculty Publications
An updated classification of diffuse lower-grade gliomas is established in the 2016 World Health Organization Classification of Tumors of the Central Nervous System based on their molecular mutations such as TP53 mutation. This study investigates machine learning methods for TP53 mutation status prediction and classification using radiomics and genomics features, respectively. Radiomics features represent patients' age and imaging features that are extracted from conventional MRI. Genomics feature is represented by patients’ gene expression using RNA sequencing. This study uses a total of 105 LGG patients, where the patient dataset is divided into a training set (80 patients) and testing set …