Title: Computational Diagnostics of GBM Tumors in the Era of Radiomics and Radiogenomics
Abstract: Machine learning (ML) integrated with medical imaging has introduced new perspectives in precision diagnostics of GBM tumors, through radiomics and radiogenomics. This has raised hopes for developing non-invasive and in-vivo biomarkers for prediction of patient survival, tumor recurrence, or molecular characterization, and therefore, encouraging treatments tailored to individualized needs. Characterization of tumor infiltration based on pre-operative multi-parametric magnetic resonance imaging (MP-MRI) scans would help in predicting the loci of future tumor recurrence, and thereby aiding in planning the course of treatment for the patients, such as increasing the resection or escalating the dose of radiation. Specifying molecular properties of GBM tumors and prediction of their changes over time and with treatment would help characterize the molecular heterogeneity of a tumor, and potentially use a respective combination treatment. In this article, we will provide examples of our work on radiomics and radiogenomics, aiming to offer personalized treatments to patients with GBM tumors.
Publication Year: 2021
Publication Date: 2021-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
Cited By Count: 2
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