Publications

@article{Sanchez2024-rv,

title = {Radiogenomics as an integrated approach to glioblastoma  precision medicine},

author = {Isabella Sanchez and Ruman Rahman},

doi = {10.1007/s11912-024-01580-z},

year  = {2024},

date = {2024-10-01},

urldate = {2024-10-01},

journal = {Curr. Oncol. Rep.},

volume = {26},

number = {10},

pages = {1213\textendash1222},

publisher = {Springer Science and Business Media LLC},

abstract = {PURPOSE OF REVIEW: Isocitrate dehydrogenase wild-type 

 glioblastoma is the most aggressive primary brain tumour in 

 adults. Its infiltrative nature and heterogeneity confer a 

 dismal prognosis, despite multimodal treatment. Precision 

 medicine is increasingly advocated to improve survival rates in 

 glioblastoma management; however, conventional neuroimaging 

 techniques are insufficient in providing the detail required for 

 accurate diagnosis of this complex condition. RECENT FINDINGS: 

 Advanced magnetic resonance imaging allows more comprehensive 

 understanding of the tumour microenvironment. Combining 

 diffusion and perfusion magnetic resonance imaging to create a 

 multiparametric scan enhances diagnostic power and can overcome 

 the unreliability of tumour characterisation by standard 

 imaging. Recent progress in deep learning algorithms establishes 

 their remarkable ability in image-recognition tasks. Integrating 

 these with multiparametric scans could transform the diagnosis 

 and monitoring of patients by ensuring that the entire tumour is 

 captured. As a corollary, radiomics has emerged as a powerful 

 approach to offer insights into diagnosis, prognosis, treatment, 

 and tumour response through extraction of information from 

 radiological scans, and transformation of these tumour 

 characteristics into quantitative data. Radiogenomics, which 

 links imaging features with genomic profiles, has exhibited its 

 ability in characterising glioblastoma, and determining 

 therapeutic response, with the potential to revolutionise 

 management of glioblastoma. The integration of deep learning 

 algorithms into radiogenomic models has established an 

 automated, highly reproducible means to predict glioblastoma 

 molecular signatures, further aiding prognosis and targeted 

 therapy. However, challenges including lack of large cohorts, 

 absence of standardised guidelines and the \'black-box\' nature of 

 deep learning algorithms, must first be overcome before this 

 workflow can be applied in clinical practice.},

keywords = {Deep learning, Glioblastoma, Neuroimaging, Precision medicine, Radiogenomics, Radiomics},

pubstate = {published},

tppubtype = {article}

}