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}

}

@article{Perez-Cota2023-nu,

title = {Classification of cancer cells at the sub-cellular level by  phonon microscopy using deep learning},

author = {Fernando P\'{e}rez-Cota and Giovanna Mart\'{i}nez-Arellano and Salvatore 3rd La Cavera and William Hardiman and Luke Thornton and Rafael Fuentes-Dom\'{i}nguez and Richard J Smith and Alan McIntyre and Matt Clark},

year  = {2023},

date = {2023-09-01},

urldate = {2023-09-01},

journal = {Sci. Rep.},

volume = {13},

number = {1},

pages = {16228},

publisher = {Springer Science and Business Media LLC},

abstract = {There is a consensus about the strong correlation between the 

 elasticity of cells and tissue and their normal, dysplastic, and 

 cancerous states. However, developments in cell mechanics have 

 not seen significant progress in clinical applications. In this 

 work, we explore the possibility of using phonon acoustics for 

 this purpose. We used phonon microscopy to obtain a measure of 

 the elastic properties between cancerous and normal breast 

 cells. Utilising the raw time-resolved phonon-derived data (300 

 k individual inputs), we employed a deep learning technique to 

 differentiate between MDA-MB-231 and MCF10a cell lines. We 

 achieved a 93% accuracy using a single phonon measurement in a 

 volume of approximately 2.5 μm3. We also investigated means 

 for classification based on a physical model that suggest the 

 presence of unidentified mechanical markers. We have 

 successfully created a compact sensor design as a proof of 

 principle, demonstrating its compatibility for use with needles 

 and endoscopes, opening up exciting possibilities for future 

 applications.},

keywords = {Deep learning, Machine learning, microscopy, Phonon microscopy},

pubstate = {published},

tppubtype = {article}

}