Evaluation of radiomic analysis over the comparison of machine learning approach and radiomic risk score on glioblastoma

Duman, Abdulkerim, Powell, J, Thomas, S and Spezi, Emiliano ORCID: https://orcid.org/0000-0002-1452-8813 2024. Evaluation of radiomic analysis over the comparison of machine learning approach and radiomic risk score on glioblastoma. Presented at: Cardiff University Engineering Research Conference 2023, Cardiff, UK, 12-14 July 2023. Published in: Spezi, Emiliano and Bray, Michaela eds. Proceedings of the Cardiff University Engineering Research Conference 2023. Cardiff: Cardiff University Press, pp. 19-22. 10.18573/conf1.f

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Abstract

Accurate patient prognosis is important to provide an effective treatment plan for Glioblastoma (GBM) patients. Radiomics analysis extracts quantitative features from medical images. Such features can be used to build models to support medical decisions for diagnosis, prognosis, and therapeutic response. The progress of radiomics analysis is continuously improving. The aim of this research is to extract standardised radiomic features from MRI scans of GBM patients, perform feature selection, and compare radiomicbased risk score (RRS) and machine learning (ML) approaches for the risk stratification of GBM patients. We have also tested the generalisability of these models which is crucial for clinical implementation. Our work demonstrates that a stratification model based on logistic regression generalised better than the RRS method when applied to new unseen datasets.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Engineering
Subjects:	Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology > TA Engineering (General). Civil engineering (General)
Additional Information:	Contents are extended abstracts of papers, not full papers
Publisher:	Cardiff University Press
ISBN:	978-1-9116-5349-3
Date of First Compliant Deposit:	10 June 2024
Date of Acceptance:	2024
Last Modified:	26 Jul 2024 13:47
URI:	https://orca.cardiff.ac.uk/id/eprint/169670

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