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

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

Proceedings of the Cardiff University Engineering Research Conference 2023

The conference was established for the first time in 2023 as part of a programme to sustain the research culture, environment, and dissemination activities of the School of Engineering at Cardiff University in the United Kingdom. The conference served as a platform to celebrate advancements in various engineering domains researched at our School, explore and discuss further advancements in the diverse fields that define contemporary engineering

The research on mechanical properties and compressive behavior of graphene foam with multi-scale model?

Computational simulation is an effective method to study the deformation mechanism and mechanical behaviour of graphene-based porous materials. However, due to limitations in computational methods and costs, existing research model deviate significantly from the real material in terms of the scale of structure. Therefore, building a highly accurate computational model and maintaining an appropriate cost is both necessary and challenging. This paper proposed a multi-scale modelling approach for finite element (FE) analysis based on the concept of structural hierarchy. The stochastic feature of the microstructure of porous materials are also considered. The simulation results of the regular structure model and the Voronoi tessellation model are compared to investigate the effect of regularity on the material properties. Despite some shortcomings, other microstructural features of porous graphene materials can be gradually introduced to improve the material model step by step. Thus the developed multiscale model has great potential to simulate the properties of materials with mesoscopic size structure such as graphene foam (GF)