A Preclinical Study on Radiomics-Driven Brain Tumor Prediction Using Deep Convolution Neural Network

Radiomics is an exponentially increasing discipline that focuses on mapping the textural details found in various tissues for medical diagnosis. Nevertheless, high-end GPUs, the method of producing Radiomics artifacts is practically infeasible but can take a long time with radiological representation for some higher order functionality like Gray-level Co-occurrence Matrix (GLCM). Researchers created RadSynth, a deep Convolutional Neural Network (CNN) framework that constructs Radiomics images efficiently. For simulation of GLCM uncertainty artifacts through post-contrast DCE-MRI, RadSynth has been investigated on a prostate cancer therapeutics market of seventy patients. When compared to conventional GLCM entropy images, RadSynth offered great computational uncertainty images. We conclude from this evaluation that both spatial distribution and optimization influence psychic distance estimation, and experimental results are less resilient to varying image resolution rather than varied optimization frequency.

Reproducibility of Experiments With Swirling Flow: Numerical Prediction With Polynomial Chaos

Achieving good reproducibility in fluid flow experiments can be challenging, in particular in scenarios where the experimental boundary conditions are obscure. We use computational uncertainty quantification (UQ) to evaluate the influence of uncertain inflow conditions on the reproducibility of experiments with swirling flow. Using a nonintrusive polynomial chaos method in combination with a computational fluid dynamics (CFD) code, we obtain the expectation and variance of the velocity fields downstream from symmetric and asymmetric swirl disturbance generators. Our results suggest that the flow patterns downstream from the asymmetric swirl disturbance generator are more reproducible than the flow patterns downstream from the symmetric swirl disturbance generator. This confirms that the inherent breaking of symmetry eliminates instability mechanisms in the wake of the disturber, thereby creating more stable swirling patterns that make the experiments more reproducible.