Yuhua Gu

TL;DR: "Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging, leading to a very large potential subject pool.

TL;DR: This study studies the reproducibility of quantitative imaging features that are used to describe tumor shape, size, and texture from computed tomography scans of non-small cell lung cancer (NSCLC).

TL;DR: Two CT features developed to quantitatively describe lung adenocarcinomas by scoring tumor shape complexity and intratumor density variation by scoring convexity and entropy ratio in routinely obtained diagnostic CT scans were found to be descriptive and demonstrated the link between imaging characteristics and patient survival.

TL;DR: Test–retest and correlation analyses have identified non-redundant CT image features with both high intra-patient reproducibility and inter-patient biological range, making the case that quantitative image features are informative and prognostic biomarkers for NSCLC.

TL;DR: A processing environment is described that integrates and automates data processing and analysis functions for imaging of proton metabolite distributions in the normal human brain, thereby allowing the formation of a database of MR‐measured human metabolite values as a function of acquisition, spatial and subject parameters.