Alexandra R. Cunliffe

TL;DR: The ability of radiomics to provide a quantitative, individualized measurement of patient lung tissue reaction to RT and assess RP development is demonstrated.

TL;DR: Addition of SUVSD to a single‐texture feature improves classifier performance on average, but the improvement is smaller in magnitude when SUVSD is added to an already effective classifier using texture alone.

TL;DR: This study examines the correlation between the radiologist-defined severity of normal tissue damage following radiation therapy (RT) for lung cancer treatment and a set of mathematical descriptors of computed tomography scan texture ('texture features').

TL;DR: Though registration altered the values of the majority of texture features, 19 features remained relatively stable after demons registration, indicating their potential for detecting pathologic change in serial CT scans and may allow for quantitative evaluation of local changes in lung tissue due to disease progression or treatment response.

TL;DR: Three demons registration-based methods to identify spatially matched regions in serial computed tomography (CT) scans for use in texture analysis were compared and the bias in feature value change between matched ROIs was minimized when feature values were calculated on original baseline and follow-up scans.