Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors

In recent years, there has been a trend of developing quantitative imaging features or texture features to characterize tumors for the purposes of diagnosis, disease classification, and treatment outcome prediction [1–8]. This kind of research is also known as “Radiomics,” a high-throughput extraction of quantitative imaging features from medical images to create mineable databases for prognostic analysis [9,10]. In Radiomics research, a large number of studies have focused on the texture features extracted from computed tomography (CT) images to predict the treatment outcomes of non-small cell lung cancer [1–3,11].

Source: Computerized Medical Imaging and Graphics - December 14, 2015 Category: Radiology Authors: Jinzhong Yang, Lifei Zhang, Xenia J. Fave, David V. Fried, Francesco C. Stingo, Chaan S. Ng, Laurence E. Court Source Type: research