3 research outputs found
Technical Challenges in the Clinical Application of Radiomics.
Radiomics is a quantitative approach to medical image analysis targeted at deciphering the morphologic and functional features of a lesion. Radiomic methods can be applied across various malignant conditions to identify tumor phenotype characteristics in the images that correlate with their likelihood of survival, as well as their association with the underlying biology. Identifying this set of characteristic features, called tumor signature, holds tremendous value in predicting the behavior and progression of cancer, which in turn has the potential to predict its response to various therapeutic options. We discuss the technical challenges encountered in the application of radiomics, in terms of methodology, workflow integration, and user experience, that need to be addressed to harness its true potential
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Decision-making Under Uncertainty: Interpretation and Use of Ranges Depicted in Bar Graphs by End Users and Test Users
Though people generally seem to be able to interpret data, they are subject to errors and biases that are influenced by the format of the graphical visualization itself. The current study sought to assess whether individuals could interpret bar graphs with min/max range bars, whether they could use this information to make decisions about watershed conservation plans, and whether test users (students) and end users (stakeholders) differed in their abilities to make these decisions. Both end users and test users were presented with a survey and asked to choose the best watershed conservation plan (out of two options) for each question. Users performed significantly above chance when interpreting graphical elements and making decisions in the presence of uncertainty. No significant differences were found between end users and test users. This suggests that users are able to interpret graphs and make optimal decisions using uncertainty information. It also suggests that test users may be sufficient proxies for end users, when end user testing is not feasible for this type of interface evaluation