Radiology
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To investigate the extent of bias in a clinical study involving "pothole analysis" of diffusion-tensor imaging (DTI) data used to quantify white matter lesion load in diseases with a heterogeneous spatial distribution of pathologic findings, such as mild traumatic brain injury (TBI), and create a mathematical model of the bias. ⋯ The pothole approach to neuroimaging data may introduce bias, which can be minimized by independent training and test groups or cross-validation methods. This bias is sufficient to call into question the previously reported diagnostic performance of DTI for mild TBI.
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To determine the feasibility of using a computer-aided diagnosis (CAD) system to differentiate among triple-negative breast cancer, estrogen receptor (ER)-positive cancer, human epidermal growth factor receptor type 2 (HER2)-positive cancer, and benign fibroadenoma lesions on dynamic contrast material-enhanced (DCE) magnetic resonance (MR) images. ⋯ Triple-negative cancers possess certain characteristic features on DCE MR images that can be captured and quantified with CAD, enabling good discrimination of triple-negative cancers from non-triple-negative cancers, as well as between triple-negative cancers and benign fibroadenomas. Such CAD algorithms may provide added diagnostic benefit in identifying the highly aggressive triple-negative cancer phenotype with DCE MR imaging in high-risk women.
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Inverse probability weighting is a propensity score-based technique that can be used to compensate for imbalance in study groups. It is an alternative to regression-based adjustment of the outcomes. ⋯ However, it can create artificial imbalance unless it is applied carefully. In this article, an introduction to inverse probability weighting is provided by using the Yang et al study published in this issue of Radiology as an example.
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To determine the feasibility of whole-body diffusion-weighted (DW) magnetic resonance (MR) imaging for assessment of treatment response in myeloma. ⋯ Preliminary work demonstrates whole-body DW imaging is a repeatable, quantifiable technique for assessment of treatment response in myeloma.