Magnetic resonance imaging
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Clinical imaging in positron emission tomography (PET) is often performed using single-time-point estimates of tracer uptake or static imaging that provides a spatial map of regional tracer concentration. However, dynamic tracer imaging can provide considerably more information about in vivo biology by delineating both the temporal and spatial pattern of tracer uptake. In addition, several potential sources of error that occur in static imaging can be mitigated. ⋯ Dynamic PET imaging output parameters, particularly transport (flow) and overall metabolic rate, have provided imaging end points for clinical trials at single-center institutions for years. However, dynamic imaging poses many challenges for multicenter clinical trial implementations from cross-center calibration to the inadequacy of a common informatics infrastructure. Underlying principles and methodology of PET dynamic imaging are first reviewed, followed by an examination of current approaches to dynamic PET image analysis with a specific case example of dynamic fluorothymidine imaging to illustrate the approach.
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The Seattle Cancer Care Alliance (SCCA) is a Pacific Northwest regional network that enables patients from community cancer centers to participate in multicenter oncology clinical trials where patients can receive some trial-related procedures at their local center. Results of positron emission tomography (PET) scans performed at community cancer centers are not currently used in SCCA Network trials since clinical trials customarily accept results from only trial-accredited PET imaging centers located at academic and large hospitals. Oncologists would prefer the option of using standard clinical PET scans from Network sites in multicenter clinical trials to increase accrual of patients for whom additional travel requirements for imaging are a barrier to recruitment. ⋯ Early experiences from both national and local PET phantom imaging trials are discussed, and next steps are proposed for including patient PET scans from the emerging regional quantitative imaging network in clinical trials. There are feasible methods to determine and characterize PET quantitation errors and improve data quality by either prospective scanner calibration or retrospective post hoc corrections. These methods should be developed and implemented in multicenter clinical trials employing quantitative PET imaging of patients.
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"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. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. ⋯ Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.