Title |
Dynamic contrast enhanced CT in nodule characterization: How we review and report
|
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Published in |
Cancer Imaging, July 2016
|
DOI | 10.1186/s40644-016-0074-4 |
Pubmed ID | |
Authors |
Nagmi R. Qureshi, Andrew Shah, Rosemary J. Eaton, Ken Miles, Fiona J. Gilbert, on behalf of the Sputnik investigators |
Abstract |
Incidental indeterminate solitary pulmonary nodules (SPN) that measure less than 3 cm in size are an increasingly common finding on computed tomography (CT) worldwide. Once identified there are a number of imaging strategies that can be performed to help with nodule characterization. These include interval CT, dynamic contrast enhanced computed tomography (DCE-CT), (18)F-fluorodeoxyglucose positron emission tomography-computed tomography ((18)F-FDG-PET-CT). To date the most cost effective and efficient non-invasive test or combination of tests for optimal nodule characterization has yet to be determined.DCE-CT is a functional test that involves the acquisition of a dynamic series of images of a nodule before and following the administration of intravenous iodinated contrast medium. This article provides an overview of the current indications and limitations of DCE- CT in nodule characterization and a systematic approach to how to perform, analyse and interpret a DCE-CT scan. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United Kingdom | 2 | 29% |
Cyprus | 1 | 14% |
United States | 1 | 14% |
Spain | 1 | 14% |
Unknown | 2 | 29% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 57% |
Practitioners (doctors, other healthcare professionals) | 2 | 29% |
Science communicators (journalists, bloggers, editors) | 1 | 14% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 46 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 9 | 20% |
Student > Master | 9 | 20% |
Student > Bachelor | 4 | 9% |
Professor | 3 | 7% |
Student > Postgraduate | 3 | 7% |
Other | 11 | 24% |
Unknown | 7 | 15% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 23 | 50% |
Computer Science | 3 | 7% |
Arts and Humanities | 2 | 4% |
Engineering | 2 | 4% |
Agricultural and Biological Sciences | 1 | 2% |
Other | 6 | 13% |
Unknown | 9 | 20% |