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Possibility of determining the degree of adhesion of the lymph node to the pulmonary artery preoperatively

Overview of attention for article published in Journal of Cardiothoracic Surgery, July 2015
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Title
Possibility of determining the degree of adhesion of the lymph node to the pulmonary artery preoperatively
Published in
Journal of Cardiothoracic Surgery, July 2015
DOI 10.1186/s13019-015-0310-1
Pubmed ID
Authors

Hidetaka Uramoto, Satoshi Nozu, Yuki Nakajima, Hiroyasu Kinoshita

Abstract

To make a preoperative determination of the degree of adhesion of the lymph node (LN) to the pulmonary artery (PA) in patients with lung cancer. We investigated clinical parameters, including sex, age, smoking, stage, histology and surgical procedure, and performed an image analysis using CT scanning. The data for sex, age, smoking, stage, histology and the surgical procedure were identical between the "adhesion" and "no adhesion" groups. However, three of the five analyzable cases in the adhesion group clearly showed the disappearance of the fat plane on minimum intensity projection (minIP) computed tomography (CT). In particular, sites on more than three slices demonstrated the disappearance of the fat plane. On the other hand, five of the eight analyzable cases in the no adhesion group showed no disappearance of the fat plane. Therefore, one central slice adequately reflected the fat plane in the cases in the no adhesion group. These findings suggest that it is necessary to obtain a careful diagnosis of the extent of attachment of the LN to the PA using modern diagnostic imaging in order to preoperatively assess the degree of adhesion of the LN to adjacent structures.

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Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 2 40%
Professor 1 20%
Unknown 2 40%
Readers by discipline Count As %
Medicine and Dentistry 3 60%
Social Sciences 1 20%
Unknown 1 20%