Computerized evaluation scheme to detect metastasis in sentinel lymph nodes using contrast-enhanced computed tomography before breast cancer surgery View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-03

AUTHORS

Hiroshi Ashiba, Ryohei Nakayama

ABSTRACT

Sentinel lymph node (SLN) biopsy for evaluating lymph node metastasis during breast cancer surgery is associated with several problems, such as the consequent increase in operation time and the possibility of abrupt changes in the treatment plan during the operation. Although it is desirable to distinguish SLNs with and without cancer metastasis before surgery, there is no established examination for this purpose. This study aimed to develop a computerized scheme for evaluating metastasis in SLNs by analyzing computed tomography lymphography images and the three-dimensional versions of these images. Our database consisted of computed tomography lymphography images from 100 patients with breast cancer. Three subjective features of the nodes were assessed in the three-dimensional images: (1) the shape of the lymphoduct, (2) degree of signal enhancement in the nodes, and (3) shape of the nodes. Six objective features were also assessed in the computed tomography lymphography images: (4) the long axis, (5) area, (6) standard deviation of the signal values, (7) mean signal values, (8) maximum signal value, and (9) minimum signal value. Support vector machines were employed to evaluate cancer metastasis in SLNs. For the input, six of the nine features were selected in a stepwise method. The classification accuracy, sensitivity, and specificity were 98.0% (98/100), 97.8% (44/45), and 98.2% (54/55), respectively. The positive and negative predictive values were 97.8% (44/45) and 98.2% (54/55), respectively. This computerized method exhibited high classification accuracy and will be useful in determining the need for lymph node dissection before breast cancer surgery. More... »

PAGES

55-60

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12194-018-00491-6

DOI

http://dx.doi.org/10.1007/s12194-018-00491-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1110274425

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30499048


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