Using the K-nearest neighbor algorithm for the classification of lymph node metastasis in gastric cancer

Su Zhang, Chao Li, Shuheng Zhang, Huan Zhang, Lifang Pang, Kin Man Lam, Chun Hui

Research output: Journal article publicationJournal articleAcademic researchpeer-review

39 Citations (Scopus)

Abstract

Accurate tumor, node, and metastasis (TNM) staging, especially N staging in gastric cancer or the metastasis on lymph node diagnosis, is a popular issue in clinical medical image analysis in which gemstone spectral imaging (GSI) can provide more information to doctors than conventional computed tomography (CT) does. In this paper, we apply machine learning methods on the GSI analysis of lymph node metastasis in gastric cancer. First, we use some feature selection or metric learning methods to reduce data dimension and feature space. We then employ the K-nearest neighbor classifier to distinguish lymph node metastasis from nonlymph node metastasis. The experiment involved 38 lymph node samples in gastric cancer, showing an overall accuracy of 96.33. Compared with that of traditional diagnostic methods, such as helical CT (sensitivity 75.2 and specificity 41.8) and multidetector computed tomography (82.09), the diagnostic accuracy of lymph node metastasis is high. GSI-CT can then be the optimal choice for the preoperative diagnosis of patients with gastric cancer in the N staging.
Original languageEnglish
Article number876545
JournalComputational and Mathematical Methods in Medicine
Volume2012
DOIs
Publication statusPublished - 3 Dec 2012

ASJC Scopus subject areas

  • Modelling and Simulation
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Applied Mathematics

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