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

Comput Math Methods Med. 2012;2012:876545. doi: 10.1155/2012/876545. Epub 2012 Oct 24.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Artificial Intelligence
  • Diagnostic Imaging / methods
  • Humans
  • Lymph Nodes / pathology*
  • Lymphatic Metastasis / diagnostic imaging*
  • Models, Statistical
  • Photons
  • Radiographic Image Interpretation, Computer-Assisted
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Stomach Neoplasms / diagnostic imaging*
  • Stomach Neoplasms / pathology
  • Tomography, X-Ray Computed / methods
  • X-Rays