Automated histological classification of whole-slide images of gastric biopsy specimens

Gastric Cancer. 2018 Mar;21(2):249-257. doi: 10.1007/s10120-017-0731-8. Epub 2017 Jun 2.


Background: Automated image analysis has been developed currently in the field of surgical pathology. The aim of the present study was to evaluate the classification accuracy of the e-Pathologist image analysis software.

Methods: A total of 3062 gastric biopsy specimens were consecutively obtained and stained. The specimen slides were anonymized and digitized. At least two experienced gastrointestinal pathologists evaluated each slide for pathological diagnosis. We compared the three-tier (positive for carcinoma or suspicion of carcinoma; caution for adenoma or suspicion of a neoplastic lesion; or negative for a neoplastic lesion) or two-tier (negative or non-negative) classification results of human pathologists and of the e-Pathologist.

Results: Of 3062 cases, 33.4% showed an abnormal finding. For the three-tier classification, the overall concordance rate was 55.6% (1702/3062). The kappa coefficient was 0.28 (95% CI, 0.26-0.30; fair agreement). For the negative biopsy specimens, the concordance rate was 90.6% (1033/1140), but for the positive biopsy specimens, the concordance rate was less than 50%. For the two-tier classification, the sensitivity, specificity, positive predictive value, and negative predictive value were 89.5% (95% CI, 87.5-91.4%), 50.7% (95% CI, 48.5-52.9%), 47.7% (95% CI, 45.4-49.9%), and 90.6% (95% CI, 88.8-92.2%), respectively.

Conclusions: Although there are limitations and requirements for applying automated histopathological classification of gastric biopsy specimens in the clinical setting, the results of the present study are promising.

Keywords: Artificial intelligence; Automated image analysis; Gastric biopsy; Histopathological classification.

MeSH terms

  • Adenocarcinoma / classification*
  • Adenocarcinoma / diagnosis
  • Adenocarcinoma / pathology
  • Automation, Laboratory / methods
  • Biopsy
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Machine Learning*
  • Pathology, Clinical / methods*
  • Software
  • Stomach Neoplasms / classification*
  • Stomach Neoplasms / diagnosis
  • Stomach Neoplasms / pathology