Hiroshi Yoshida1, Taichi Shimazu2, Tomoharu Kiyuna3, Atsushi Marugame4, Yoshiko Yamashita3, Eric Cosatto5, Hirokazu Taniguchi6, Shigeki Sekine6,7, Atsushi Ochiai6,8. 1. Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. hiroyosh@ncc.go.jp. 2. Epidemiology and Prevention Group, Center for Public Health Sciences, National Cancer Center, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. 3. Medical Solutions Division, NEC Corporation, 5-7-1 Shiba, Minato-ku, Tokyo, 108-8001, Japan. 4. Space System Division, NEC Corporation, 10, Nisshin-cho 1-Chome, Fuchu, Tokyo, 183-8501, Japan. 5. Department of Machine Learning, NEC Laboratories America, 4 Independence Way, Suite 200, Princeton, NJ, 08540, USA. 6. Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. 7. Division of Molecular Pathology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. 8. Division of Pathology, Research Center for Innovative Oncology, National Cancer Center, 6-5-1, Kashiwa, Chiba, 277-8577, Japan.
Abstract
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.
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.
Authors: Yue Du; Roy Zhang; Abolfazl Zargari; Theresa C Thai; Camille C Gunderson; Katherine M Moxley; Hong Liu; Bin Zheng; Yuchen Qiu Journal: Ann Biomed Eng Date: 2018-07-26 Impact factor: 3.934