Research Papers: Pathology:
Automated histological classification of whole slide images of colorectal biopsy specimens
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Hiroshi Yoshida1,*, Yoshiko Yamashita2,*, Taichi Shimazu3, Eric Cosatto4, Tomoharu Kiyuna2, Hirokazu Taniguchi1, Shigeki Sekine1,5 and Atsushi Ochiai1,5,6
1 Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, Chuo-ku, Tokyo, Japan
2 Medical Solutions Division, NEC Corporation, Minato-ku, Tokyo, Japan
3 Epidemiology and Prevention Group, Research Center for Cancer Prevention and Screening, National Cancer Center, Chuo-ku, Tokyo, Japan
4 Department of Machine Learning, NEC Laboratories America, Princeton, NJ, USA
5 Division of Molecular Pathology, National Cancer Center Research Institute, Chuo-ku, Tokyo, Japan
6 Division of Pathology, Research Center for Innovative Oncology, National Cancer Center, Kashiwa, Chiba, Japan
* These authors are equally contributed to this work
Hiroshi Yoshida, email:
Keywords: colorectal cancer, artificial intelligence, automated image analysis, whole slide imaging, histological classification, Pathology Section
Received: March 20, 2017 Accepted: September 30, 2017 Published: October 12, 2017
Background: An automated image analysis system, e-Pathologist, was developed to improve the quality of colorectal biopsy diagnostics in routine pathology practice.
Objective: The aim of the study was to evaluate the classification accuracy of the e-Pathologist image analysis software in the setting of routine pathology practice in two institutions.
Materials and methods: In total, 1328 colorectal tissue specimens were consecutively obtained from two hospitals (1077 tissues from Tokyo hospital, and 251 tissues from East hospital) and the stained specimen slides were anonymized and digitized. At least two experienced gastrointestinal pathologists evaluated each slide for pathological diagnosis. We compared the 3-tier classification results (carcinoma or suspicion of carcinoma, adenoma, and lastly negative for a neoplastic lesion) between the human pathologists and that of e-Pathologist.
Results: For the Tokyo hospital specimens, all carcinoma tissues were correctly classified (n=112), and 9.9% (80/810) of the adenoma tissues were incorrectly classified as negative. For the East hospital specimens, 0 out of the 51 adenoma tissues were incorrectly classified as negative while 9.3% (11/118) of the carcinoma tissues were incorrectly classified as either adenoma, or negative. For the Tokyo and East hospital datasets, the undetected rate of carcinoma, undetected rate of adenoma, and over-detected proportion were 0% and 9.3%, 9.9% and 0%, and 36.1% and 27.1%, respectively.
Conclusions: This image analysis system requires some improvements; however, it has the potential to assist pathologists in quality improvement of routine pathological practice in the not too distant future.
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