Yoshida Hiroshi, Yamashita Yoshiko, Shimazu Taichi, Cosatto Eric, Kiyuna Tomoharu, Taniguchi Hirokazu, Sekine Shigeki, Ochiai Atsushi
Division of Pathology and Clinical Laboratories, National Cancer Center Hospital, Chuo-ku, Tokyo, Japan.
Medical Solutions Division, NEC Corporation, Minato-ku, Tokyo, Japan.
Oncotarget. 2017 Oct 12;8(53):90719-90729. doi: 10.18632/oncotarget.21819. eCollection 2017 Oct 31.
An automated image analysis system, e-Pathologist, was developed to improve the quality of colorectal biopsy diagnostics in routine pathology practice.
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.
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.
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.
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.
开发了一种自动化图像分析系统e-Pathologist，以提高常规病理实践中结直肠活检诊断的质量。
本研究的目的是评估e-Pathologist图像分析软件在两家机构常规病理实践中的分类准确性。
总共从两家医院连续获取了1328份结直肠组织标本（东京医院1077份组织，东方医院251份组织），对染色的标本玻片进行匿名化处理并数字化。至少两名经验丰富的胃肠病理学家对每张玻片进行病理诊断评估。我们比较了人类病理学家和e-Pathologist的三级分类结果（癌或疑似癌、腺瘤，最后为肿瘤性病变阴性）。
对于东京医院的标本，所有癌组织均被正确分类（n = 112），9.9%（80/810）的腺瘤组织被错误分类为阴性。对于东方医院的标本，51份腺瘤组织中没有一份被错误分类为阴性，而9.3%（11/118）的癌组织被错误分类为腺瘤或阴性。对于东京医院和东方医院的数据集，癌的未检出率、腺瘤的未检出率和过度检出比例分别为0%和9.3%、9.9%和0%、36.1%和27.1%。
该图像分析系统需要一些改进；然而，在不久的将来，它有可能帮助病理学家提高常规病理实践的质量。
