Automatic detection of squamous cell carcinoma metastasis in esophageal lymph nodes using semantic segmentation. Issue 3 (28th July 2020)

• Record Type: Journal Article
• Authors: Pan, Yi; Sun, Zhuo; Wang, Wenmiao; Yang, Zhaoyang; Jia, Jia; Feng, Xiaolong; Wang, Yaxi; Fang, Qing; Li, Jiangtao; Dai, Hongtian; Ku, Calvin; Wang, Shuhao; Liu, Cancheng; Xue, Liyan; Lyu, Ning; Zou, Shuangmei
• Is Part Of: Clinical and translational medicine. Volume 10:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-07-28
• Subjects: esophagus -- lymph node metastasis -- semantic segmentation -- squamous cell carcinoma; Clinical medicine -- Periodicals; Medicine, Experimental -- Periodicals; Medical innovations -- Periodicals; Molecular biology -- Periodicals; Pathology, Molecular -- Periodicals; 616.027
• DOI: 10.1002/ctm2.129
• Languages: English
• ISSNs: 2001-1326

Abstract

Abstract: Esophageal squamous cell carcinoma (ESCC) is more prevalent than esophageal adenocarcinoma in Asia, especially in China, where more than half of ESCC cases occur worldwide. Many studies have reported that the automatic detection of lymph node metastasis using semantic segmentation shows good performance in breast cancer and other adenocarcinomas. However, the detection of squamous cell carcinoma metastasis in hematoxylin‐eosin (H&E)‐stained slides has never been reported. We collected a training set of 110 esophageal lymph node slides with metastasis and 132 lymph node slides without metastasis. An iPad‐based annotation system was used to draw the contours of the cancer metastasis region. A DeepLab v3 model was trained to achieve the best fit with the training data. The learned model could estimate the probability of metastasis. To evaluate the effectiveness of the detection model of learned metastasis, we used another large cohort of clinical H&E‐stained esophageal lymph node slides containing 795 esophageal lymph nodes from 154 esophageal cancer patients. The basic authenticity label for each slide was confirmed by experienced pathologists. After filtering isolated noise in the prediction, we obtained an accuracy of 94%. Furthermore, we applied the learned model to throat and lung lymph node squamous cell carcinoma metastases and achieved the following promising results: an accuracy of 96.7% in throat cancer and an accuracy of 90% in lung cancer. In this work, we organized an annotated dataset of H&E‐stained esophageal lymph node and trained a deep neural network to detect lymph node metastasis in H&E‐stained slides of squamous cell carcinoma automatically. Moreover, it is possible to use this model to detect lymph nodes metastasis in squamous cell carcinoma from other organs. This study directly demonstrates the potential for determining the localization of squamous cell carcinoma metastases in lymph node and assisting in pathological diagnosis. Abstract : XXX