Yoshimasa Horie1, Toshiyuki Yoshio2, Kazuharu Aoyama3, Shoichi Yoshimizu4, Yusuke Horiuchi4, Akiyoshi Ishiyama4, Toshiaki Hirasawa2, Tomohiro Tsuchida4, Tsuyoshi Ozawa5, Soichiro Ishihara5, Youichi Kumagai6, Mitsuhiro Fujishiro7, Iruru Maetani8, Junko Fujisaki4, Tomohiro Tada9. 1. Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan; Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan. 2. Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan; Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan. 3. AI Medical Service Inc, Tokyo, Japan. 4. Department of Gastroenterology, Cancer Institute Hospital, Japanese Foundation for Cancer Research, Tokyo, Japan. 5. Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan; Surgery Department, Sanno Hospital, International University of Health and Welfare, Tokyo, Japan. 6. Department of Digestive Tract and General Surgery, Saitama Medical Center, Saitama Medical University, Saitama, Japan. 7. Department of Gastroenterology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan. 8. Division of Gastroenterology and Hepatology, Department of Internal Medicine, Toho University Ohashi Medical Center, Tokyo, Japan. 9. Tada Tomohiro Institute of Gastroenterology and Proctology, Saitama, Japan; AI Medical Service Inc, Tokyo, Japan; Department of Surgical Oncology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
Abstract
BACKGROUND AND AIMS: The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too late for effective treatment. Recently, artificial intelligence (AI) using deep learning has made remarkable progress in medicine. However, there are no reports on its application for diagnosing esophageal cancer. Here, we demonstrate the diagnostic ability of AI to detect esophageal cancer including squamous cell carcinoma and adenocarcinoma. METHODS: We retrospectively collected 8428 training images of esophageal cancer from 384 patients at the Cancer Institute Hospital, Japan. Using these, we developed deep learning through convolutional neural networks (CNNs). We also prepared 1118 test images for 47 patients with 49 esophageal cancers and 50 patients without esophageal cancer to evaluate the diagnostic accuracy. RESULTS: The CNN took 27 seconds to analyze 1118 test images and correctly detected esophageal cancer cases with a sensitivity of 98%. CNN could detect all 7 small cancer lesions less than 10 mm in size. Although the positive predictive value for each image was 40%, misdiagnosing shadows and normal structures led to a negative predictive value of 95%. The CNN could distinguish superficial esophageal cancer from advanced cancer with an accuracy of 98%. CONCLUSIONS: The constructed CNN system for detecting esophageal cancer can analyze stored endoscopic images in a short time with high sensitivity. However, more training would lead to higher diagnostic accuracy. This system can facilitate early detection in practice, leading to a better prognosis in the near future.
BACKGROUND AND AIMS: The prognosis of esophageal cancer is relatively poor. Patients are usually diagnosed at an advanced stage when it is often too late for effective treatment. Recently, artificial intelligence (AI) using deep learning has made remarkable progress in medicine. However, there are no reports on its application for diagnosing esophageal cancer. Here, we demonstrate the diagnostic ability of AI to detect esophageal cancer including squamous cell carcinoma and adenocarcinoma. METHODS: We retrospectively collected 8428 training images of esophageal cancer from 384 patients at the Cancer Institute Hospital, Japan. Using these, we developed deep learning through convolutional neural networks (CNNs). We also prepared 1118 test images for 47 patients with 49 esophageal cancers and 50 patients without esophageal cancer to evaluate the diagnostic accuracy. RESULTS: The CNN took 27 seconds to analyze 1118 test images and correctly detected esophageal cancer cases with a sensitivity of 98%. CNN could detect all 7 small cancer lesions less than 10 mm in size. Although the positive predictive value for each image was 40%, misdiagnosing shadows and normal structures led to a negative predictive value of 95%. The CNN could distinguish superficial esophageal cancer from advanced cancer with an accuracy of 98%. CONCLUSIONS: The constructed CNN system for detecting esophageal cancer can analyze stored endoscopic images in a short time with high sensitivity. However, more training would lead to higher diagnostic accuracy. This system can facilitate early detection in practice, leading to a better prognosis in the near future.
Authors: Andreas Kleppe; Ole-Johan Skrede; Sepp De Raedt; Knut Liestøl; David J Kerr; Håvard E Danielsen Journal: Nat Rev Cancer Date: 2021-01-29 Impact factor: 60.716