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Stratification of gastric cancer risk using a deep neural network
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Zeitschriftentitel: | JGH Open |
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Personen und Körperschaften: | , , , , , , , , , , , , , , , , , , , , , |
In: | JGH Open, 4, 2020, 3, S. 466-471 |
Medientyp: | E-Article |
Sprache: | Englisch |
veröffentlicht: |
Wiley
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Schlagwörter: |
author_facet |
Nakahira, Hiroko Ishihara, Ryu Aoyama, Kazuharu Kono, Mitsuhiro Fukuda, Hiromu Shimamoto, Yusaku Nakagawa, Kentaro Ohmori, Masayasu Iwatsubo, Taro Iwagami, Hiroyoshi Matsuno, Kenshi Inoue, Shuntaro Matsuura, Noriko Shichijo, Satoki Maekawa, Akira Kanesaka, Takashi Yamamoto, Sachiko Takeuchi, Yoji Higashino, Koji Uedo, Noriya Matsunaga, Takashi Tada, Tomohiro Nakahira, Hiroko Ishihara, Ryu Aoyama, Kazuharu Kono, Mitsuhiro Fukuda, Hiromu Shimamoto, Yusaku Nakagawa, Kentaro Ohmori, Masayasu Iwatsubo, Taro Iwagami, Hiroyoshi Matsuno, Kenshi Inoue, Shuntaro Matsuura, Noriko Shichijo, Satoki Maekawa, Akira Kanesaka, Takashi Yamamoto, Sachiko Takeuchi, Yoji Higashino, Koji Uedo, Noriya Matsunaga, Takashi Tada, Tomohiro |
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author |
Nakahira, Hiroko Ishihara, Ryu Aoyama, Kazuharu Kono, Mitsuhiro Fukuda, Hiromu Shimamoto, Yusaku Nakagawa, Kentaro Ohmori, Masayasu Iwatsubo, Taro Iwagami, Hiroyoshi Matsuno, Kenshi Inoue, Shuntaro Matsuura, Noriko Shichijo, Satoki Maekawa, Akira Kanesaka, Takashi Yamamoto, Sachiko Takeuchi, Yoji Higashino, Koji Uedo, Noriya Matsunaga, Takashi Tada, Tomohiro |
spellingShingle |
Nakahira, Hiroko Ishihara, Ryu Aoyama, Kazuharu Kono, Mitsuhiro Fukuda, Hiromu Shimamoto, Yusaku Nakagawa, Kentaro Ohmori, Masayasu Iwatsubo, Taro Iwagami, Hiroyoshi Matsuno, Kenshi Inoue, Shuntaro Matsuura, Noriko Shichijo, Satoki Maekawa, Akira Kanesaka, Takashi Yamamoto, Sachiko Takeuchi, Yoji Higashino, Koji Uedo, Noriya Matsunaga, Takashi Tada, Tomohiro JGH Open Stratification of gastric cancer risk using a deep neural network Gastroenterology Hepatology |
author_sort |
nakahira, hiroko |
spelling |
Nakahira, Hiroko Ishihara, Ryu Aoyama, Kazuharu Kono, Mitsuhiro Fukuda, Hiromu Shimamoto, Yusaku Nakagawa, Kentaro Ohmori, Masayasu Iwatsubo, Taro Iwagami, Hiroyoshi Matsuno, Kenshi Inoue, Shuntaro Matsuura, Noriko Shichijo, Satoki Maekawa, Akira Kanesaka, Takashi Yamamoto, Sachiko Takeuchi, Yoji Higashino, Koji Uedo, Noriya Matsunaga, Takashi Tada, Tomohiro 2397-9070 2397-9070 Wiley Gastroenterology Hepatology http://dx.doi.org/10.1002/jgh3.12281 <jats:sec><jats:title>Background and Aim</jats:title><jats:p>Stratifying gastric cancer (GC) risk and endoscopy findings in high‐risk individuals may provide effective surveillance for GC. We developed a computerized image‐ analysis system for endoscopic images to stratify the risk of GC.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>The system was trained using images taken during endoscopic examinations with non‐magnified white‐light imaging. Patients were classified as high‐risk (patients with GC), moderate‐risk (patients with current or past <jats:italic>Helicobacter pylori</jats:italic> infection or gastric atrophy), or low‐risk (patients with no history of <jats:italic>H. pylori</jats:italic> infection or gastric atrophy). After selection, 20,960, 17,404, and 68,920 images were collected as training images for the high‐, moderate‐, and low‐risk groups, respectively.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Performance of the artificial intelligence (AI) system was evaluated by the prevalence of GC in each group using an independent validation dataset of patients who underwent endoscopic examination and <jats:italic>H. pylori</jats:italic> serum antibody testing. In total, 12,824 images from 454 patients were included in the analysis. The time required for diagnosing all the images was 345 seconds. The AI system diagnosed 46, 250, and 158 patients as low‐, moderate‐, and high risk, respectively. The prevalence of GC in the low‐, moderate‐, and high‐risk groups was 2.2, 8.8, and 16.4%, respectively (<jats:italic>P</jats:italic> = 0.0017). Three experienced endoscopists also successfully stratified the risk; however, interobserver agreement was not satisfactory (kappa value of 0.27, indicating fair agreement).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The current AI system detected significant differences in the prevalence of GC among the low‐, moderate‐, and high‐risk groups, suggesting its potential for stratifying GC risk.</jats:p></jats:sec> Stratification of gastric cancer risk using a deep neural network JGH Open |
doi_str_mv |
10.1002/jgh3.12281 |
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Medizin |
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Wiley, 2020 |
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Wiley |
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JGH Open |
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title |
Stratification of gastric cancer risk using a deep neural network |
title_unstemmed |
Stratification of gastric cancer risk using a deep neural network |
title_full |
Stratification of gastric cancer risk using a deep neural network |
title_fullStr |
Stratification of gastric cancer risk using a deep neural network |
title_full_unstemmed |
Stratification of gastric cancer risk using a deep neural network |
title_short |
Stratification of gastric cancer risk using a deep neural network |
title_sort |
stratification of gastric cancer risk using a deep neural network |
topic |
Gastroenterology Hepatology |
url |
http://dx.doi.org/10.1002/jgh3.12281 |
publishDate |
2020 |
physical |
466-471 |
description |
<jats:sec><jats:title>Background and Aim</jats:title><jats:p>Stratifying gastric cancer (GC) risk and endoscopy findings in high‐risk individuals may provide effective surveillance for GC. We developed a computerized image‐ analysis system for endoscopic images to stratify the risk of GC.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>The system was trained using images taken during endoscopic examinations with non‐magnified white‐light imaging. Patients were classified as high‐risk (patients with GC), moderate‐risk (patients with current or past <jats:italic>Helicobacter pylori</jats:italic> infection or gastric atrophy), or low‐risk (patients with no history of <jats:italic>H. pylori</jats:italic> infection or gastric atrophy). After selection, 20,960, 17,404, and 68,920 images were collected as training images for the high‐, moderate‐, and low‐risk groups, respectively.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Performance of the artificial intelligence (AI) system was evaluated by the prevalence of GC in each group using an independent validation dataset of patients who underwent endoscopic examination and <jats:italic>H. pylori</jats:italic> serum antibody testing. In total, 12,824 images from 454 patients were included in the analysis. The time required for diagnosing all the images was 345 seconds. The AI system diagnosed 46, 250, and 158 patients as low‐, moderate‐, and high risk, respectively. The prevalence of GC in the low‐, moderate‐, and high‐risk groups was 2.2, 8.8, and 16.4%, respectively (<jats:italic>P</jats:italic> = 0.0017). Three experienced endoscopists also successfully stratified the risk; however, interobserver agreement was not satisfactory (kappa value of 0.27, indicating fair agreement).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The current AI system detected significant differences in the prevalence of GC among the low‐, moderate‐, and high‐risk groups, suggesting its potential for stratifying GC risk.</jats:p></jats:sec> |
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author | Nakahira, Hiroko, Ishihara, Ryu, Aoyama, Kazuharu, Kono, Mitsuhiro, Fukuda, Hiromu, Shimamoto, Yusaku, Nakagawa, Kentaro, Ohmori, Masayasu, Iwatsubo, Taro, Iwagami, Hiroyoshi, Matsuno, Kenshi, Inoue, Shuntaro, Matsuura, Noriko, Shichijo, Satoki, Maekawa, Akira, Kanesaka, Takashi, Yamamoto, Sachiko, Takeuchi, Yoji, Higashino, Koji, Uedo, Noriya, Matsunaga, Takashi, Tada, Tomohiro |
author_facet | Nakahira, Hiroko, Ishihara, Ryu, Aoyama, Kazuharu, Kono, Mitsuhiro, Fukuda, Hiromu, Shimamoto, Yusaku, Nakagawa, Kentaro, Ohmori, Masayasu, Iwatsubo, Taro, Iwagami, Hiroyoshi, Matsuno, Kenshi, Inoue, Shuntaro, Matsuura, Noriko, Shichijo, Satoki, Maekawa, Akira, Kanesaka, Takashi, Yamamoto, Sachiko, Takeuchi, Yoji, Higashino, Koji, Uedo, Noriya, Matsunaga, Takashi, Tada, Tomohiro, Nakahira, Hiroko, Ishihara, Ryu, Aoyama, Kazuharu, Kono, Mitsuhiro, Fukuda, Hiromu, Shimamoto, Yusaku, Nakagawa, Kentaro, Ohmori, Masayasu, Iwatsubo, Taro, Iwagami, Hiroyoshi, Matsuno, Kenshi, Inoue, Shuntaro, Matsuura, Noriko, Shichijo, Satoki, Maekawa, Akira, Kanesaka, Takashi, Yamamoto, Sachiko, Takeuchi, Yoji, Higashino, Koji, Uedo, Noriya, Matsunaga, Takashi, Tada, Tomohiro |
author_sort | nakahira, hiroko |
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description | <jats:sec><jats:title>Background and Aim</jats:title><jats:p>Stratifying gastric cancer (GC) risk and endoscopy findings in high‐risk individuals may provide effective surveillance for GC. We developed a computerized image‐ analysis system for endoscopic images to stratify the risk of GC.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>The system was trained using images taken during endoscopic examinations with non‐magnified white‐light imaging. Patients were classified as high‐risk (patients with GC), moderate‐risk (patients with current or past <jats:italic>Helicobacter pylori</jats:italic> infection or gastric atrophy), or low‐risk (patients with no history of <jats:italic>H. pylori</jats:italic> infection or gastric atrophy). After selection, 20,960, 17,404, and 68,920 images were collected as training images for the high‐, moderate‐, and low‐risk groups, respectively.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Performance of the artificial intelligence (AI) system was evaluated by the prevalence of GC in each group using an independent validation dataset of patients who underwent endoscopic examination and <jats:italic>H. pylori</jats:italic> serum antibody testing. In total, 12,824 images from 454 patients were included in the analysis. The time required for diagnosing all the images was 345 seconds. The AI system diagnosed 46, 250, and 158 patients as low‐, moderate‐, and high risk, respectively. The prevalence of GC in the low‐, moderate‐, and high‐risk groups was 2.2, 8.8, and 16.4%, respectively (<jats:italic>P</jats:italic> = 0.0017). Three experienced endoscopists also successfully stratified the risk; however, interobserver agreement was not satisfactory (kappa value of 0.27, indicating fair agreement).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The current AI system detected significant differences in the prevalence of GC among the low‐, moderate‐, and high‐risk groups, suggesting its potential for stratifying GC risk.</jats:p></jats:sec> |
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spelling | Nakahira, Hiroko Ishihara, Ryu Aoyama, Kazuharu Kono, Mitsuhiro Fukuda, Hiromu Shimamoto, Yusaku Nakagawa, Kentaro Ohmori, Masayasu Iwatsubo, Taro Iwagami, Hiroyoshi Matsuno, Kenshi Inoue, Shuntaro Matsuura, Noriko Shichijo, Satoki Maekawa, Akira Kanesaka, Takashi Yamamoto, Sachiko Takeuchi, Yoji Higashino, Koji Uedo, Noriya Matsunaga, Takashi Tada, Tomohiro 2397-9070 2397-9070 Wiley Gastroenterology Hepatology http://dx.doi.org/10.1002/jgh3.12281 <jats:sec><jats:title>Background and Aim</jats:title><jats:p>Stratifying gastric cancer (GC) risk and endoscopy findings in high‐risk individuals may provide effective surveillance for GC. We developed a computerized image‐ analysis system for endoscopic images to stratify the risk of GC.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>The system was trained using images taken during endoscopic examinations with non‐magnified white‐light imaging. Patients were classified as high‐risk (patients with GC), moderate‐risk (patients with current or past <jats:italic>Helicobacter pylori</jats:italic> infection or gastric atrophy), or low‐risk (patients with no history of <jats:italic>H. pylori</jats:italic> infection or gastric atrophy). After selection, 20,960, 17,404, and 68,920 images were collected as training images for the high‐, moderate‐, and low‐risk groups, respectively.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Performance of the artificial intelligence (AI) system was evaluated by the prevalence of GC in each group using an independent validation dataset of patients who underwent endoscopic examination and <jats:italic>H. pylori</jats:italic> serum antibody testing. In total, 12,824 images from 454 patients were included in the analysis. The time required for diagnosing all the images was 345 seconds. The AI system diagnosed 46, 250, and 158 patients as low‐, moderate‐, and high risk, respectively. The prevalence of GC in the low‐, moderate‐, and high‐risk groups was 2.2, 8.8, and 16.4%, respectively (<jats:italic>P</jats:italic> = 0.0017). Three experienced endoscopists also successfully stratified the risk; however, interobserver agreement was not satisfactory (kappa value of 0.27, indicating fair agreement).</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>The current AI system detected significant differences in the prevalence of GC among the low‐, moderate‐, and high‐risk groups, suggesting its potential for stratifying GC risk.</jats:p></jats:sec> Stratification of gastric cancer risk using a deep neural network JGH Open |
spellingShingle | Nakahira, Hiroko, Ishihara, Ryu, Aoyama, Kazuharu, Kono, Mitsuhiro, Fukuda, Hiromu, Shimamoto, Yusaku, Nakagawa, Kentaro, Ohmori, Masayasu, Iwatsubo, Taro, Iwagami, Hiroyoshi, Matsuno, Kenshi, Inoue, Shuntaro, Matsuura, Noriko, Shichijo, Satoki, Maekawa, Akira, Kanesaka, Takashi, Yamamoto, Sachiko, Takeuchi, Yoji, Higashino, Koji, Uedo, Noriya, Matsunaga, Takashi, Tada, Tomohiro, JGH Open, Stratification of gastric cancer risk using a deep neural network, Gastroenterology, Hepatology |
title | Stratification of gastric cancer risk using a deep neural network |
title_full | Stratification of gastric cancer risk using a deep neural network |
title_fullStr | Stratification of gastric cancer risk using a deep neural network |
title_full_unstemmed | Stratification of gastric cancer risk using a deep neural network |
title_short | Stratification of gastric cancer risk using a deep neural network |
title_sort | stratification of gastric cancer risk using a deep neural network |
title_unstemmed | Stratification of gastric cancer risk using a deep neural network |
topic | Gastroenterology, Hepatology |
url | http://dx.doi.org/10.1002/jgh3.12281 |