胃と腸56巻4号

2021年04月発行

今月の主題 消化管疾患AI診断の現状 主題

胃疾患におけるAI診断—高効率な学習スキームによる胃癌の領域検出AI

著者： 堀圭介¹² 竹本智子³ 横田秀夫³ 池松弘朗¹² 矢野友規¹⁴

所属機関： ¹国立がん研究センター東病院消化管内視鏡科 ²国立がん研究センター先端医療開発センター内視鏡機器開発分野 ³理化学研究所光量子工学研究センター画像情報処理研究チーム ⁴国立がん研究センター東病院NEXT医療機器開発センター

ページ範囲：P.423 - P.431

要旨●近年，消化管内視鏡領域におけるコンピュータによる補助診断（CAD）の応用は，畳み込みニューラルネットワーク（CNN）の発展に伴い急速に進歩している．胃癌に関連するCADの応用は，胃炎の鑑別診断，解剖学的部位の判別から始まり，胃癌の質的，量的診断と拾い上げ診断など多岐にわたって良好な成績が報告されつつある．一方，CNNを用いたCADには多量の学習用画像を必要とすることが多い．今回筆者らは少数の画像から切り出した微小領域のデータを拡張し，高効率な学習を可能とした胃癌の領域情報を提示するモデルを構築した．300画像から構築したCADにより，1年分の連続する胃癌患者137例462画像に対して画像ベースでの感度87.2%，症例ベースでの感度97.8%と良好な結果であった．高効率な学習を可能とするアプローチは，今後のCADの構築にも有用であると考えられた．

参考文献

1）Bray F, Ferlay J, Soerjomataram I, et al. Global cancer statistics 2018：GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin　68：394-424, 2018

2）Hamashima C, Ogoshi K, Okamoto M, et al. A community-based, case-control study evaluating mortality reduction from gastric cancer by endoscopic screening in Japan. PLoS One　8：e79088, 2013

3）Jun JK, Choi KS, Lee HY, et al. Effectiveness of the Korean national cancer screening program in reducing gastric cancer mortality. Gastroenterology　152：1319-1328, 2017

4）Ezoe Y, Muto M, Uedo N, et al. Magnifying narrowband imaging is more accurate than conventional white-light imaging in diagnosis of gastric mucosal cancer. Gastroenterology　141：2017-2025, 2011

5）Dohi O, Yagi N, Majima A, et al. Diagnostic ability of magnifying endoscopy with blue laser imaging for early gastric cancer：a prospective study. Gastric Cancer　20：297-303, 2017

6）Kanzaki H, Takenaka R, Kawahara Y, et al. Linked color imaging（LCI）, a novel image-enhanced endoscopy technology, emphasizes the color of early gastric cancer. Endosc Int Open　5：e1005-1013, 2017

7）Dohi O, Yagi N, Naito Y, et al. Blue laser imaging-bright improves the real-time detection rate of early gastric cancer：a randomized controlled study. Gastrointest Endosc　89：47-57, 2019

8）Ono S, Kawada K, Dohi O, et al. Linked color imaging focused on neoplasm detection in the upper gastrointestinal tract：a randomized trial. Ann Intern Med　2020［ahead of print］

9）Yoshida N, Doyama H, Yano T, et al. Early gastric cancer detection in high-risk patients：a multicentre randomised controlled trial on the effect of second-generation narrow band imaging. Gut　70：67-75, 2021

10）Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In Pereira F, Burges CJC, Bottou L, et al（eds）. Advances in Neural Information Processing Systems 25. NIPS, pp 1097-1105, 2012

11）Ren S, He K, Girshick R, et al. Faster r-cnn：Towards real-time object detection with region proposal networks. In Cortes C, Lawrence N, Lee D, et al（eds）. Advances in Neural Information Processing Systems 28. NIPS, pp 91-99, 2015

12）Liu W, Anguelov D, Erhan D, et al（eds）. SSD：Single Shot Multibox Detector. In Computer Vision-ECCV. Springer, Berlin, pp 21-37, 2016

13）Redmon J, Divvala S, Girshick R, et al. You Only Look Once：Unified, Real-Time Object Detection. In Proceedings of the IEEE conference on computer vision and pattern recognition. pp 779-788, 2016

14）Shichijo S, Nomura S, Aoyama K, et al. Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine　25：106-111, 2017

15）Takiyama H, Ozawa T, Ishihara S, et al. Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep　8：7497, 2018

16）Wu L, Zhang J, Zhou W, et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut　68：2161-2169, 2019

17）Horiuchi Y, Aoyama K, Tokai Y, et al. Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging. Dig Dis Sci　65：1355-1363, 2020

18）Li L, Chen Y, Shen Z, et al. Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer　23：126-132, 2020

19）Horiuchi Y, Hirasawa T, Ishizuka N, et al. Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging（with videos）. Gastrointest Endosc　92：856-865, 2020

20）Ueyama H, Kato Y, Akazawa Y, et al. Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow-band imaging. J Gastroenterol Hepatol　2020［ahead of print］

21）Zhu Y, Wang QC, Xu MD, et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc　89：806-815, 2019

22）Nagao S, Tsuji Y, Sakaguchi Y, et al. Highly accurate artificial intelligence systems to predict the invasion depth of gastric cancer：efficacy of conventional white-light imaging, nonmagnifying narrow-band imaging, and indigo-carmine dye contrast imaging. Gastrointest Endosc　92：866-873, 2020

23）Hirasawa T, Aoyama K, Tanimoto T, et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer　21：653-660, 2018

24）Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc　31：e34-35, 2019

25）Luo H, Xu G, Li C, et al. Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy：a multicentre, case-control, diagnostic study. Lancet Oncol　20：1645-1654, 2019

26）Sakai Y, Takemoto S, Hori K, et al. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network. Annu Int Conf IEEE Eng Med Biol Soc　2018：4138-4141, 2018