Artificial intelligence-based diagnostic system classifying gastric cancers and ulcers: comparison between the original and newly developed systems

Abstract

Background We previously reported for the first time the usefulness of artificial intelligence (AI) systems in detecting gastric cancers. However, the “original convolutional neural network (O-CNN)” employed in the previous study had a relatively low positive predictive value (PPV). Therefore, we aimed to develop an advanced AI-based diagnostic system and evaluate its applicability for the classification of gastric cancers and gastric ulcers.

Methods We constructed an “advanced CNN” (A-CNN) by adding a new training dataset (4453 gastric ulcer images from 1172 lesions) to the O-CNN, which had been trained using 13 584 gastric cancer and 373 gastric ulcer images. The diagnostic performance of the A-CNN in terms of classifying gastric cancers and ulcers was retrospectively evaluated using an independent validation dataset (739 images from 100 early gastric cancers and 720 images from 120 gastric ulcers) and compared with that of the O-CNN by estimating the overall classification accuracy.

Results The sensitivity, specificity, and PPV of the A-CNN in classifying gastric cancer at the lesion level were 99.0 % (95 % confidence interval [CI] 94.6 %−100 %), 93.3 % (95 %CI 87.3 %−97.1 %), and 92.5 % (95 %CI 85.8 %−96.7 %), respectively, and for classifying gastric ulcers were 93.3 % (95 %CI 87.3 %−97.1 %), 99.0 % (95 %CI 94.6 %−100 %), and 99.1 % (95 %CI 95.2 %−100 %), respectively. At the lesion level, the overall accuracies of the O- and A-CNN for classifying gastric cancers and gastric ulcers were 45.9 % (gastric cancers 100 %, gastric ulcers 0.8 %) and 95.9 % (gastric cancers 99.0 %, gastric ulcers 93.3 %), respectively.

Conclusion The newly developed AI-based diagnostic system can effectively classify gastric cancers and gastric ulcers.

Publikationsverlauf

Eingereicht: 13. August 2019

Angenommen: 05. Juni 2020

Accepted Manuscript online:
05. Juni 2020

Artikel online veröffentlicht:
08. Juli 2020

• References

• 1 Erlay J, Ervik M, Dikshit R. et al. GLOBOCAN 2012 v1.0, cancer incidence and mortality worldwide: IARC CancerBase No. 11 (Internet). Int Agency Res Cancer 2013; 42: 124-140
• 2 Marques-Lespier JM, Juan M, Cruz-Correa M. Current perspectives on gastric cancer. Gastroenterol Clin North Am 2016; 45: 413-428
• 3 Sano T, Coit DG, Kim HH. et al. Proposal of a new stage grouping of gastric cancer for TNM classification: International Gastric Cancer Association staging project. Gastric Cancer 2017; 20: 217-225
• 4 Katai H, Ishikawa T, Akazawa K. et al. Five-year survival analysis of surgically resected gastric cancer cases in Japan: a retrospective analysis of more than 100,000 patients from the nationwide registry of the Japanese Gastric Cancer Association (2001–2007). Gastric Cancer 2018; 21: 144-154
• 5 Menon S, Trudgill N. How commonly is upper gastrointestinal cancer missed at endoscopy? A meta-analysis. Endosc Int Open 2014; 2: E46-E50
• 6 Yalamarthi S, Witherspoon P, McCole D. et al. Missed diagnoses in patients with upper gastrointestinal cancers. Endoscopy 2004; 36: 874-879
• 7 Hosokawa O, Hattori M, Douden K. et al. Difference in accuracy between gastroscopy and colonoscopy for detection of cancer. Hepatogastroenterology 2007; 54: 442-4
• 8 Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems. New York: Curran Associates; 2012 6. 1097-1105
• 9 Russakovsky O, Deng J, Su H. et al. ImageNet large scale visual recognition challenge. Int J Comput Vis 2015; 115: 211-252
• 10 Gulshan V, Peng L, Coram M. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016; 316: 2402-2410
• 11 Babak EB, Mitko V, Paul JD. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 2017; 318: 2199-2210
• 12 Esteva A, Kuprel B, Novoa RA. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 2017; 542: 115-118
• 13 Horie Y, Yoshio T, Aoyama K. et al. Diagnostic outcomes of esophageal cancer by artificial intelligence using convolutional neural networks. Gastrointest Endosc 2019; 89: 25-32
• 14 Kumagai Y, Takubo K, Tada T. et al. Diagnosis using deep-learning artificial intelligence based on the endocytoscopic observation of the esophagus. Esophagus 2019; 16: 180-187
• 15 Misawa M, Kudo SE, Mori Y. et al. Artificial intelligence-assisted polyp detection for colonoscopy: initial experience. Gastroenterology 2018; 154: 2027-2029
• 16 Chen PJ, Lin MC, Lai MJ. et al. Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 2018; 154: 568-575
• 17 Ozawa T, Ishihara S, Fujishiro M. et al. Novel computer-assisted diagnosis system for endoscopic disease activity in patients with ulcerative colitis. Gastrointest Endosc 2019; 89: 416-421
• 18 Aoki T, Yamada A, Aoyama K. et al. Automatic detection of erosions and ulcerations in wireless capsule endoscopy images based on a deep convolutional neural network. Gastrointest Endosc 2019; 89: 357-363
• 19 Takiyama H, Ozawa T, Ishihara S. et al. Automatic anatomical classification of esophagogastroduodenoscopy images using deep convolutional neural networks. Sci Rep 2018; 8: 7497
• 20 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 2017; 25: 106-111
• 21 Kanesaka T, Lee TC, Uedo N. et al. Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging. Gastrointest Endosc 2018; 87: 1339-1344
• 22 de Lange T, Halvorsen P, Riegler M. Methodology to develop machine learning algorithms to improve performance in gastrointestinal endoscopy. World J Gastroenterol 2018; 24: 5057-5062
• 23 Hirasawa T, Fujisaki J, Tanimoto T. et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21: 653-660
• 24 Ishioka M, Hirasawa T, Tada T. Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc 2019; 31: e34-e35
• 25 Wu L, Zhou W, Wan X. et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy 2019; 51: 522-531
• 26 Bustamante M, Devesa F, Borghol A. et al. Accuracy of the initial endoscopic diagnosis in the discrimination of gastric ulcers: is endoscopic follow-up study always needed?. J Clin Gastroenterol 2002; 35: 25-28