Abstract
Objective
This study aims to systematically review the application of artificial intelligence (AI) techniques in gastric cancer and to discuss the potential limitations and future directions of AI in gastric cancer.
Methods
A systematic review was performed that follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Pubmed, EMBASE, the Web of Science, and the Cochrane Library were used to search for gastric cancer publications with an emphasis on AI that were published up to June 2020. The terms “artificial intelligence” and “gastric cancer” were used to search for the publications.
Results
A total of 64 articles were included in this review. In gastric cancer, AI is mainly used for molecular bio-information analysis, endoscopic detection for Helicobacter pylori infection, chronic atrophic gastritis, early gastric cancer, invasion depth, and pathology recognition. AI may also be used to establish predictive models for evaluating lymph node metastasis, response to drug treatments, and prognosis. In addition, AI can be used for surgical training, skill assessment, and surgery guidance.
Conclusions
In the foreseeable future, AI applications can play an important role in gastric cancer management in the era of precision medicine.
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References
Ali H, Yasmin M, Sharif M, Rehmani MH (2018) Computer assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images. Comput Methods Programs Biomed 157:39–47. https://doi.org/10.1016/j.cmpb.2018.01.013
Amiri Z, Mohammad K, Mahmoudi M, Parsaeian M, Zeraati H (2013) Assessing the effect of quantitative and qualitative predictors on gastric cancer individuals survival using hierarchical artificial neural network models. Iran Red Crescent Med J 15:42–48. https://doi.org/10.5812/ircmj.4122
Andras I et al (2019) Artificial intelligence and robotics: a combination that is changing the operating room. World J Urol. https://doi.org/10.1007/s00345-019-03037-6
Andreu-Perez J, Poon CC, Merrifield RD, Wong ST, Yang GZ (2015) Big data for health. IEEE J Biomed Health Inform 19:1193–1208. https://doi.org/10.1109/JBHI.2015.2450362
Biglarian A, Hajizadeh E, Kazemnejad A, Zayeri F (2010) Determining of prognostic factors in gastric cancer patients using artificial neural networks. Asian Pac J Cancer Prev 11:533–536
Biglarian A, Hajizadeh E, Kazemnejad A, Zali M (2011) Application of artificial neural network in predicting the survival rate of gastric cancer patients. Iran J Public Health 40:80–86
Bollschweiler EH, Mönig SP, Hensler K, Baldus SE, Maruyama K, Hölscher AH (2004) Artificial neural network for prediction of lymph node metastases in gastric cancer: a phase II diagnostic study. Ann Surg Oncol 11:506–511. https://doi.org/10.1245/aso.2004.04.018
Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 68:394–424. https://doi.org/10.3322/caac.21492
Chen T et al (2019) A gastric cancer LncRNAs model for MSI and survival prediction based on support vector machine. BMC Genomics 20:846. https://doi.org/10.1186/s12864-019-6135-x
Chien CW, Lee YC, Ma T, Lee TS, Lin YC, Wang W, Lee WJ (2008) The application of artificial neural networks and decision tree model in predicting post-operative complication for gastric cancer patients. Hepatogastroenterology 55:1140–1145
Cho BJ et al (2019) Automated classification of gastric neoplasms in endoscopic images using a convolutional neural network. Endoscopy 51:1121–1129. https://doi.org/10.1055/a-0981-6133
Choi J, Kim SG, Im JP, Kim JS, Jung HC, Song IS (2010) Comparison of endoscopic ultrasonography and conventional endoscopy for prediction of depth of tumor invasion in early gastric cancer. Endoscopy 42:705–713. https://doi.org/10.1055/s-0030-1255617
Colom R, Karama S, Jung RE, Haier RJ (2010) Human intelligence and brain networks. Dialogues Clin Neurosci 12:489–501
Correa P, Piazuelo MB (2012) The gastric precancerous cascade. J Dig Dis 13:2–9. https://doi.org/10.1111/j.1751-2980.2011.00550.x
Fard MJ, Ameri S, Darin Ellis R, Chinnam RB, Pandya AK, Klein MD (2018) Automated robot-assisted surgical skill evaluation: predictive analytics approach. Int J Med Robot. https://doi.org/10.1002/rcs.1850
Gao Y et al (2019) Deep neural network-assisted computed tomography diagnosis of metastatic lymph nodes from gastric cancer. Chin Med J 132:2804–2811. https://doi.org/10.1097/CM9.0000000000000532
Hashimoto DA, Rosman G, Rus D, Meireles OR (2018) Artificial intelligence in surgery: promises and perils. Ann Surg 268:70–76. https://doi.org/10.1097/SLA.0000000000002693
Hensler K, Waschulzik T, Mönig SP, Maruyama K, Hölscher AH, Bollschweiler E (2005) Quality-assured Efficient Engineering of Feedforward Neural Networks (QUEEN)—pretherapeutic estimation of lymph node status in patients with gastric carcinoma. Methods Inf Med 44:647–654
Hirasawa T et al (2018) Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 21:653–660. https://doi.org/10.1007/s10120-018-0793-2
Horiuchi Y et al (2020a) Convolutional neural network for differentiating gastric cancer from gastritis using magnified endoscopy with narrow band imaging. Dig Dis Sci. https://doi.org/10.1007/s10620-019-05862-6
Horiuchi Y et al (2020b) Performance of a computer-aided diagnosis system in diagnosing early gastric cancer using magnifying endoscopy videos with narrow-band imaging (with videos). Gastrointest Endosc. https://doi.org/10.1016/j.gie.2020.04.079
Huang CR, Sheu BS, Chung PC, Yang HB (2004) Computerized diagnosis of Helicobacter pylori infection and associated gastric inflammation from endoscopic images by refined feature selection using a neural network. Endoscopy 36:601–608. https://doi.org/10.1055/s-2004-814519
Huang Y, Zhu J, Li W, Zhang Z, Xiong P, Wang H, Zhang J (2018) Serum microRNA panel excavated by machine learning as a potential biomarker for the detection of gastric cancer. Oncol Rep 39:1338–1346. https://doi.org/10.3892/or.2017.6163
Iizuka O, Kanavati F, Kato K, Rambeau M, Arihiro K, Tsuneki M (2020) Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Sci Rep 10:1504. https://doi.org/10.1038/s41598-020-58467-9
Ikenoyama Y et al (2020) Detecting early gastric cancer: comparison between the diagnostic ability of convolutional neural networks and endoscopists. Dig Endosc. https://doi.org/10.1111/den.13688
Ishii H, Sasaki H, Aoyagi K, Yamazaki T (2013) Classification of gastric cancer subtypes using ICA, MLR and Bayesian network. Studi Health Technol Inform 192:1014
Ishioka M, Hirasawa T, Tada T (2019) Detecting gastric cancer from video images using convolutional neural networks. Dig Endosc 31:e34–e35. https://doi.org/10.1111/den.13306
Itoh T, Kawahira H, Nakashima H, Yata N (2018) Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images. Endosc Int Open 6:E139–E144. https://doi.org/10.1055/s-0043-120830
Jagric T, Potrc S, Jagric T (2010) Prediction of liver metastases after gastric cancer resection with the use of learning vector quantization neural networks. Dig Dis Sci 55:3252–3261. https://doi.org/10.1007/s10620-010-1155-z
Jiang Y et al (2017) Prognostic and predictive value of p21-activated kinase 6 associated support vector machine classifier in gastric cancer treated by 5-fluorouracil/oxaliplatin chemotherapy. EBioMedicine 22:78–88. https://doi.org/10.1016/j.ebiom.2017.06.028
Jiang Y et al (2018) Immunomarker support vector machine classifier for prediction of gastric cancer survival and adjuvant chemotherapeutic benefit. Clin Cancer Res 24:5574–5584. https://doi.org/10.1158/1078-0432.ccr-18-0848
Kanesaka T et al (2018) Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging. Gastrointest Endosc 87:1339–1344. https://doi.org/10.1016/j.gie.2017.11.029
Kather JN, Pearson AT, Halama N (2019) Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med 25:1054–1056. https://doi.org/10.1038/s41591-019-0462-y
Korhani Kangi A, Bahrampour A (2018) Predicting the survival of gastric cancer patients using artificial and bayesian neural networks. Asian Pac J Cancer Prevent 19:487–490. https://doi.org/10.22034/apjcp.2018.19.2.487
Kubota K, Kuroda J, Yoshida M, Ohta K, Kitajima M (2012) Medical image analysis: computer-aided diagnosis of gastric cancer invasion on endoscopic images. Surg Endosc 26:1485–1489. https://doi.org/10.1007/s00464-011-2036-z
Lai KC, Chiang HC, Chen WC, Tsai FJ, Jeng LB (2008) Artificial neural network-based study can predict gastric cancer staging. Hepatogastroenterology 55:1859–1863
Lee J et al (2018) Deep learning-based survival analysis identified associations between molecular subtype and optimal adjuvant treatment of patients with gastric cancer. JCO Clin Cancer Inform 2:1–14. https://doi.org/10.1200/CCI.17.00065
Lee JH et al (2019) Spotting malignancies from gastric endoscopic images using deep learning. Surg Endosc 33:3790–3797. https://doi.org/10.1007/s00464-019-06677-2
Li Q, Wang W, Ling X, Wu JG (2013) Detection of gastric cancer with Fourier transform infrared spectroscopy and support vector machine classification. BioMed Res Int 2013:942427. https://doi.org/10.1155/2013/942427
Li L et al (2020) Convolutional neural network for the diagnosis of early gastric cancer based on magnifying narrow band imaging. Gastric Cancer 23:126–132. https://doi.org/10.1007/s10120-019-00992-2
Liu H, Qiao P, Wu X, Wang L, Ao Y, Jia Z, Pi X (2014) A smart capsule system of gastric occult blood detection. Biomed Mater Eng 24:519–528. https://doi.org/10.3233/BME-130838
Liu B, Tan J, Wang X, Liu X (2018) Identification of recurrent risk-related genes and establishment of support vector machine prediction model for gastric cancer. Neoplasma 65:360–366. https://doi.org/10.4149/neo_2018_170507N326
Luo HY et al (2019) Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study. Lancet Oncol 20:1645–1654. https://doi.org/10.1016/s1470-2045(19)30637-0
Miyaki R et al (2013) Quantitative identification of mucosal gastric cancer under magnifying endoscopy with flexible spectral imaging color enhancement. J Gastroenterol Hepatol 28:841–847. https://doi.org/10.1111/jgh.12149
Miyaki R et al (2015) A computer system to be used with laser-based endoscopy for quantitative diagnosis of early gastric cancer. J Clin Gastroenterol 49:108–115. https://doi.org/10.1097/MCG.0000000000000104
Mori H, Miwa H (2019) A histopathologic feature of the behavior of gastric signet-ring cell carcinoma; an image analysis study with deep learning. Pathol Int 69:437–439. https://doi.org/10.1111/pin.12828
Nakashima H, Kawahira H, Kawachi H, Sakaki N (2018a) Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: a single-center prospective study. Ann Gastroenterol 31:462–468. https://doi.org/10.20524/aog.2018.0269
Nakashima H, Kawahira H, Kawachi H, Sakaki N (2018b) Artificial intelligence diagnosis of Helicobacter pylori infection using blue laser imaging-bright and linked color imaging: a single-center prospective study. BioMed Res Int 31:462–468. https://doi.org/10.20524/aog.2018.0269
Nakashima H, Kawahira H, Kawachi H, Sakaki N (2020) Endoscopic three-categorical diagnosis of Helicobacter pylori infection using linked color imaging and deep learning: a single-center prospective study (with video). Gastric Cancer. https://doi.org/10.1007/s10120-020-01077-1
Nilsaz-Dezfouli H, Abu-Bakar MR, Arasan J, Adam MB, Pourhoseingholi MA (2017) Improving gastric cancer outcome prediction using single time-point artificial neural network models. Cancer Inform 16:1176935116686062. https://doi.org/10.1177/1176935116686062
Oh SE, Seo SW, Choi MG, Sohn TS, Bae JM, Kim S (2018) Prediction of overall survival and novel classification of patients with gastric cancer using the survival recurrent network. Ann Surg Oncol 25:1153–1159. https://doi.org/10.1245/s10434-018-6343-7
O'Sullivan S et al (2019) Legal, regulatory, and ethical frameworks for development of standards in artificial intelligence (AI) and autonomous robotic surgery. Int J Med Robot 15:e1968. https://doi.org/10.1002/rcs.1968
Polkowski M, Palucki J, Wronska E, Szawlowski A, Nasierowska-Guttmejer A, Butruk E (2004) Endosonography versus helical computed tomography for locoregional staging of gastric cancer. Endoscopy 36:617–623. https://doi.org/10.1055/s-2004-814522
Qu J, Hiruta N, Terai K, Nosato H (2018) Gastric pathology image classification using stepwise fine-tuning for deep neural networks. J Healthc Eng 2018:8961781. https://doi.org/10.1155/2018/8961781
Que SJ et al (2019) Application of preoperative artificial neural network based on blood biomarkers and clinicopathological parameters for predicting long-term survival of patients with gastric cancer. World J Gastroenterol 25:6451–6464. https://doi.org/10.3748/wjg.v25.i43.6451
Sakai Y, Takemoto S, Hori K, Nishimura M, Ikematsu H, Yano T, Yokota H (2018) Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network. Conf Proc IEEE Eng Med Biol Soc 2018:4138–4141. https://doi.org/10.1109/embc.2018.8513274
Sharma H, Zerbe N, Klempert I, Hellwich O, Hufnagl P (2017) Deep convolutional neural networks for automatic classification of gastric carcinoma using whole slide images in digital histopathology. Comput Med Imaging Graph 61:2–13. https://doi.org/10.1016/j.compmedimag.2017.06.001
Shichijo S et al (2017) Application of convolutional neural networks in the diagnosis of Helicobacter pylori infection based on endoscopic images. EBioMedicine 25:106–111. https://doi.org/10.1016/j.ebiom.2017.10.014
Shichijo S et al (2019) Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images. Scand J Gastroenterol 54:158–163. https://doi.org/10.1080/00365521.2019.1577486
Su F et al (2020) Development and validation of a deep learning system for ascites cytopathology interpretation. Gastric Cancer. https://doi.org/10.1007/s10120-020-01093-1
Togo R et al (2019) Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography. J Gastroenterol 54:321–329. https://doi.org/10.1007/s00535-018-1514-7
Wang J, Li M, Hu YT, Zhu Y (2009) Comparison of hospital charge prediction models for gastric cancer patients: neural network vs. decision tree models. BMC Health Serv Res 9:161. https://doi.org/10.1186/1472-6963-9-161
Wang K, Duan X, Gao F, Wang W, Liu L, Wang X (2018) Dissecting cancer heterogeneity based on dimension reduction of transcriptomic profiles using extreme learning machines. PLoS One 13:e0203824. https://doi.org/10.1371/journal.pone.0203824
Wu L et al (2019) A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy 51:522–531. https://doi.org/10.1055/a-0855-3532
Xu YG, Cheng M, Zhang X, Sun SH, Bi WM (2017) Mutual information network-based support vector machine strategy identifies salivary biomarkers in gastric cancer. Gut 22:119–125. https://doi.org/10.1136/gutjnl-2018-317645
Yoon HJ, Kim S, Kim JH (2019a) A lesion-based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer. J Clin Med. https://doi.org/10.3390/jcm8091310
Yoon HJ et al (2019b) A lesion-based convolutional neural network improves endoscopic detection and depth prediction of early gastric cancer. J Clin Med. https://doi.org/10.3390/jcm8091310
Zhang F, Xu W, Liu J, Liu X, Huo B, Li B, Wang Z (2018) Optimizing miRNA-module diagnostic biomarkers of gastric carcinoma via integrated network analysis. PLoS One 13:e0198445. https://doi.org/10.1371/journal.pone.0198445
Zhang YQ et al (2020) Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence. Dig Liver Dis 52:566–572. https://doi.org/10.1016/j.dld.2019.12.146
Zhu L, Luo W, Su M, Wei H, Wei J, Zhang X, Zou C (2013) Comparison between artificial neural network and Cox regression model in predicting the survival rate of gastric cancer patients. Biomed Rep 1:757–760. https://doi.org/10.3892/br.2013.140
Zhu Y et al (2019) Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc 89:806. https://doi.org/10.1016/j.gie.2018.11.011
Acknowledgements
The authors declare that they have no competing interests. The research was sponsored by National Natural Science Foundation of China, No. 81772642; Capital’s Funds for Health Improvement and Research, No. CFH 2018-2-4022; Wu Jieping Medical Foundation, No. 320.6750.15276; Beijing Hope Run Special Fund of Cancer Foundation of China, No. LC2019L05.
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Jin, P., Ji, X., Kang, W. et al. Artificial intelligence in gastric cancer: a systematic review. J Cancer Res Clin Oncol 146, 2339–2350 (2020). https://doi.org/10.1007/s00432-020-03304-9
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DOI: https://doi.org/10.1007/s00432-020-03304-9