Artificial intelligence in gastric cancer: Application and future perspectives

Niu, Penghui; Zhao, Lulu; Wu, Hongliang; Zhao, Dongbing; Yingtai, Chen

doi:10.3748/wjg.v26.i36.5408

Cited by 93 publications

(61 citation statements)

References 68 publications

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“…AI has gained much attention in recent years. In the field of GI endoscopy, DL is also a promising innovation in the identification and characterization of lesions[ 9 - 13 ]. Many successful studies have focused on GI cancers.…”

Section: Challenges and Recommendationsmentioning

confidence: 99%

“…Over the past few decades, AI techniques such as machine learning (ML) and deep learning (DL) have been widely used in endoscopic imaging to improve the diagnostic accuracy and efficiency of various GI lesions[ 9 - 13 ]. The exact definition of AI, ML and DL can be misunderstood by physicians.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the above limitations, DL-based AI systems are revolutionizing GI endoscopy. While there are several surveys on DL for GI cancers[ 9 - 13 ], no specific review on the application of DL in the endoscopic diagnosis of precancerous lesions is available in the literature. Therefore, the performance of DL on gastroenterology is summarized in this review, with an emphasis on the automatic diagnosis of precancerous lesions in the upper GI tract.…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

Yan

Wong

Qin

2021

WJG

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Upper gastrointestinal (GI) cancers are the leading cause of cancer-related deaths worldwide. Early identification of precancerous lesions has been shown to minimize the incidence of GI cancers and substantiate the vital role of screening endoscopy. However, unlike GI cancers, precancerous lesions in the upper GI tract can be subtle and difficult to detect. Artificial intelligence techniques, especially deep learning algorithms with convolutional neural networks, might help endoscopists identify the precancerous lesions and reduce interobserver variability. In this review, a systematic literature search was undertaken of the Web of Science, PubMed, Cochrane Library and Embase, with an emphasis on the deep learning-based diagnosis of precancerous lesions in the upper GI tract. The status of deep learning algorithms in upper GI precancerous lesions has been systematically summarized. The challenges and recommendations targeting this field are comprehensively analyzed for future research.

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Section: Challenges and Recommendationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

Yan

Wong

Qin

2021

WJG

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“…Among the three common tumor diseases of the digestive tract, esophageal cancer, gastric cancer, and colon cancer, the value of AI has been fully demonstrated. Multiple AI algorithms have been developed to run real-time during gastroscopy and colonoscopy for cancerous detection, diagnosis, or invasion depth measurement[ 48 , 52 , 53 ]. The application of AI in the field of gastric cancer and colon cancer seems to be earlier and more comprehensive than that in the field of esophageal cancer.…”

Section: Limitation Of Ai Application In Early Ec Detectionmentioning

confidence: 99%

Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step?

Liu¹

2021

WJG

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Esophageal cancer (EC) is a common malignant tumor of the digestive tract and originates from the epithelium of the esophageal mucosa. It has been confirmed that early EC lesions can be cured by endoscopic therapy, and the curative effect is equivalent to that of surgical operation. Upper gastrointestinal endoscopy is still the gold standard for EC diagnosis. The accuracy of endoscopic examination results largely depends on the professional level of the examiner. Artificial intelligence (AI) has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. This paper reviews the application of AI in the field of endoscopic detection of early EC, including squamous cell carcinoma and adenocarcinoma, and describes the relevant progress. Although up to now most of the studies evaluating the clinical application of AI in early EC endoscopic detection are focused on still images, AI-assisted real-time detection based on live-stream video may be the next step.

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“…ML methods have been widely employed in bioinformatics [35,36] but recently also in the health area and especially in support of cancer management including diagnosis, prognosis and treatment. Several studies, for example, have attempted to use deep learning (DL) to help identify dysplasia and early esophageal cancer [37] while different AI models have been developed to evaluate different aspects of gastric cancer such as the diagnosis or prognosis [38]. In addition, DL models have been used in breast cancer to identify potential diagnostic biomarkers [39] and to improve the accuracy in the histologic classification [40] or diagnosis [41].…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy

et al. 2021

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The application of machine learning (ML) techniques could facilitate the identification of predictive biomarkers of somatostatin analog (SSA) efficacy in patients with neuroendocrine tumors (NETs). We collected data from 74 patients with a pancreatic or gastrointestinal NET who received SSA as first-line therapy. We developed three classification models to predict whether the patient would experience a progressive disease (PD) after 12 or 18 months based on clinic-pathological factors at the baseline. The dataset included 70 samples and 15 features. We initially developed three classification models with accuracy ranging from 55% to 70%. We then compared ten different ML algorithms. In all but one case, the performance of the Multinomial Naïve Bayes algorithm (80%) was the highest. The support vector machine classifier (SVC) had a higher performance for the recall metric of the progression-free outcome (97% vs. 94%). Overall, for the first time, we documented that the factors that mainly influenced progression-free survival (PFS) included age, the number of metastatic sites and the primary site. In addition, the following factors were also isolated as important: adverse events G3–G4, sex, Ki67, metastatic site (liver), functioning NET, the primary site and the stage. In patients with advanced NETs, ML provides a predictive model that could potentially be used to differentiate prognostic groups and to identify patients for whom SSA therapy as a single agent may not be sufficient to achieve a long-lasting PFS.

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Artificial intelligence in gastric cancer: Application and future perspectives

Cited by 93 publications

References 68 publications

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

Deep learning for diagnosis of precancerous lesions in upper gastrointestinal endoscopy: A review

Artificial intelligence-assisted endoscopic detection of esophageal neoplasia in early stage: The next step?

A Machine Learning Decision Support System (DSS) for Neuroendocrine Tumor Patients Treated with Somatostatin Analog (SSA) Therapy

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