The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis

Shi, Yiheng; Fan, Haohan; Li, Li; Hou, Yaqi; Qian, Feifei; Zhuang, Mengting; Miao, Bei; Fei, Sujuan

doi:10.1186/s12957-024-03321-9

Cited by 3 publications

(1 citation statement)

References 58 publications

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“…Concerning AI models and gastrointestinal pathology, recent medical literature shows that this is an exponentially growing field, which indirectly translates the existing interest in this area. We found promising models for different pathologies, such as gastric cancer, liver fibrosis and cirrhosis, gastrointestinal stromal tumors, and Barrett's esophagus, among others[ 4 , 5 ]. Overall, all these models (both diagnostic and prognostic) show very high performances that exceed the gold standards previously used.…”

Section: Artificial Intelligencementioning

confidence: 99%

From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery

Arredondo Montero

2024

World J Gastrointest Surg

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Recent medical literature shows that the application of artificial intelligence (AI) models in gastrointestinal pathology is an exponentially growing field, with promising models that show very high performances. Regarding inflammatory bowel disease (IBD), recent reviews demonstrate promising diagnostic and prognostic AI models. However, studies are generally at high risk of bias (especially in AI models that are image-based). The creation of specific AI models that improve diagnostic performance and allow the establishment of a general prognostic forecast in IBD is of great interest, as it may allow the stratification of patients into subgroups and, in turn, allow the creation of different diagnostic and therapeutic protocols for these patients. Regarding surgical models, predictive models of postoperative complications have shown great potential in large-scale studies. In this work, the authors present the development of a predictive algorithm for early post-surgical complications in Crohn's disease based on a Random Forest model with exceptional predictive ability for complications within the cohort. The present work, based on logical and reasoned, clinical, and applicable aspects, lays a solid foundation for future prospective work to further develop post-surgical prognostic tools for IBD. The next step is to develop in a prospective and multicenter way, a collaborative path to optimize this line of research and make it applicable to our patients.

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Section: Artificial Intelligencementioning

confidence: 99%

From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery

Arredondo Montero

2024

World J Gastrointest Surg

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Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms

Kikuchi,

Okamoto,

Ozawa

et al. 2024

Digestion

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Background: Artificial intelligence (AI) using deep learning systems has recently been utilized in various medical fields. In the field of gastroenterology, AI is primarily implemented in image recognition and utilized in the realm of gastrointestinal (GI) endoscopy. In GI endoscopy, computer-aided detection/diagnosis (CAD) systems assist endoscopists in GI neoplasm detection or differentiation of cancerous or non-cancerous lesions. Several AI systems for colorectal polyps have already been applied in colonoscopy clinical practices. In esophagogastroduodenoscopy, a few CAD systems for upper GI neoplasms have been launched in Asian countries. The usefulness of these CAD systems in GI endoscopy has been gradually elucidated. Summary: In this review, we outline recent articles on several studies of endoscopic AI systems for GI neoplasms, focusing on esophageal squamous cell carcinoma (ESCC), esophageal adenocarcinoma (EAC), gastric cancer (GC), and colorectal polyps. In ESCC and EAC, computer-aided detection (CADe) systems were mainly developed, and a recent meta-analysis study showed sensitivities of 91.2% and 93.1% and specificities of 80% and 86.9%, respectively. In GC, a recent meta-analysis study on CADe systems demonstrated that their sensitivity and specificity were as high as 90%. A randomized controlled trial (RCT) also showed that the use of the CADe system reduced the miss rate. Regarding computer-aided diagnosis (CADx) systems for GC, although RCTs have not yet been conducted, most studies have demonstrated expert-level performance. In colorectal polyps, multiple RCTs have shown the usefulness of the CADe system for improving the polyp detection rate, and several CADx systems have been shown to have high accuracy in colorectal polyp differentiation. Key Messages: Most analyses of endoscopic AI systems suggested that their performance was better than that of non-expert endoscopists and equivalent to that of expert endoscopists. Thus, Endoscopic AI systems may be useful for reducing the risk of overlooking lesions and improving the diagnostic ability of endoscopists.

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Predicting early gastric cancer risk using machine learning: A population-based retrospective study

Ke,

Cai,

Bian

et al. 2024

DIGITAL HEALTH

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Background Early detection and treatment are crucial for reducing gastrointestinal tumour-related mortality. The diagnostic efficiency of the most commonly used diagnostic markers for gastric cancer (GC) is not very high. A single laboratory test cannot meet the requirements of early screening, and machine learning methods are needed to aid the early diagnosis of GC by combining multiple indicators. Methods Based on the XGBoost algorithm, a new model was developed to distinguish between GC and precancerous lesions in newly admitted patients between 2018 and 2023 using multiple laboratory tests. We evaluated the ability of the prediction score derived from this model to predict early GC. In addition, we investigated the efficacy of the model in correctly screening for GC given negative protein tumour marker results. Results The XHGC20 model constructed using the XGBoost algorithm could distinguish GC from precancerous disease well (area under the receiver operating characteristic curve [AUC] = 0.901), with a sensitivity, specificity and cut-off value of 0.830, 0.806 and 0.265, respectively. The prediction score was very effective in the diagnosis of early GC. When the cut-off value was 0.27, and the AUC was 0.888, the sensitivity and specificity were 0.797 and 0.807, respectively. The model was also effective at evaluating GC given negative conventional markers (AUC = 0.970), with the sensitivity and specificity of 0.941 and 0.906, respectively, which helped to reduce the rate of missed diagnoses. Conclusions The XHGC20 model established by the XGBoost algorithm integrates information from 20 clinical laboratory tests and can aid in the early screening of GC, providing a useful new method for auxiliary laboratory diagnosis.

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The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis

Cited by 3 publications

References 58 publications

From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery

From the mathematical model to the patient: The scientific and human aspects of artificial intelligence in gastrointestinal surgery

Endoscopic Artificial Intelligence for Image Analysis in Gastrointestinal Neoplasms

Predicting early gastric cancer risk using machine learning: A population-based retrospective study

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