Prediction of liver metastases after gastric cancer resection with the use of learning vector quantization neural networks

Role of artificial intelligence in gastrointestinal surgery

Artificial intelligence is rapidly evolving and its application is increasing day-by-day in the medical field. The application of artificial intelligence is also valuable in gastrointestinal diseases, by calculating various scoring systems, evaluating radiological images, preoperative and intraoperative assistance, processing pathological slides, prognosticating, and in treatment responses. This field has a promising future and can have an impact on many management algorithms. In this minireview, we aimed to determine the basics of artificial intelligence, the role that artificial intelligence may play in gastrointestinal surgeries and malignancies, and the limitations thereof.

Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review

Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.

Artificial Intelligence-Enabled Gastric Cancer Interpretations: Are We There yet?

The integration of digital pathology and artificial intelligence (AI) is revolutionizing pathology by providing pathologists with new tools to improve workflow, enhance diagnostic accuracy, and undertake novel discovery. The capability of AI to recognize patterns and features in digital images beyond human perception is particularly valuable, thereby providing additional information for prognostic and predictive purposes. AI-based tools diagnose gastric carcinoma in digital images, detect gastric carcinoma metastases in lymph nodes, automate Ki-67 scoring in gastric neuroendocrine tumors, and quantify tumor-infiltrating lymphocytes. This article provides an overview of all of these applications of AI pertaining to gastric cancer.

Artificial intelligence applications in pathological diagnosis of gastric cancer

Globally, gastric cancer is the third leading cause of death from tumors. Prevention and individualized treatment are considered to be the best options for reducing the mortality rate of gastric cancer. Artificial intelligence (AI) technology has been widely used in the field of gastric cancer, including diagnosis, prognosis, and image analysis. Eligible papers were identified from PubMed and IEEE up to April 13, 2022. Through the comparison of these articles, the application status of AI technology in the diagnosis of gastric cancer was summarized, including application types, application scenarios, advantages and limitations. This review presents the current state and role of AI in the diagnosis of gastric cancer based on four aspects: 1) accurate sampling from early diagnosis (endoscopy), 2) digital pathological diagnosis, 3) molecules and genes, and 4) clinical big data analysis and prognosis prediction. AI plays a very important role in facilitating the diagnosis of gastric cancer; however, it also has shortcomings such as interpretability. The purpose of this review is to provide assistance to researchers working in this domain.

Artificial intelligence in pathological evaluation of gastrointestinal cancers

The integration of artificial intelligence (AI) has shown promising benefits in many fields of diagnostic histopathology, including for gastrointestinal cancers (GCs), such as tumor identification, classification, and prognosis prediction. In parallel, recent evidence suggests that AI may help reduce the workload in gastrointestinal pathology by automatically detecting tumor tissues and evaluating prognostic parameters. In addition, AI seems to be an attractive tool for biomarker/genetic alteration prediction in GC, as it can contain a massive amount of information from visual data that is complex and partially understandable by pathologists. From this point of view, it is suggested that advances in AI could lead to revolutionary changes in many fields of pathology. Unfortunately, these findings do not exclude the possibility that there are still many hurdles to overcome before AI applications can be safely and effectively applied in actual pathology practice. These include a broad spectrum of challenges from needs identification to cost-effectiveness. Therefore, unlike other disciplines of medicine, no histopathology-based AI application, including in GC, has ever been approved either by a regulatory authority or approved for public reimbursement. The purpose of this review is to present data related to the applications of AI in pathology practice in GC and present the challenges that need to be overcome for their implementation.

Artificial intelligence in gastroenterology: A state-of-the-art review

The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.

Status quo and future prospects of artificial neural network from the perspective of gastroenterologists

Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.

Artificial intelligence in gastric cancer: Application and future perspectives

Gastric cancer is the fourth leading cause of cancer-related mortality across the globe, with a 5-year survival rate of less than 40%. In recent years, several applications of artificial intelligence (AI) have emerged in the gastric cancer field based on its efficient computational power and learning capacities, such as image-based diagnosis and prognosis prediction. AI-assisted diagnosis includes pathology, endoscopy, and computerized tomography, while researchers in the prognosis circle focus on recurrence, metastasis, and survival prediction. In this review, a comprehensive literature search was performed on articles published up to April 2020 from the databases of PubMed, Embase, Web of Science, and the Cochrane Library. Thereby the current status of AI-applications was systematically summarized in gastric cancer. Moreover, future directions that target this field were also analyzed to overcome the risk of overfitting AI models and enhance their accuracy as well as the applicability in clinical practice.

Artificial intelligence in gastric cancer: a systematic review

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.

Surgery for Liver Metastases From Gastric Cancer: A Meta-Analysis of Observational Studies

The role of surgical therapy in patients with liver metastases from gastric cancer is still controversial. In this study, we investigated the results obtained with local treatment of hepatic metastases in patients with gastric cancer, by performing a systematic literature review and meta-analysis.We performed a systematic review and meta-analysis of observational studies published between 1990 and 2014. These works included multiple studies that evaluated the different survival rate among patients who underwent local treatment, such as hepatectomy or radiofrequency ablation, for hepatic metastases derived from primary gastric cancer. The collected studies were evaluated for heterogeneity, publication bias, and quality, and a pooled hazard ratio (HR) was calculated with a confidence interval estimated at 95% (95% CI).After conducting a thorough research among all published works, 2337 studies were found and after the review process 11 observational studies were included in the analysis. The total amount of patients considered in the survival analysis was 1010. An accurate analysis of all included studies reported a significantly higher survival rate in the group of patients who underwent the most aggressive local treatment for hepatic metastases (HR 0.54, 95% CI 0.46-0.95) as opposed to patients who underwent only palliation or systemic treatment. Furthermore, palliative local treatment of hepatic metastases had a higher survival rate if compared to surgical (without liver surgery) and systemic palliation (HR 0.50, 95% CI 0.26-0.96). Considering the only 3 studies where data from multivariate analyses was available, we found a higher survival rate in the local treatment groups, but the difference was not significant (HR 0.50, 95% CI 0.22-1.15).Curative and also palliative surgery of liver metastases from gastric cancer may improve patients' survival. However, further trials are needed in order to better understand the role of surgery in this group of patients.

Surgery for Liver Metastases From Gastric Cancer: A Meta-Analysis of Observational Studies

The role of surgical therapy in patients with liver metastases from gastric cancer is still controversial. In this study, we investigated the results obtained with local treatment of hepatic metastases in patients with gastric cancer, by performing a systematic literature review and meta-analysis.We performed a systematic review and meta-analysis of observational studies published between 1990 and 2014. These works included multiple studies that evaluated the different survival rate among patients who underwent local treatment, such as hepatectomy or radiofrequency ablation, for hepatic metastases derived from primary gastric cancer. The collected studies were evaluated for heterogeneity, publication bias, and quality, and a pooled hazard ratio (HR) was calculated with a confidence interval estimated at 95% (95% CI).After conducting a thorough research among all published works, 2337 studies were found and after the review process 11 observational studies were included in the analysis. The total amount of patients considered in the survival analysis was 1010. An accurate analysis of all included studies reported a significantly higher survival rate in the group of patients who underwent the most aggressive local treatment for hepatic metastases (HR 0.54, 95% CI 0.46-0.95) as opposed to patients who underwent only palliation or systemic treatment. Furthermore, palliative local treatment of hepatic metastases had a higher survival rate if compared to surgical (without liver surgery) and systemic palliation (HR 0.50, 95% CI 0.26-0.96). Considering the only 3 studies where data from multivariate analyses was available, we found a higher survival rate in the local treatment groups, but the difference was not significant (HR 0.50, 95% CI 0.22-1.15).Curative and also palliative surgery of liver metastases from gastric cancer may improve patients' survival. However, further trials are needed in order to better understand the role of surgery in this group of patients.

Prognostic significance of radical surgical treatment for gastric cancer patients with synchronous liver metastases

It has been widely accepted that radical resection is the primary consideration to improve the survival rate for gastric cancer, but it is still controversial whether surgery could bring any substantial survival benefit to gastric cancer patients with synchronous liver metastasis. We retrospectively analyzed pathological and clinical data of 39 gastric patients with liver metastasis who underwent gastric-hepatic radical resection to explore the related prognostic factors. In the whole group of 39 patients, 1-, 2-, 3- and 5-year RFS rates were 30.8, 12.8, 10.3 and 7.7 %; 1-, 2-, 3- and 5-year overall survival (OS) rates were 56.4, 25.6, 17.9 and 10.3 %, respectively. Compared with patients without surgery, operative ones had a statistically significant long-term survival rate. With univariate analysis, lymph node metastasis (N stage), soft tissue invasion and number of liver metastases were significant prognostic factors associated with OS time of synchronous liver metastasis after radical gastrectomy (P < 0.05). What is more, N stage and number of liver metastases were independent factors associated with OS in multivariate analysis. For gastric adenocarcinoma with liver metastases, surgery maybe a superior option if complete resection of gastric and hepatic lesions is feasible and careful postoperative supporting treatment could be received at the same time, especially ones who had less number of liver metastases.