Artificial intelligence in gastric cancer: Application and future perspectives

SIAH1 facilitates the migration and invasion of gastric cancer cells through promoting the ubiquitination and degradation of RECK

Siah E3 ubiquitin protein ligase 1 (SIAH1) has been reported to participate in the development of several human cancers, including gastric cancer. However, the effect and mechanism of SIAH1 on the migration and invasion of gastric cancer cells need be further explored. Here, we first analyzed the clinical value of SIAH1 in gastric cancer, and found that SIAH1 was up-regulated in gastric cancer and associated with a poor prognosis. In addition, silencing of SIAH1 significantly inhibited the migration and invasion of gastric cancer cells through inhibiting the expression of matrix metalloproteinase-9 (MMP9), while overexpression of SIAH1 had the opposite effect. Molecularly, we provided the evidence that reversion-inducing cysteine-rich protein with Kazal motifs (RECK) was a potential substrate of SIAH1. We determined that SIAH1 could destabilize RECK through promoting its ubiquitination and degradation via proteasome pathway. We also found RECK was involved in SIAH1-regulated gastric cancer cell migration and invasion. In conclusion, SIAH1 is up-regulated in gastric cancer, which promotes the migration and invasion of gastric cancer cells through regulating RECK-MMP9 pathway.

Improving diagnosis and outcome prediction of gastric cancer via multimodal learning using whole slide pathological images and gene expression

For the diagnosis and outcome prediction of gastric cancer (GC), machine learning methods based on whole slide pathological images (WSIs) have shown promising performance and reduced the cost of manual analysis. Nevertheless, accurate prediction of GC outcome may rely on multiple modalities with complementary information, particularly gene expression data. Thus, there is a need to develop multimodal learning methods to enhance prediction performance. In this paper, we collect a dataset from Ruijin Hospital and propose a multimodal learning method for GC diagnosis and outcome prediction, called GaCaMML, which is featured by a cross-modal attention mechanism and Per-Slide training scheme. Additionally, we perform feature attribution analysis via integrated gradient (IG) to identify important input features. The proposed method improves prediction accuracy over the single-modal learning method on three tasks, i.e., survival prediction (by 4.9% on C-index), pathological stage classification (by 11.6% on accuracy), and lymph node classification (by 12.0% on accuracy). Especially, the Per-Slide strategy addresses the issue of a high WSI-to-patient ratio and leads to much better results compared with the Per-Person training scheme. For the interpretable analysis, we find that although WSIs dominate the prediction for most samples, there is still a substantial portion of samples whose prediction highly relies on gene expression information. This study demonstrates the great potential of multimodal learning in GC-related prediction tasks and investigates the contribution of WSIs and gene expression, respectively, which not only shows how the model makes a decision but also provides insights into the association between macroscopic pathological phenotypes and microscopic molecular features.

Novel milestones for early esophageal carcinoma: From bench to bed

Esophageal cancer (EC) is the seventh most common cancer worldwide, and esophageal squamous cell carcinoma (ESCC) accounts for the majority of cases of EC. To effectively diagnose and treat ESCC and improve patient prognosis, timely diagnosis in the initial phase of the illness is necessary. This article offers a detailed summary of the latest advancements and emerging technologies in the timely identification of ECs. Molecular biology and epigenetics approaches involve the use of molecular mechanisms combined with fluorescence quantitative polymerase chain reaction (qPCR), high-throughput sequencing technology (next-generation sequencing), and digital PCR technology to study endogenous or exogenous biomolecular changes in the human body and provide a decision-making basis for the diagnosis, treatment, and prognosis of diseases. The investigation of the microbiome is a swiftly progressing area in human cancer research, and microorganisms with complex functions are potential components of the tumor microenvironment. The intratumoral microbiota was also found to be connected to tumor progression. The application of endoscopy as a crucial technique for the early identification of ESCC has been essential, and with ongoing advancements in technology, endoscopy has continuously improved. With the advancement of artificial intelligence (AI) technology, the utilization of AI in the detection of gastrointestinal tumors has become increasingly prevalent. The implementation of AI can effectively resolve the discrepancies among observers, improve the detection rate, assist in predicting the depth of invasion and differentiation status, guide the pericancerous margins, and aid in a more accurate diagnosis of ESCC.

The antidepressive mechanism of Longya Lilium combined with Fluoxetine in mice with depression-like behaviors

Traditional Chinese medicine is one of the most commonly used complementary and alternative medicine therapies for depression. Integrated Chinese-western therapies have been extensively applied in numerous diseases due to their superior efficiency in individual treatment. We used the meta-analysis, network pharmacology, and bioinformatics studies to identify the putative role of Longya Lilium combined with Fluoxetine in depression. Depression-like behaviors were mimicked in mice after exposure to the chronic unpredictable mild stress (CUMS). The underlying potential mechanism of this combination therapy was further explored based on in vitro and in vivo experiments to analyze the expression of COX-2, PGE2, and IL-22, activation of microglial cells, and neuron viability and apoptosis in the hippocampus. The antidepressant effect was noted for the combination of Longya Lilium with Fluoxetine in mice compared to a single treatment. COX-2 was mainly expressed in hippocampal CA1 areas. Longya Lilium combined with Fluoxetine reduced the expression of COX-2 and thus alleviated depression-like behavior and neuroinflammation in mice. A decrease of COX-2 curtailed BV-2 microglial cell activation, inflammation, and neuron apoptosis by blunting the PGE2/IL-22 axis. Therefore, a combination of Longya Lilium with Fluoxetine inactivates the COX-2/PGE2/IL-22 axis, consequently relieving the neuroinflammatory response and the resultant depression.

Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?

Artificial intelligence (AI)-based diagnostic tools can offer numerous benefits to the field of histopathology, including improved diagnostic accuracy, efficiency and productivity. As a result, such tools are likely to have an increasing role in routine practice. However, all AI tools are prone to errors, and these AI-associated errors have been identified as a major risk in the introduction of AI into healthcare. The errors made by AI tools are different, in terms of both cause and nature, to the errors made by human pathologists. As highlighted by the National Institute for Health and Care Excellence, it is imperative that practising pathologists understand the potential limitations of AI tools, including the errors made. Pathologists are in a unique position to be gatekeepers of AI tool use, maximizing patient benefit while minimizing harm. Furthermore, their pathological knowledge is essential to understanding when, and why, errors have occurred and so to developing safer future algorithms. This paper summarises the literature on errors made by AI diagnostic tools in histopathology. These include erroneous errors, data concerns (data bias, hidden stratification, data imbalances, distributional shift, and lack of generalisability), reinforcement of outdated practices, unsafe failure mode, automation bias, and insensitivity to impact. Methods to reduce errors in both tool design and clinical use are discussed, and the practical roles for pathologists in error minimisation are highlighted. This aims to inform and empower pathologists to move safely through this seismic change in practice and help ensure that novel AI tools are adopted safely.

Landscape of Innovative Methods for Early Diagnosis of Gastric Cancer: A Systematic Review

From a global perspective, gastric cancer (GC) persists as a significant healthcare issue. In the Western world, the majority of cases are discovered at late stages, when the treatment is generally unsuccessful. There are no organized screening programs outside of Asia (Japan and Republic of Korea). Traditional diagnosis techniques (such as upper endoscopy), conventional tumor markers (CEA, CA19-9, and CA72-4), radiographic imaging, and CT scanning all have drawbacks. The gold standard for the earliest detection of cancer and related premalignant lesions is still endoscopy with a proper biopsy follow-up. Since there are currently no clinically approved biomarkers for the early diagnosis of GC, the identification of non-invasive biomarkers is expected to help improve the prognosis and survival rate of these patients. The search for new screening biomarkers is currently underway. These include genetic biomarkers, such as circulating tumor cells, microRNAs, and exosomes, as well as metabolic biomarkers obtained from biofluids. Meanwhile, cutting-edge high-resolution endoscopic technologies are demonstrating promising outcomes in the visual diagnosis of mucosal lesions with the aid of linked color imaging and machine learning models. Following the PRISMA guidelines, this study examined the articles in databases such as PubMed, resulting in 167 included articles. This review discusses the currently available and emerging methods for diagnosing GC early on, as well as new developments in the endoscopic detection of early lesions of the stomach.

Automated machine learning-based model for predicting benign anastomotic strictures in patients with rectal cancer who have received anterior resection

BACKGROUND

Benign anastomotic strictures (BAS) significantly impact patients' quality of life and long-term prognosis. However, the current clinical practice lacks accurate tools for predicting BAS. This study aimed to develop a machine-learning model to predict BAS in patients with rectal cancer who have undergone anterior resection.

METHODS

Data from 1973 patients who underwent anterior resection for rectal cancer were collected. Multiple machine learning classification models were integrated to analyze the data and identify the optimal model. Model performance was evaluated using receiver operator characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. The Shapley Additive exPlanation (SHAP) algorithm was utilized to assess the impact of various clinical characteristics on the optimal model to enhance the interpretability of the model results.

RESULTS

A total of 10 clinical features were considered in constructing the machine learning model. The model evaluation results indicated that the random forest (RF)model was optimal, with the area under the test set curve (AUC: 0.888, 95% CI: 0.810-0.965), accuracy: 0.792, sensitivity: 0.846, specificity: 0.791. The SHAP algorithm analysis identified prophylactic ileostomy, operative time, and anastomotic leakage as significant contributing factors influencing the predictions of the RF model.

CONCLUSION

We developed a robust machine-learning model and user-friendly online prediction tool for predicting BAS following anterior resection of rectal cancer. This tool offers a potential foundation for BAS prevention and aids clinical practice by enabling more efficient disease management and precise medical interventions.

A discrimination model by machine learning to avoid gastrectomy for early gastric cancer

Aim

Gastrectomy is recommended for patients with early gastric cancer (EGC) because the possibility of lymph node metastasis (LNM) cannot be completely denied. The aim of this study was to develop a discrimination model to select patients who do not require surgery using machine learning.

Methods

Data from 382 patients who received gastrectomy for gastric cancer and who were diagnosed with pT1b were extracted for developing a discrimination model. For the validation of this discrimination model, data from 140 consecutive patients who underwent endoscopic resection followed by gastrectomy, with a diagnosis of pT1b EGC, were extracted. We applied XGBoost to develop a discrimination model for clinical and pathological variables. The performance of the discrimination model was evaluated based on the number of cases classified as true negatives for LNM, with no false negatives for LNM allowed.

Results

Lymph node metastasis was observed in 95 patients (25%) in the development cohort and 11 patients (8%) in the validation cohort. The discrimination model was developed to identify 27 (7%) patients with no indications for additional surgery due to the prediction of an LNM-negative status with no false negatives. In the validation cohort, 13 (9%) patients were identified as having no indications for additional surgery and no patients with LNM were classified into this group.

Conclusion

The discrimination model using XGBoost algorithms could select patients with no risk of LNM from patients with pT1b EGC. This discrimination model was considered promising for clinical decision-making in relation to patients with EGC.

The Importance of Artificial Intelligence in Upper Gastrointestinal Endoscopy

Recently, there has been a growing interest in the application of artificial intelligence (AI) in medicine, especially in specialties where visualization methods are applied. AI is defined as a computer's ability to achieve human cognitive performance, which is accomplished through enabling computer "learning". This can be conducted in two ways, as machine learning and deep learning. Deep learning is a complex learning system involving the application of artificial neural networks, whose algorithms imitate the human form of learning. Upper gastrointestinal endoscopy allows examination of the esophagus, stomach and duodenum. In addition to the quality of endoscopic equipment and patient preparation, the performance of upper endoscopy depends on the experience and knowledge of the endoscopist. The application of artificial intelligence in endoscopy refers to computer-aided detection and the more complex computer-aided diagnosis. The application of AI in upper endoscopy is aimed at improving the detection of premalignant and malignant lesions, with special attention on the early detection of dysplasia in Barrett's esophagus, the early detection of esophageal and stomach cancer and the detection of H. pylori infection. Artificial intelligence reduces the workload of endoscopists, is not influenced by human factors and increases the diagnostic accuracy and quality of endoscopic methods.

Bibliometric analysis of the global scientific production on machine learning applied to different cancer types

Cancer disease is one of the main causes of death in the world, with million annual cases in the last decades. The need to find a cure has stimulated the search for efficient treatments and diagnostic procedures. One of the most promising tools that has emerged against cancer in recent years is machine learning (ML), which has raised a huge number of scientific papers published in a relatively short period of time. The present study analyzes global scientific production on ML applied to the most relevant cancer types through various bibliometric indicators. We find that over 30,000 studies have been published so far and observe that cancers with the highest number of published studies using ML (breast, lung, and colon cancer) are those with the highest incidence, being the USA and China the main scientific producers on the subject. Interestingly, the role of China and Japan in stomach cancer is correlated with the number of cases of this cancer type in Asia (78% of the worldwide cases). Knowing the countries and institutions that most study each area can be of great help for improving international collaborations between research groups and countries. Our analysis shows that medical and computer science journals lead the number of publications on the subject and could be useful for researchers in the field. Finally, keyword co-occurrence analysis suggests that ML-cancer research trends are focused not only on the use of ML as an effective diagnostic method, but also for the improvement of radiotherapy- and chemotherapy-based treatments.

Digital Pathology: Transforming Diagnosis in the Digital Age

In the context of rapid technological advancements, the narrative review titled "Digital Pathology: Transforming Diagnosis in the Digital Age" explores the significant impact of digital pathology in reshaping diagnostic approaches. This review delves into the various effects of the field, including remote consultations and artificial intelligence (AI)-assisted analysis, revealing the ongoing transformation taking place. The investigation explores the process of digitizing traditional glass slides, which aims to improve accessibility and facilitate sharing. Additionally, it addresses the complexities associated with data security and standardization challenges. Incorporating AI enhances pathologists' diagnostic capabilities and accelerates analytical procedures. Furthermore, the review highlights the growing importance of collaborative networks facilitating global knowledge sharing. It also emphasizes the significant impact of this technology on medical education and patient care. This narrative review aims to provide an overview of digital pathology's transformative and innovative potential, highlighting its disruptive nature in reshaping diagnostic practices.

Optimized detection and segmentation of nuclei in gastric cancer images using stain normalization and blurred artifact removal

Histological analysis with microscopy is the gold standard to diagnose and stage cancer, where slides or whole slide images are analyzed for cell morphological and spatial features by pathologists. The nuclei of cancerous cells are characterized by nonuniform chromatin distribution, irregular shapes, and varying size. As nucleus area and shape alone carry prognostic value, detection and segmentation of nuclei are among the most important steps in disease grading. However, evaluation of nuclei is a laborious, time-consuming, and subjective process with large variation among pathologists. Recent advances in digital pathology have allowed significant applications in nuclei detection, segmentation, and classification, but automated image analysis is greatly affected by staining factors, scanner variability, and imaging artifacts, requiring robust image preprocessing, normalization, and segmentation methods for clinically satisfactory results. In this paper, we aimed to evaluate and compare the digital image analysis techniques used in clinical pathology and research in the setting of gastric cancer. A literature review was conducted to evaluate potential methods of improving nuclei detection. Digitized images of 35 patients from a retrospective cohort of gastric adenocarcinoma at Oulu University Hospital in 1987-2016 were annotated for nuclei (n = 9085) by expert pathologists and 14 images of different cancer types from public TCGA dataset with annotated nuclei (n = 7000) were used as a comparison to evaluate applicability in other cancer types. The detection and segmentation accuracy with the selected color normalization and stain separation techniques were compared between the methods. The extracted information can be supplemented by patient's medical data and fed to the existing statistical clinical tools or subjected to subsequent AI-assisted classification and prediction models. The performance of each method is evaluated by several metrics against the annotations done by expert pathologists. The F1-measure of 0.854 ± 0.068 is achieved with color normalization for the gastric cancer dataset, and 0.907 ± 0.044 with color deconvolution for the public dataset, showing comparable results to the earlier state-of-the-art works. The developed techniques serve as a basis for further research on application and interpretability of AI-assisted tools for gastric cancer diagnosis.

Automated machine learning (AutoML) can predict 90-day mortality after gastrectomy for cancer

Early postoperative mortality risk prediction is crucial for clinical management of gastric cancer. This study aims to predict 90-day mortality in gastric cancer patients undergoing gastrectomy using automated machine learning (AutoML), optimize models for preoperative prediction, and identify factors influential in prediction. National Cancer Database was used to identify stage I-III gastric cancer patients undergoing gastrectomy between 2004 and 2016. 26 features were used to train predictive models using H2O.ai AutoML. Performance on validation cohort was measured. In 39,108 patients, 90-day mortality rate was 8.8%. The highest performing model was an ensemble (AUC = 0.77); older age, nodal ratio, and length of inpatient stay (LOS) following surgery were most influential for prediction. Removing the latter two parameters decreased model performance (AUC 0.71). For optimizing models for preoperative use, models were developed to first predict node ratio or LOS, and these predicted values were inputted for 90-day mortality prediction (AUC of 0.73-0.74). AutoML performed well in predicting 90-day mortality in a larger cohort of gastric cancer patients that underwent gastrectomy. These models can be implemented preoperatively to inform prognostication and patient selection for surgery. Our study supports broader evaluation and application of AutoML to guide surgical oncologic care.

Artificial Intelligence in Gastric Cancer Imaging With Emphasis on Diagnostic Imaging and Body Morphometry

Gastric cancer remains a significant global health concern, coercing the need for advancements in imaging techniques for ensuring accurate diagnosis and effective treatment planning. Artificial intelligence (AI) has emerged as a potent tool for gastric-cancer imaging, particularly for diagnostic imaging and body morphometry. This review article offers a comprehensive overview of the recent developments and applications of AI in gastric cancer imaging. We investigated the role of AI imaging in gastric cancer diagnosis and staging, showcasing its potential to enhance the accuracy and efficiency of these crucial aspects of patient management. Additionally, we explored the application of AI body morphometry specifically for assessing the clinical impact of gastrectomy. This aspect of AI utilization holds significant promise for understanding postoperative changes and optimizing patient outcomes. Furthermore, we examine the current state of AI techniques for the prognosis of patients with gastric cancer. These prognostic models leverage AI algorithms to predict long-term survival outcomes and assist clinicians in making informed treatment decisions. However, the implementation of AI techniques for gastric cancer imaging has several limitations. As AI continues to evolve, we hope to witness the translation of cutting-edge technologies into routine clinical practice, ultimately improving patient care and outcomes in the fight against gastric cancer.

The 'double‑track sign': A novel CT finding suggestive of the diagnosis of T1a gastric cancer

Effective identification of T1a stage cancer is crucial for planning endoscopic resection for early gastric cancers. The present study aimed to determine the diagnostic value of the double-track sign in patients with T1a gastric cancer using computed tomography (CT) imaging. A total of 152 patients diagnosed with pathologically proven T1a gastric cancer at The First Affiliated Hospital of Zhengzhou University (Zhengzhou, China) between July 2011 and August 2021 were retrospectively reviewed. The control group consisted of 2,926 patients with gastritis. Clinical data, including patient characteristics and preoperative CT imaging findings with gastric morphological features, were reviewed and analyzed. Out of 51 patients with T1a gastric cancer finally included, 31 (60.8%) exhibited local double-track enhancement changes of the stomach, referred to as the 'double-track sign', on CT images. In addition, four patients (7.8%) had well-enhanced mucosal thickening of the gastric wall. Of the 2,926 control subjects, none had any double-track sign and six patients (0.2%) had local gastric wall thickening with abnormally strengthened enhancement. In conclusion, a double-track sign on CT images is beneficial in the diagnostic differentiation of T1a gastric cancer.

Risk factor profiles for gastric cancer prediction with respect to Helicobacter pylori: A study of a tertiary care hospital in Pakistan

BACKGROUND

Gastric cancer (GC) is the fourth leading cause of cancer-related deaths worldwide. Diagnosis relies on histopathology and the number of endoscopies is increasing. Helicobacter pylori (H. pylori) infection is a major risk factor.

AIM

To develop an in-silico GC prediction model to reduce the number of diagnostic surgical procedures. The meta-data of patients with gastroduodenal symptoms, risk factors associated with GC, and H. pylori infection status from Holy Family Hospital Rawalpindi, Pakistan, were used with machine learning.

METHODS

A cohort of 341 patients was divided into three groups [normal gastric mucosa (NGM), gastroduodenal diseases (GDD), and GC]. Information associated with socioeconomic and demographic conditions and GC risk factors was collected using a questionnaire. H. pylori infection status was determined based on urea breath test. The association of these factors and histopathological grades was assessed statistically. K-Nearest Neighbors and Random Forest (RF) machine learning models were tested.

RESULTS

This study reported an overall frequency of 64.2% (219/341) of H. pylori infection among enrolled subjects. It was higher in GC (74.2%, 23/31) as compared to NGM and GDD and higher in males (54.3%, 119/219) as compared to females. More abdominal pain (72.4%, 247/341) was observed than other clinical symptoms including vomiting, bloating, acid reflux and heartburn. The majority of the GC patients experienced symptoms of vomiting (91%, 20/22) with abdominal pain (100%, 22/22). The multinomial logistic regression model was statistically significant and correctly classified 80% of the GDD/GC cases. Age, income level, vomiting, bloating and medication had significant association with GDD and GC. A dynamic RF GC-predictive model was developed, which achieved > 80% test accuracy.

CONCLUSION

GC risk factors were incorporated into a computer model to predict the likelihood of developing GC with high sensitivity and specificity. The model is dynamic and will be further improved and validated by including new data in future research studies. Its use may reduce unnecessary endoscopic procedures. It is freely available.

Identification of high-risk factors associated with mortality at 1-, 3-, and 5-year intervals in gastric cancer patients undergoing radical surgery and immunotherapy: an 8-year multicenter retrospective analysis

Background

Combining immunotherapy with surgical intervention is a prevailing and radical therapeutic strategy for individuals afflicted with gastric carcinoma; nonetheless, certain patients exhibit unfavorable prognoses even subsequent to this treatment regimen. This research endeavors to devise a machine learning algorithm to recognize risk factors with a high probability of inducing mortality among patients diagnosed with gastric cancer, both prior to and during their course of treatment.

Methods

Within the purview of this investigation, a cohort of 1015 individuals with gastric cancer were incorporated, and 39 variables encompassing diverse features were recorded. To construct the models, we employed three distinct machine learning algorithms, specifically extreme gradient boosting (XGBoost), random forest (RF), and k-nearest neighbor algorithm (KNN). The models were subjected to internal validation through employment of the k-fold cross-validation technique, and subsequently, an external dataset was utilized to externally validate the models.

Results

In comparison to other machine learning algorithms employed, the XGBoost algorithm demonstrated superior predictive capacity regarding the risk factors that affect mortality after combination therapy in gastric cancer patients for a duration of one year, three years, and five years posttreatment. The common risk factors that significantly impacted patient survival during the aforementioned time intervals were identified as advanced age, tumor invasion, tumor lymph node metastasis, tumor peripheral nerve invasion (PNI), multiple tumors, tumor size, carcinoembryonic antigen (CEA) level, carbohydrate antigen 125 (CA125) level, carbohydrate antigen 72-4 (CA72-4) level, and H. pylori infection.

Conclusion

The XGBoost algorithm can assist clinicians in identifying pivotal prognostic factors that are of clinical significance and can contribute toward individualized patient monitoring and management.

Recent advances in endoscopic management of gastric neoplasms

The development and clinical application of new diagnostic endoscopic technologies such as endoscopic ultrasonography with biopsy, magnification endoscopy, and narrow-band imaging, more recently supplemented by artificial intelligence, have enabled wider recognition and detection of various gastric neoplasms including early gastric cancer (EGC) and subepithelial tumors, such as gastrointestinal stromal tumors and neuroendocrine tumors. Over the last decade, the evolution of novel advanced therapeutic endoscopic techniques, such as endoscopic mucosal resection, endoscopic submucosal dissection, endoscopic full-thickness resection, and submucosal tunneling endoscopic resection, along with the advent of a broad array of endoscopic accessories, has provided a promising and yet less invasive strategy for treating gastric neoplasms with the advantage of a reduced need for gastric surgery. Thus, the management algorithms of various gastric tumors in a defined subset of the patient population at low risk of lymph node metastasis and amenable to endoscopic resection, may require revision considering upcoming data given the high success rate of en bloc resection by experienced endoscopists. Moreover, endoscopic surveillance protocols for precancerous gastric lesions will continue to be refined by systematic reviews and meta-analyses of further research. However, the lack of familiarity with subtle endoscopic changes associated with EGC, as well as longer procedural time, evolving resection techniques and tools, a steep learning curve of such high-risk procedures, and lack of coding are issues that do not appeal to many gastroenterologists in the field. This review summarizes recent advances in the endoscopic management of gastric neoplasms, with special emphasis on diagnostic and therapeutic methods and their future prospects.

Predicting metastasis in gastric cancer patients: machine learning-based approaches

Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. This study aims to develop predictive models using different machine learning (ML) classifiers based on both demographic and clinical variables to predict metastasis status of patients with GC. The data applied in this study including 733 of GC patients, divided into a train and test groups at a ratio of 8:2, diagnosed at Taleghani tertiary hospital. In order to predict metastasis in GC, ML-based algorithms, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, F1 score, precision, sensitivity, specificity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and precision-recall AUC (PR-AUC) were obtained. 262 (36%) experienced metastasis among 733 patients with GC. Although all models have optimal performance, the indices of SVM model seems to be more appropiate (training set: AUC: 0.94, Sensitivity: 0.94; testing set: AUC: 0.85, Sensitivity: 0.92). Then, NN has the higher AUC among ML approaches (training set: AUC: 0.98; testing set: AUC: 0.86). The RF of ML-based models, which determine size of tumor and age as two essential variables, is considered as the third efficient model, because of higher specificity and AUC (84% and 87%). Based on the demographic and clinical characteristics, ML approaches can predict the metastasis status in GC patients. According to AUC, sensitivity and specificity in both SVM and NN can be regarded as better algorithms among 6 applied ML-based methods.

A deep-learning based system using multi-modal data for diagnosing gastric neoplasms in real-time (with video)

BACKGROUND

White light (WL) and weak-magnifying (WM) endoscopy are both important methods for diagnosing gastric neoplasms. This study constructed a deep-learning system named ENDOANGEL-MM (multi-modal) aimed at real-time diagnosing gastric neoplasms using WL and WM data.

METHODS

WL and WM images of a same lesion were combined into image-pairs. A total of 4201 images, 7436 image-pairs, and 162 videos were used for model construction and validation. Models 1-5 including two single-modal models (WL, WM) and three multi-modal models (data fusion on task-level, feature-level, and input-level) were constructed. The models were tested on three levels including images, videos, and prospective patients. The best model was selected for constructing ENDOANGEL-MM. We compared the performance between the models and endoscopists and conducted a diagnostic study to explore the ENDOANGEL-MM's assistance ability.

RESULTS

Model 4 (ENDOANGEL-MM) showed the best performance among five models. Model 2 performed better in single-modal models. The accuracy of ENDOANGEL-MM was higher than that of Model 2 in still images, real-time videos, and prospective patients. (86.54 vs 78.85%, P = 0.134; 90.00 vs 85.00%, P = 0.179; 93.55 vs 70.97%, P < 0.001). Model 2 and ENDOANGEL-MM outperformed endoscopists on WM data (85.00 vs 71.67%, P = 0.002) and multi-modal data (90.00 vs 76.17%, P = 0.002), significantly. With the assistance of ENDOANGEL-MM, the accuracy of non-experts improved significantly (85.75 vs 70.75%, P = 0.020), and performed no significant difference from experts (85.75 vs 89.00%, P = 0.159).

CONCLUSIONS

The multi-modal model constructed by feature-level fusion showed the best performance. ENDOANGEL-MM identified gastric neoplasms with good accuracy and has a potential role in real-clinic.

Butyrate prevents the migration and invasion, and aerobic glycolysis in gastric cancer via inhibiting Wnt/β-catenin/c-Myc signaling

Gastric cancer (GC) remains a common cause of cancer death worldwide. Evidence has found that butyrate exhibited antitumor effects on GC cells. However, the mechanism by which butyrate regulate GC cell proliferation, migration, invasion, and aerobic glycolysis remains largely unknown. The proliferation, migration, and invasion of GC cells were tested by EdU staining, transwell assays. Additionally, protein expressions were determined by western blot assay. Next, glucose uptake, lactate production, and cellular ATP levels in GC cells were detected. Furthermore, the antitumor effects of butyrate in tumor-bearing nude mice were evaluated. We found, butyrate significantly prevented GC cell proliferation, migration, and invasion (p < .01). Additionally, butyrate markedly inhibited GC cell aerobic glycolysis, as shown by the reduced expressions of GLUT1, HK2, and LDHA (p < .01). Moreover, butyrate notably decreased nuclear β-catenin and c-Myc levels in GC cells (p < .01). Remarkably, through activating Wnt/β-catenin signaling with LiCl, the inhibitory effects of butyrate on the growth and aerobic glycolysis of GC cells were diminished (p < .01). Moreover, butyrate notably suppressed tumor volume and weight in GC cell xenograft nude mice in vivo (p < .01). Meanwhile, butyrate obviously reduced nuclear β-catenin, c-Myc, GLUT1, HK2 and LDHA levels in tumor tissues in GC cell xenograft mice (p < .01). Collectively, butyrate could suppress the growth and aerobic glycolysis of GC cells in vitro and in vivo via downregulating wnt/β-catenin/c-Myc signaling. These findings are likely to prove useful in better understanding the role of butyrate in GC.

Prognostic artificial intelligence model to predict 5 year survival at 1 year after gastric cancer surgery based on nutrition and body morphometry

BACKGROUND

Personalized survival prediction is important in gastric cancer patients after gastrectomy based on large datasets with many variables including time-varying factors in nutrition and body morphometry. One year after gastrectomy might be the optimal timing to predict long-term survival because most patients experience significant nutritional change, muscle loss, and postoperative changes in the first year after gastrectomy. We aimed to develop a personalized prognostic artificial intelligence (AI) model to predict 5 year survival at 1 year after gastrectomy.

METHODS

From a prospectively built gastric surgery registry from a tertiary hospital, 4025 gastric cancer patients (mean age 56.1 ± 10.9, 36.2% females) treated gastrectomy and survived more than a year were selected. Eighty-nine variables including clinical and derived time-varying variables were used as input variables. We proposed a multi-tree extreme gradient boosting (XGBoost) algorithm, an ensemble AI algorithm based on 100 datasets derived from repeated five-fold cross-validation. Internal validation was performed in split datasets (n = 1121) by comparing our proposed model and six other AI algorithms. External validation was performed in 590 patients from other hospitals (mean age 55.9 ± 11.2, 37.3% females). We performed a sensitivity analysis to analyse the effect of the nutritional and fat/muscle indices using a leave-one-out method.

RESULTS

In the internal validation, our proposed model showed AUROC of 0.8237, which outperformed the other AI algorithms (0.7988-0.8165), 80.00% sensitivity, 72.34% specificity, and 76.17% balanced accuracy. In the external validation, our model showed AUROC of 0.8903, 86.96% sensitivity, 74.60% specificity, and 80.78% balanced accuracy. Sensitivity analysis demonstrated that the nutritional and fat/muscle indices influenced the balanced accuracy by 0.31% and 6.29% in the internal and external validation set, respectively. Our developed AI model was published on a website for personalized survival prediction.

CONCLUSIONS

Our proposed AI model provides substantially good performance in predicting 5 year survival at 1 year after gastric cancer surgery. The nutritional and fat/muscle indices contributed to increase the prediction performance of our AI model.

Establishing machine learning models to predict the early risk of gastric cancer based on lifestyle factors

BACKGROUND

Gastric cancer is one of the leading causes of death worldwide. Screening for gastric cancer greatly relies on endoscopy and pathology biopsy, which are invasive and pose financial burdens. Thus, the prevention of the disease by modifying lifestyle-related behaviors and dietary habits or even the prevention of risk factor formation is of great importance. This study aimed to construct an inexpensive, non-invasive, fast, and high-precision diagnostic model using six machine learning (ML) algorithms to classify patients at high or low risk of developing gastric cancer by analyzing individual lifestyle factors.

METHODS

This retrospective study used the data of 2029 individuals from the gastric cancer database of Ayatollah Taleghani Hospital in Abadan City, Iran. The data were randomly separated into training and test sets (ratio 0.7:0.3). Six  ML methods, including multilayer perceptron (MLP), support vector machine (SVM) (linear kernel), SVM (RBF kernel), k-nearest neighbors (KNN) (K = 1, 3, 7, 9), random forest (RF), and eXtreme Gradient Boosting (XGBoost), were trained to construct prognostic models before and after performing the relief feature selection method. Finally, to evaluate the models' performance, the metrics derived from the confusion matrix were calculated via a test split and cross-validation.

RESULTS

This study found 11 important influence factors for the risk of gastric cancer, such as Helicobacter pylori infection, high salt intake, and chronic atrophic gastritis, among other factors. Comparisons indicated that the XGBoost had the best performance for the risk prediction of gastric cancer.

CONCLUSIONS

The results suggest that based on simple baseline patient data, the ML techniques have the potential to start the prescreening of gastric cancer and identify high-risk individuals who should proceed with invasive examinations. Our model could also considerably lessen the number of cases that need endoscopic surveillance. Future studies are required to validate the efficacy of the models in a larger and multicenter population.

Digital pathology implementation in a private laboratory: The CEDAP experience

Introduction

The transition to digital pathology has been carried out by several laboratories across the globe, with some cases described in Portugal. In this article, we describe the transition to digital pathology in a high-volume private laboratory, considering the main challenges and opportunities.

Material and methods

Our process started in 2020, with laboratory workflow adaptation and we are currently using a high-capacity scanner (Aperio GT450DX) to digitize slides at 20×. The visualization system, Aperio eSlide Manager WebViewer, is integrated into the Laboratory System. The validation process followed the Royal College of Pathologists Guidelines.

Results

Regarding validation, the first phase detected an error rate of 6.8%, mostly due to digitization errors. Phase optimization and collaboration with technical services led to improvements in this process. In the second validation phase, most of the slides had the desired quality for evaluation, with only an error rate of 0.6%, corrected with a new scan. The interpathologist correlation had a total agreement rate of 96.87% and 3.13% partial agreement.

Conclusion

The implementation and validation of digital pathology was a success, being ready for prime time. The total integration of all laboratory systems and the acquisition of new equipment will maximize their use, especially with the application of artificial intelligence algorithms.

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.

Systematic review and meta-analysis: Artificial intelligence for the diagnosis of gastric precancerous lesions and Helicobacter pylori infection

BACKGROUND

The endoscopic diagnosis of Helicobacter-pylori(H.pylori) infection and gastric precancerous lesions(GPL), namely atrophic-gastritis and intestinal-metaplasia, still remains challenging. Artificial intelligence(AI) may represent a powerful resource for the endoscopic recognition of these conditions.

AIMS

To explore the diagnostic performance(DP) of AI in the diagnosis of GPL and H.pylori infection.

METHODS

A systematic-review was performed by two independent authors up to September 2021. Inclusion criteria were studies focusing on the DP of AI-system in the diagnosis of GPL and H.pylori infection. The pooled accuracy of studies included was reported.

RESULTS

Overall, 128 studies were found (PubMed-Embase-Cochrane Library) and four and nine studies were finally included regarding GPL and H.pylori infection, respectively. The pooled-accuracy(random effects model) was 90.3%(95%CI 84.3-94.9) and 79.6%(95%CI 66.7-90.0) with a significant heterogeneity[I²=90.4%(95%CI 78.5-95.7);I²=97.9%(97.2-98.6)] for GPL and H.pylori infection, respectively. The Begg's-test showed a significant publication-bias(p = 0.0371) only among studies regarding H.pylori infection. The pooled-accuracy(random-effects-model) was similar considering only studies using CNN-model for the diagnosis of H.pylori infection: 74.1%[(95%CI 51.6-91.3);I²=98.9%(95%CI 98.5-99.3)], Begg's-test(p = 0.1416) did not show publication-bias.

CONCLUSION

AI-system seems to be a good resource for an easier diagnosis of GPL and H.pylori infection, showing a pooled-diagnostic-accuracy of 90% and 80%, respectively. However, considering the high heterogeneity, these promising data need an external validation by randomized control trials and prospective real-time studies.

Machine Learning for Risk Prediction of Oesophago-Gastric Cancer in Primary Care: Comparison with Existing Risk-Assessment Tools

Simple Summary

Oesophago-gastric cancer is one of the commonest cancers worldwide, yet it can be particularly difficult to diagnose given that initial symptoms are often non-specific and routine screening is not available. Cancer risk-assessment tools, which calculate cancer risk based on symptoms and other risk factors present in the primary care record, can aid decisions on referrals for cancer investigations, facilitating earlier diagnosis. Diagnosing common cancers earlier could help improve survival rates. Using UK primary care electronic health record data, we compared five different machine learning techniques for probabilistic classification of cancer patients against a current widely used UK primary care cancer risk-assessment tool. The machine learning algorithms outperformed the current risk-assessment tool, with a higher overall accuracy and an ability to reasonably identify 11–25% more cancer patients. We conclude that machine-learning-based risk-assessment tools could help better identify suitable patients for further investigation and support earlier diagnosis.

Abstract

Oesophago-gastric cancer is difficult to diagnose in the early stages given its typical non-specific initial manifestation. We hypothesise that machine learning can improve upon the diagnostic performance of current primary care risk-assessment tools by using advanced analytical techniques to exploit the wealth of evidence available in the electronic health record. We used a primary care electronic health record dataset derived from the UK General Practice Research Database (7471 cases; 32,877 controls) and developed five probabilistic machine learning classifiers: Support Vector Machine, Random Forest, Logistic Regression, Naïve Bayes, and Extreme Gradient Boosted Decision Trees. Features included basic demographics, symptoms, and lab test results. The Logistic Regression, Support Vector Machine, and Extreme Gradient Boosted Decision Tree models achieved the highest performance in terms of accuracy and AUROC (0.89 accuracy, 0.87 AUROC), outperforming a current UK oesophago-gastric cancer risk-assessment tool (ogRAT). Machine learning also identified more cancer patients than the ogRAT: 11.0% more with little to no effect on false positives, or up to 25.0% more with a slight increase in false positives (for Logistic Regression, results threshold-dependent). Feature contribution estimates and individual prediction explanations indicated clinical relevance. We conclude that machine learning could improve primary care cancer risk-assessment tools, potentially helping clinicians to identify additional cancer cases earlier. This could, in turn, improve survival outcomes.

Jolkinolide B induces cell cycle arrest and apoptosis in MKN45 gastric cancer cells and inhibits xenograft tumor growth in vivo

Gastric cancer is one of the most common digestive carcinomas throughout the world and represents high mortality. There is an urgent quest for seeking a novel and efficient antigastric cancer drug. Euphorbia fischeriana Steud had long been used as a traditional Chinese medicine for the treatment of cancer. According to the basic theory of traditional Chinese medicine, its antitumor mechanism is ‘to combat poison with poison’. However, its effective material foundation of it is still ambiguous. In our previous work, we studied the chemical constituents of E. fischeriana Steud. Jolkinolide B (JB) is an ent-abietane-type diterpenoid we isolated from it. The purpose of the present study was to investigate the antigastric effect and mechanism of JB. Results revealed that JB could suppress the proliferation of MKN45 cells in vitro and inhibit MKN45 xenograft tumor growth in nude mice in vivo. We further investigated its anticancer mechanism. On the one hand, JB caused DNA damage in gastric cancer MKN45 cells and induced the S cycle arrest by activating the ATR-CHK1-CDC25A-Cdk2 signaling pathway, On the other hand, JB induced MKN45 cells apoptosis through the mitochondrial pathway, and ultimately effectively inhibited the growth of gastric cancer cells. These results suggest that JB appears to be a promising candidate drug with antigastric cancer activity and warrants further research.

Artificial Intelligence Assisted Topographic Mapping System for Endoscopic Submucosal Dissection Specimens

Background

Endoscopic submucosal dissection (ESD), a minimally invasive surgery used to treat early gastrointestinal malignancies, has been widely embraced around the world. The gross reconstruction of ESD specimens can facilitate a more precise pathological diagnosis and allow endoscopists to explore lesions thoroughly. The traditional method of mapping is time-consuming and inaccurate. We aim to design a topographic mapping system via artificial intelligence to perform the job automatically.

Methods

The topographic mapping system was built using computer vision techniques. We enrolled 23 ESD cases at the Peking Union Medical College Hospital from September to November 2019. The reconstruction maps were created for each case using both the traditional approach and the system.

Results

Using the system, the time saved per case ranges from 34 to 3,336 s. Two approaches revealed no significant variations in the shape, size, or tumor area.

Conclusion

We developed an AI-assisted system that would help pathologists complete the ESD topographic mapping process rapidly and accurately.

Scoping out the future: The application of artificial intelligence to gastrointestinal endoscopy

Artificial intelligence (AI) is a quickly expanding field in gastrointestinal endoscopy. Although there are a myriad of applications of AI ranging from identification of bleeding to predicting outcomes in patients with inflammatory bowel disease, a great deal of research has focused on the identification and classification of gastrointestinal malignancies. Several of the initial randomized, prospective trials utilizing AI in clinical medicine have centered on polyp detection during screening colonoscopy. In addition to work focused on colorectal cancer, AI systems have also been applied to gastric, esophageal, pancreatic, and liver cancers. Despite promising results in initial studies, the generalizability of most of these AI systems have not yet been evaluated. In this article we review recent developments in the field of AI applied to gastrointestinal oncology.

Evaluation of CSTB and DMBT1 expression in saliva of gastric cancer patients and controls

Background

Gastric cancer (GC) is the fifth most common cancer and the third cause of cancer deaths globally, with late diagnosis, low survival rate, and poor prognosis. This case-control study aimed to evaluate the expression of cystatin B (CSTB) and deleted in malignant brain tumor 1 (DMBT1) in the saliva of GC patients with healthy individuals to construct diagnostic algorithms using statistical analysis and machine learning methods.

Methods

Demographic data, clinical characteristics, and food intake habits of the case and control group were gathered through a standard checklist. Unstimulated whole saliva samples were taken from 31 healthy individuals and 31 GC patients. Through ELISA test and statistical analysis, the expression of salivary CSTB and DMBT1 proteins was evaluated. To construct diagnostic algorithms, we used the machine learning method.

Results

The mean salivary expression of CSTB in GC patients was significantly lower (115.55 ± 7.06, p = 0.001), and the mean salivary expression of DMBT1 in GC patients was significantly higher (171.88 ± 39.67, p = 0.002) than the control.

Multiple linear regression analysis demonstrated that GC was significantly correlated with high levels of DMBT1 after controlling the effects of age of participants (R² = 0.20, p < 0.001).

Considering salivary CSTB greater than 119.06 ng/mL as an optimal cut-off value, the sensitivity and specificity of CSTB in the diagnosis of GC were 83.87 and 70.97%, respectively. The area under the ROC curve was calculated as 0.728. The optimal cut-off value of DMBT1 for differentiating GC patients from controls was greater than 146.33 ng/mL (sensitivity = 80.65% and specificity = 64.52%). The area under the ROC curve was up to 0.741.

As a result of the machine learning method, the area under the receiver-operating characteristic curve for the diagnostic ability of CSTB, DMBT1, demographic data, clinical characteristics, and food intake habits was 0.95. The machine learning model’s sensitivity, specificity, and accuracy were 100, 70.8, and 80.5%, respectively.

Conclusion

Salivary levels of DMBT1 and CSTB may be accurate in diagnosing GCs. Machine learning analyses using salivary biomarkers, demographic, clinical, and nutrition habits data simultaneously could provide affordability models with acceptable accuracy for differentiation of GC by a cost-effective and non-invasive method.

Artificial Intelligence Mortality Prediction Model for Gastric Cancer Surgery Based on Body Morphometry, Nutritional, and Surgical Information: Feasibility Study

The objective of this study is to develop a mortality prediction model for patients undergoing gastric cancer surgery based on body morphometry, nutritional, and surgical information. Using a prospectively built gastric surgery registry from the Asan Medical Center (AMC), 621 gastric cancer patients, who were treated with surgery with no recurrence of cancer, were selected for the development of the prediction model. Input features (i.e., body morphometry, nutritional, surgical, and clinicopathologic information) were selected in the collected data based on the XGBoost analysis results and experts’ opinions. A convolutional neural network (CNN) framework was developed to predict the mortality of patients undergoing gastric cancer surgery. Internal validation was performed in split datasets of the AMC, whereas external validation was performed in patients in the Ajou University Hospital. Fifteen features were selected for the prediction of survival probability based on the XGBoost analysis results and experts’ suggestions. Accuracy, F1 score, and area under the curve of our CNN model were 0.900, 0.909, and 0.900 in the internal validation set and 0.879, 0.882, and 0.881 in the external validation set, respectively. Our developed CNN model was published on a website where anyone could predict mortality using individual patients’ data. Our CNN model provides substantially good performance in predicting mortality in patients undergoing surgery for gastric cancer, mainly based on body morphometry, nutritional, and surgical information. Using the web application, clinicians and gastric cancer patients will be able to efficiently manage mortality risk factors.

Weakly supervised end-to-end artificial intelligence in gastrointestinal endoscopy

Artificial intelligence (AI) is widely used to analyze gastrointestinal (GI) endoscopy image data. AI has led to several clinically approved algorithms for polyp detection, but application of AI beyond this specific task is limited by the high cost of manual annotations. Here, we show that a weakly supervised AI can be trained on data from a clinical routine database to learn visual patterns of GI diseases without any manual labeling or annotation. We trained a deep neural network on a dataset of N = 29,506 gastroscopy and N = 18,942 colonoscopy examinations from a large endoscopy unit serving patients in Germany, the Netherlands and Belgium, using only routine diagnosis data for the 42 most common diseases. Despite a high data heterogeneity, the AI system reached a high performance for diagnosis of multiple diseases, including inflammatory, degenerative, infectious and neoplastic diseases. Specifically, a cross-validated area under the receiver operating curve (AUROC) of above 0.70 was reached for 13 diseases, and an AUROC of above 0.80 was reached for two diseases in the primary data set. In an external validation set including six disease categories, the AI system was able to significantly predict the presence of diverticulosis, candidiasis, colon and rectal cancer with AUROCs above 0.76. Reverse engineering the predictions demonstrated that plausible patterns were learned on the level of images and within images and potential confounders were identified. In summary, our study demonstrates the potential of weakly supervised AI to generate high-performing classifiers and identify clinically relevant visual patterns based on non-annotated routine image data in GI endoscopy and potentially other clinical imaging modalities.

Development of web-based dynamic nomogram to predict survival in patients with gastric cancer: a population-based study

Gastric cancer (GC) is the fifth most frequent malignancy worldwide and the third leading cause of cancer-associated mortality. The study’s goal was to construct a predictive model and nomograms to predict the survival of GC patients. This historical cohort study assessed 733 patients who underwent treatments for GC. The univariate and multivariable Cox proportional hazard (CPH) survival analyses were applied to identify the factors related to overall survival (OS). A dynamic nomogram was developed as a graphical representation of the CPH regression model. The internal validation of the nomogram was evaluated by Harrell’s concordance index (C-index) and time-dependent AUC. The results of the multivariable Cox model revealed that the age of patients, body mass index (BMI), grade of tumor, and depth of tumor elevate the mortality hazard of gastric cancer patients (P < 0.05). The built nomogram had a discriminatory performance, with a C-index of 0.64 (CI 0.61, 0.67). We constructed and validated an original predictive nomogram for OS in patients with GC. Furthermore, nomograms may help predict the individual risk of OS in patients treated for GC.

Flotillin-1 promotes progression and dampens chemosensitivity to cisplatin in gastric cancer via ERK and AKT signaling pathways

BACKGROUND

Several past studies have reported the overexpression of Flotillin-1 in a variety of cancer types. Cisplatin is a chemotherapeutic drug commonly used for cancer treatment. The present study investigated the role of Flotillin-1 in the progression of GC and assessed whether it assists in the chemical sensitization of GC cells toward cisplatin.

METHOD

The expression of Flotillin-1 was detected both in human gastric mucosal cells and GC cells. Next, siRNA and shRNA were used to construct a stable cell line expressing low levels of Flotillin-1. Furthermore, the Cell Counting Kit 8 (CCK-8), flow cytometry, and transwell assays were employed to detect the impact of Flotillin-1 on GC cells. In addition, a nude mouse model of human GC was used to verify the knockdown of Flotillin-1 to increase the sensitivity of GC cells to cisplatin.

RESULTS

Flotillin-1 was overexpressed in GC cells when compared to that in human gastric mucosal cells. The results for in vitro and vivo assays revealed that the knockdown of Flotillin-1 could significantly inhibit the proliferation of GC cells and increased the sensitivity of GC cells to cisplatin via the regulation of the protein kinase B (AKT) and extracellular signal-regulated kinase (ERK) signaling pathway.

CONCLUSION

Flotillin-1 might be used as a molecular marker for GC diagnosis and could be explored as a potential new target for the treatment of GC.

Significance of Lymph Node Metastasis in the Treatment of Gastric Cancer and Current Challenges in Determining the Extent of Metastasis

The stomach exhibits abundant lymphatic flow, and metastasis to lymph nodes is common. In the case of gastric cancer, there is a regularity to the spread of lymph node metastasis, and it does not easily metastasize outside the regional nodes. Furthermore, when its extent is limited, nodal metastasis of gastric cancer can be cured by appropriate lymph node dissection. Therefore, identifying and determining the extent of lymph node metastasis is important for ensuring accurate diagnosis and appropriate surgical treatment in patients with gastric cancer. However, precise detection of lymph node metastasis remains difficult. Most nodal metastases in gastric cancer are microscopic metastases, which often occur in small-sized lymph nodes, and are thus difficult to diagnose both preoperatively and intraoperatively. Preoperative nodal diagnoses are mainly made using computed tomography, although the specificity of this method is low because it is mainly based on the size of the lymph node. Furthermore, peripheral nodal metastases cannot be palpated intraoperatively, nodal harvesting of resected specimens remains difficult, and the number of lymph nodes detected vary greatly depending on the skill of the technician. Based on these findings, gastrectomy with prophylactic lymph node dissection is considered the standard surgical procedure for gastric cancer. In contrast, several groups have examined the value of sentinel node biopsy for accurately evaluating nodal metastasis in patients with early gastric cancer, reporting high sensitivity and accuracy. Sentinel node biopsy is also important for individualizing and optimizing the extent of uniform prophylactic lymph node dissection and determining whether patients are indicated for function-preserving curative gastrectomy, which is superior in preventing post-gastrectomy symptoms and maintaining dietary habits. Notably, advancements in surgical treatment for early gastric cancer are expected to result in individualized surgical strategies with sentinel node biopsy. Chemotherapy for advanced gastric cancer has also progressed, and conversion gastrectomy can now be performed after downstaging, even in cases previously regarded as inoperable. In this review, we discuss the importance of determining lymph node metastasis in the treatment of gastric cancer, the associated difficulties, and the need to investigate strategies that can improve the diagnosis of lymph node metastasis.

Machine learning: A non-invasive prediction method for gastric cancer based on a survey of lifestyle behaviors

Gastric cancer remains an enormous threat to human health. It is extremely significant to make a clear diagnosis and timely treatment of gastrointestinal tumors. The traditional diagnosis method (endoscope, surgery, and pathological tissue extraction) of gastric cancer is usually invasive, expensive, and time-consuming. The machine learning method is fast and low-cost, which breaks through the limitations of the traditional methods as we can apply the machine learning method to diagnose gastric cancer. This work aims to construct a cheap, non-invasive, rapid, and high-precision gastric cancer diagnostic model using personal behavioral lifestyles and non-invasive characteristics. A retrospective study was implemented on 3,630 participants. The developed models (extreme gradient boosting, decision tree, random forest, and logistic regression) were evaluated by cross-validation and the generalization ability in our test set. We found that the model developed using fingerprints based on the extreme gradient boosting (XGBoost) algorithm produced better results compared with the other models. The overall accuracy of which test set was 85.7%, AUC was 89.6%, sensitivity 78.7%, specificity 76.9%, and positive predictive values 73.8%, verifying that the proposed model has significant medical value and good application prospects.

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.

Application of artificial intelligence-assisted endoscopic detection of early esophageal cancer

In recent years, artificial intelligence (AI) combined with endoscopy has made an appearance in the diagnosis of early esophageal cancer (EC) and achieved satisfactory results. Due to the rapid progression and poor prognosis of EC, the early detection and diagnosis of EC are of great value for patient prognosis improvement. AI has been applied in the screening of early EC and has shown advantages; notably, it is more accurate than less-experienced endoscopists. In China, the detection of early EC depends on endoscopist expertise and is inevitably subject to interobserver variability. The excellent imaging recognition ability of AI is very suitable for the diagnosis and recognition of EC, thereby reducing the missed diagnosis and helping physicians to perform endoscopy better. This paper reviews the application and relevant progress of AI in the field of endoscopic detection of early EC (including squamous cell carcinoma and adenocarcinoma), with a focus on diagnostic performance of AI to identify different types of endoscopic images, such as sensitivity and specificity.

A Review of Applications of Artificial Intelligence in Gastroenterology

Artificial intelligence (AI) is the science that deals with creating 'intelligent machines'. AI has revolutionized medicine because of its application in several fields across medicine like radiology, neurology, ophthalmology, orthopedics and gastroenterology. In this review, we intend to summarize the basics of AI, the application of AI in various gastrointestinal pathologies till date as well as challenges/ problems related to the application of AI in medicine. Literature search using keywords like artificial intelligence, gastroenterology, applications, etc. were used. The literature search was done using Google Scholar, PubMed and ScienceDirect. All the relevant articles were gathered and relevant data were extracted from them. We concluded AI has achieved major feats in the past few decades. It has helped clinicians in diagnosing complex diseases, managing treatments as well as in predicting outcomes, all in all, which helps doctors from all over the globe in dispensing better healthcare services.

Recent Advances in the Diagnosis, Staging, Treatment, and Prognosis of Advanced Gastric Cancer: A Literature Review

Gastric cancer is one of the most common cause of cancer related deaths worldwide which results in malignant tumors in the digestive tract. The only radical treatment option available is surgical resection. Recently, the implementation of neoadjuvant chemotherapy resulted in 5-year survival rates of 95% for early gastric cancer. The main reason of treatment failure is that early diagnosis is minimal, with many patients presenting advanced stages. Hence, the greatest benefit of radical resection is missed. Consequently, the main therapeutic approach for advanced gastric cancer is combined surgery with neoadjuvant chemotherapy, targeted therapy, or immunotherapy. In this review, we will discuss the various treatment options for advanced gastric cancer. Clinical practice and clinical research is the most practical way of reaching new advents in terms of patients' characteristics, optimum drug choice, and better prognosis. With the recent advances in gastric cancer diagnosis, staging, treatment, and prognosis, we are evident that the improvement of survival in this patient population is just a matter of time.

Diagnostic Performance of Artificial Intelligence-Centred Systems in the Diagnosis and Postoperative Surveillance of Upper Gastrointestinal Malignancies Using Computed Tomography Imaging: A Systematic Review and Meta-Analysis of Diagnostic Accuracy

Background

Upper gastrointestinal cancers are aggressive malignancies with poor prognosis, even following multimodality therapy. As such, they require timely and accurate diagnostic and surveillance strategies; however, such radiological workflows necessitate considerable expertise and resource to maintain. In order to lessen the workload upon already stretched health systems, there has been increasing focus on the development and use of artificial intelligence (AI)-centred diagnostic systems. This systematic review summarizes the clinical applicability and diagnostic performance of AI-centred systems in the diagnosis and surveillance of esophagogastric cancers.

Methods

A systematic review was performed using the MEDLINE, EMBASE, Cochrane Review, and Scopus databases. Articles on the use of AI and radiomics for the diagnosis and surveillance of patients with esophageal cancer were evaluated, and quality assessment of studies was performed using the QUADAS-2 tool. A meta-analysis was performed to assess the diagnostic accuracy of sequencing methodologies.

Results

Thirty-six studies that described the use of AI were included in the qualitative synthesis and six studies involving 1352 patients were included in the quantitative analysis. Of these six studies, four studies assessed the utility of AI in gastric cancer diagnosis, one study assessed its utility for diagnosing esophageal cancer, and one study assessed its utility for surveillance. The pooled sensitivity and specificity were 73.4% (64.6–80.7) and 89.7% (82.7–94.1), respectively.

Conclusions

AI systems have shown promise in diagnosing and monitoring esophageal and gastric cancer, particularly when combined with existing diagnostic methods. Further work is needed to further develop systems of greater accuracy and greater consideration of the clinical workflows that they aim to integrate within.

Artificial intelligence for cancer detection in upper gastrointestinal endoscopy, current status, and future aspirations

This minireview discusses the benefits and pitfalls of machine learning, and artificial intelligence in upper gastrointestinal endoscopy for the detection and characterization of neoplasms. We have reviewed the literature for relevant publications on the topic using PubMed, IEEE, Science Direct, and Google Scholar databases. We discussed the phases of machine learning and the importance of advanced imaging techniques in upper gastrointestinal endoscopy and its association with artificial intelligence.

The application of CA72-4 in the diagnosis, prognosis, and treatment of gastric cancer

The role of conventional serum tumor marker, carbohydrate antigen 72-4 (CA72-4), in assisting diagnosis, monitoring dynamic progression, and evaluating the prognosis of gastric cancer (GC) should not be ignored, especially in the Chinese population. Even though CA72-4 has been used in clinical practice for decades, its modest positivity rate, sensitivity, and specificity did not meet the high demand of the clinical application. However, over the years, some progress in the functions of CA72-4 has been achieved, suggesting that CA72-4 can still be considered a promising marker in oncology. As a biomarker, CA72-4 can achieve improved sensitivity (SEN) and specificity (SPE) when combined with other biomarkers, selecting suitable reference values, improving detection techniques, and identifying the risk threshold. As a predictor, elevated serum CA72-4 levels were found to be significantly associated with prognostic risk factors, further assessing therapeutic validity and resectability. Recently, an effective method to reduce the toxicity of CA72-4 targeted therapy has been developed. Moreover, CA72-4 could induce novel aptamers to react with tumor cells and enhance the efficacy of trastuzumab in HER2-positive GC. Therefore, in this review, we discuss the most recent application of CA72-4 in the diagnosis, prognosis, and treatment of GC.

Progress on the study of the anticancer effects of artesunate

Artesunate (ART) is a derivative of artemisinin that is extracted from the wormwood plant Artemisia annua. ART is an antimalarial drug that has been shown to be safe and effective for clinical use. In addition to its antimalarial properties, ART has been attracting attention over recent years due to its reported inhibitory effects on cancer cell proliferation, invasion and migration. Therefore, ART has a wider range of potential clinical applications than first hypothesized. The aim of the present review was to summarize the latest research progress on the possible anticancer effects of ART, in order to lay a theoretical foundation for the further development of ART as a therapeutic option for cancer.

Artificial intelligence in small intestinal diseases: Application and prospects

The small intestine is located in the middle of the gastrointestinal tract, so small intestinal diseases are more difficult to diagnose than other gastrointestinal diseases. However, with the extensive application of artificial intelligence in the field of small intestinal diseases, with its efficient learning capacities and computational power, artificial intelligence plays an important role in the auxiliary diagnosis and prognosis prediction based on the capsule endoscopy and other examination methods, which improves the accuracy of diagnosis and prediction and reduces the workload of doctors. In this review, a comprehensive retrieval was performed on articles published up to October 2020 from PubMed and other databases. Thereby the application status of artificial intelligence in small intestinal diseases was systematically introduced, and the challenges and prospects in this field were also analyzed.

A Deep Recurrent Neural Network-Based Explainable Prediction Model for Progression from Atrophic Gastritis to Gastric Cancer

Gastric cancer is the fifth most common cancer type worldwide and one of the most frequently diagnosed cancers in South Korea. In this study, we propose DeepPrevention, which comprises a prediction module to predict the possibility of progression from atrophic gastritis to gastric cancer and an explanation module to identify risk factors for progression from atrophic gastritis to gastric cancer, to identify patients with atrophic gastritis who are at high risk of gastric cancer. The data set used in this study was South Korea National Health Insurance Service (NHIS) medical checkup data for atrophic gastritis patients from 2002 to 2013. Our experimental results showed that the most influential predictors of gastric cancer development were sex, smoking duration, and current smoking status. In addition, we found that the average age of gastric cancer diagnosis in a group of high-risk patients was 57, and income, BMI, regular exercise, and the number of endoscopic screenings did not show any significant difference between groups. At the individual level, we identified that there were relatively strong associations between gastric cancer and smoking duration and smoking status.

Requirements for implementation of artificial intelligence in the practice of gastrointestinal pathology

Tremendous advances in artificial intelligence (AI) in medical image analysis have been achieved in recent years. The integration of AI is expected to cause a revolution in various areas of medicine, including gastrointestinal (GI) pathology. Currently, deep learning algorithms have shown promising benefits in areas of diagnostic histopathology, such as tumor identification, classification, prognosis prediction, and biomarker/genetic alteration prediction. While AI cannot substitute pathologists, carefully constructed AI applications may increase workforce productivity and diagnostic accuracy in pathology practice. Regardless of these promising advances, unlike the areas of radiology or cardiology imaging, no histopathology-based AI application has been approved by a regulatory authority or for public reimbursement. Thus, implying that there are still some obstacles to be overcome before AI applications can be safely and effectively implemented in real-life pathology practice. The challenges have been identified at different stages of the development process, such as needs identification, data curation, model development, validation, regulation, modification of daily workflow, and cost-effectiveness balance. The aim of this review is to present challenges in the process of AI development, validation, and regulation that should be overcome for its implementation in real-life GI pathology practice.