Development and evaluation of a double-check support system using artificial intelligence in endoscopic screening for gastric cancer

Biological Clock Genes are Crucial and Promising Biomarkers for the Therapeutic Targets and Prognostic Assessment in Gastric Cancer

BACKGROUND

Gastric cancer is one of the major public health problems worldwide. Circadian rhythm disturbances driven by circadian clock genes play a role in the development of cancer. However, whether circadian clock genes can serve as potential therapeutic targets and prognostic biomarkers for gastric cancer remains elusive.

METHODS

In this study, we comprehensively analyzed the potential relationship between circadian clock genes and gastric cancer using online bioinformatics databases such as GEPIA, cBioPortal, STRING, GeneMANIA, Metascape, TIMER, TRRUST, and GEDS.

RESULTS

Biological clock genes are expressed differently in human tumors. Compared with normal tissues, only PER1, CLOCK, and TIMELESS expression differences were statistically significant in gastric cancer (p < 0.05). PER1 (p = 0.0169) and CLOCK (p = 0.0414) were associated with gastric cancer pathological stage (p < 0.05). Gastric cancer patients with high expression of PER1 (p = 0.0028) and NR1D1 (p = 0.016) had longer overall survival, while those with high expression of PER1 (p = 0.042) and NR1D1 (p = 0.016) had longer disease-free survival. The main function of the biological clock gene is related to the circadian rhythms and melatonin metabolism and effects. CLOCK, NPAS2, and KAT2B were key transcription factors for circadian clock genes. In addition, we also found important correlations between circadian clock genes and various immune cells in the gastric cancer microenvironment.

CONCLUSIONS

This study may establish a new gastric cancer prognostic indicator based on the biological clock gene and develop new drugs for the treatment of gastric cancer using biological clock gene targets.

Early gastric cancer detection and lesion segmentation based on deep learning and gastroscopic images

Gastric cancer is a highly prevalent disease that poses a serious threat to public health. In clinical practice, gastroscopy is frequently used by medical practitioners to screen for gastric cancer. However, the symptoms of gastric cancer at different stages of advancement vary significantly, particularly in the case of early gastric cancer (EGC). The manifestations of EGC are often indistinct, leading to a detection rate of less than 10%. In recent years, researchers have focused on leveraging deep learning algorithms to assist medical professionals in detecting EGC and thereby improve detection rates. To enhance the ability of deep learning to detect EGC and segment lesions in gastroscopic images, an Improved Mask R-CNN (IMR-CNN) model was proposed. This model incorporates a "Bi-directional feature extraction and fusion module" and a "Purification module for feature channel and space" based on the Mask R-CNN (MR-CNN). Our study includes a dataset of 1120 images of EGC for training and validation of the models. The experimental results indicate that the IMR-CNN model outperforms the original MR-CNN model, with Precision, Recall, Accuracy, Specificity and F1-Score values of 92.9%, 95.3%, 93.9%, 92.5% and 94.1%, respectively. Therefore, our proposed IMR-CNN model has superior detection and lesion segmentation capabilities and can effectively aid doctors in diagnosing EGC from gastroscopic images.

Clinical and endoscopic features of metachronous gastric cancer with possible lymph node metastasis after endoscopic submucosal dissection and Helicobacter pylori eradication

BACKGROUND

Several studies have reported the metachronous gastric cancers (MGCs) with possible lymph node metastasis (LNM) after endoscopic submucosal dissection (ESD) and Helicobacter pylori (H. pylori) eradication in which a curative ESD had not been achieved. There have been no published reports of evaluations of the features of patients with MGC with possible LNM after ESD and H. pylori eradication.

METHODS

We identified 264 patients with 369 MGCs after H. pylori eradication among the 4354 patients with 5059 early gastric cancers (EGCs) who underwent ESD between 1999 and 2017 and divided them into two groups: patients with MGCs with possible LNM (Group I) and patients with MGCs undergone curative ESD (Group II). We retrospectively compared the features of patients with MGCs and patients with EGCs at index ESD in the two groups.

RESULT

Group I consisted of 20 patients with 21 MGCs, and Group II consisted of 244 patients with 348 MGCs. Group I lesions were significantly more common in the posterior wall than in the lesser curvature (odds ratio [OR] = 3.97; 95% confidence intervals [CI] 1.20-13.10). Development of Group I was significantly more common in patients with a body mass index (BMI) < 19.0 kg/m2 than in patients with a BMI ≥ 19.0 kg/m2 at index ESD (OR = 4.44; 95% CI 1.30-15.20).

CONCLUSIONS

During surveillance endoscopy after gastric ESD and H. pylori eradication, the posterior wall should be carefully examined to detect MGCs early. Lower BMI may be associated with the development of MGCs with possible LNM.

Application of artificial intelligence for improving early detection and prediction of therapeutic outcomes for gastric cancer in the era of precision oncology

Gastric cancer is a leading contributor to cancer incidence and mortality globally. Recently, artificial intelligence approaches, particularly machine learning and deep learning, are rapidly reshaping the full spectrum of clinical management for gastric cancer. Machine learning is formed from computers running repeated iterative models for progressively improving performance on a particular task. Deep learning is a subtype of machine learning on the basis of multilayered neural networks inspired by the human brain. This review summarizes the application of artificial intelligence algorithms to multi-dimensional data including clinical and follow-up information, conventional images (endoscope, histopathology, and computed tomography (CT)), molecular biomarkers, etc. to improve the risk surveillance of gastric cancer with established risk factors; the accuracy of diagnosis, and survival prediction among established gastric cancer patients; and the prediction of treatment outcomes for assisting clinical decision making. Therefore, artificial intelligence makes a profound impact on almost all aspects of gastric cancer from improving diagnosis to precision medicine. Despite this, most established artificial intelligence-based models are in a research-based format and often have limited value in real-world clinical practice. With the increasing adoption of artificial intelligence in clinical use, we anticipate the arrival of artificial intelligence-powered gastric cancer care.

A convolutional neural network-based system for detecting early gastric cancer in white-light endoscopy

BACKGROUND

White-light endoscopy (WLE) is a main and standard modality for detection of early gastric cancer (EGC). The detection rate of EGC is not satisfactory so far. In this single-center retrospective study we developed a convolutional neural network (CNN)-based system to automatically detect EGC in WLE images.

METHODS

An EGC detecting system was constructed based on the CNN architecture EfficientDet. We trained our system with a data set including 4527 images from 130 cases (cancerous images, 1737; noncancerous images, 2790). Then we tested its performance with a data set including 1243 images from 64 cases (cancerous images, 445; noncancerous images, 798).

RESULTS

For case-based analysis, our system successfully detected EGC in 63 of 64 cases and the sensitivity was 98.4%. For image-based analysis, the accuracy was 88.3%. The sensitivity, specificity, positive predictive value and negative predictive value were 84.5%, 90.5%, 83.2% and 91.3%, respectively. The most common cause for false positives was gastritis (57.9%). The most common cause for false negatives was that the lesion was too small with a diameter of 10 mm or less (44.9%).

CONCLUSION

Our CNN-based EGC detecting system was able to achieve satisfactory sensitivity for detecting EGC in WLE images and shows great potential in assisting endoscopists with the detection of EGC.

A Global Perspective on Gastric Cancer Screening: Which Concepts Are Feasible, and When?

BACKGROUND

Gastric cancer (GC) remains the fifth most common cancer and the third most common cause of cancer-related death globally. In 2022, GC fell into the scope of the updated EU recommendations for targeted cancer screening. Given the growing awareness of the GC burden, we aimed to review the existing screening strategies for GC in high-risk regions and discuss potentially applicable modalities in countries with low-to-intermediate incidence.

METHODS

The references for this Review article were identified through searches of PubMed with the search terms "gastric cancer", "stomach cancer", "Helicobacter pylori", and "screening" over the period from 1995 until August 2022.

RESULTS

As Helicobacter pylori (H. pylori)-induced gastritis is the primary step in the development of GC, the focus on GC prevention may be directed toward testing for and treating this infection. Such a strategy may be appealing in countries with low- and intermediate- GC incidence. Other biomarker-based approaches to identify at-risk individuals in such regions are being evaluated. Within high-incidence areas, both primary endoscopic screening and population-based H. pylori "test-and-treat" strategies represent cost-effective models.

CONCLUSIONS

Given the significant variations in GC incidence and healthcare resources around the globe, screening strategies for GC should be adjusted to the actual conditions in each region. While several proven tools exist for accurate GC diagnosis, a universal modality for the screening of GC populations remains elusive.

Current status and future perspective of linked color imaging for gastric cancer screening: a literature review

Screening endoscopy has advanced to facilitate improvements in the detection and prognosis of gastric cancer. However, most early gastric cancers (EGCs) have subtle morphological or color features that are difficult to detect by white-light imaging (WLI); thus, even well-trained endoscopists can miss EGC when using this conventional endoscopic approach. This review summarizes the current and future status of linked color imaging (LCI), a new image-enhancing endoscopy (IEE) method, for gastric screening. LCI has been shown to produce bright images even at a distant view and provide excellent visibility of gastric cancer due to high color contrast relative to the surrounding tissue. LCI delineates EGC as orange-red and intestinal metaplasia as purple, regardless of a history of Helicobacter pylori (Hp) eradication, and contributes to the detection of superficial EGC. Moreover, LCI assists in the determination of Hp infection status, which is closely related to the risk of developing gastric cancer. Transnasal endoscopy (ultra-thin) using LCI is also useful for identifying gastric neoplastic lesions. Recently, several prospective studies have demonstrated that LCI has a higher detection ratio for gastric cancer than WLI. We believe that LCI should be used in routine upper gastrointestinal endoscopies.

Gastric Cancer Screening in Japan: A Narrative Review

Gastric cancer is the second leading cause of cancer incidence in Japan, although gastric cancer mortality has decreased over the past few decades. This decrease is attributed to a decline in the prevalence of H. pylori infection. Radiographic examination has long been performed as the only method of gastric screening with evidence of reduction in mortality in the past. The revised 2014 Japanese Guidelines for Gastric Cancer Screening approved gastric endoscopy for use in population-based screening, together with radiography. While endoscopic gastric cancer screening has begun, there are some problems associated with its implementation, including endoscopic capacity, equal access, and cost-effectiveness. As H. pylori infection and atrophic gastritis are well-known risk factors for gastric cancer, a different screening method might be considered, depending on its association with the individual's background and gastric cancer risk. In this review, we summarize the current status and problems of gastric cancer screening in Japan. We also introduce and discuss the results of gastric cancer screening using H. pylori infection status in Hoki-cho, Tottori prefecture. Further, we review risk stratification as a system for improving gastric cancer screening in the future.

Usefulness of artificial intelligence in early gastric cancer

Gastric cancer (GC) is a major cancer worldwide, with high mortality and morbidity. Endoscopy, important for the early detection of GC, requires trained skills, high-quality technologies, surveillance and screening programs. Early diagnosis allows a better prognosis, through surgical or curative endoscopic therapy. Magnified endoscopy with virtual chromoendoscopy remarkably improve the detection of early gastric cancer (EGC) when endoscopy is performed by expert endoscopists. Artificial intelligence (AI) has also been introduced to GC diagnostics to increase diagnostic efficiency. AI improves the early detection of gastric lesions because it supports the non-expert and experienced endoscopist in defining the margins of the tumor and the depth of infiltration. AI increases the detection rate of EGC, reduces the rate of missing tumors, and characterizes EGCs, allowing clinicians to make the best therapeutic decision, that is, one that ensures curability. AI has had a remarkable evolution in medicine in recent years, moving from the research phase to clinical practice. In addition, the diagnosis of GC has markedly progressed. We predict that AI will allow great evolution in the diagnosis and treatment of EGC by overcoming the variability in performance that is currently a limitation of chromoendoscopy.