Early Detection Of Gastric Cancer Research Articles

Endoscopic diagnosis and treatment in gastric cancer: Current evidence and new perspectives.

Gastric cancer is the fifth most common cause of cancer related deaths worldwide. Despite advancement in endoscopic techniques, the majority of the cases are diagnosed at late stage, when the curative treatment options are very limited. The early gastric cancer (EGC) on the other side is potentially curable, and in selected cases endoscopic resection techniques offer similar survival rates then surgical resection. The detection of EGC is endoscopically challenging and requires high quality examination. Recent data show that close to 10% of the gastric cancer cases had a previous negative endoscopy. This highlights the urgent need to improve the quality of the endoscopy services, what can be achieved by increasing the awareness of gastroenterologists and continuously monitoring the key performance indicators of upper gastrointestinal endoscopy. Newer endoscopic imaging techniques are also becoming commonly available to aid the detection of gastric premalignant lesions and EGC. High-definition endoscopy with image enhancement techniques is preferred over white light endoscopy to recognize these lesions, and they are also useful to determine the invasion depth of EGC. The endoscopic optical characterization of lesions is necessary for the selection of proper resection method and decide whether endoscopic resection techniques can be considered. Artificial intelligence systems aid the detection of EGC and can help to determine the depth of invasion. Endoscopic mucosal resection and endoscopic submucosal dissection requires centralized care and tertiary referral centers with appropriate expertise to ensure proper patient selection, high success rate and low adverse event rate. Appropriately scheduled endoscopic surveillance of high-risk patients, premalignant lesions and after resection of EGC is also important in the early detection and successful treatment of gastric cancer.

Abstract 5304: Development of a plasma biomarker panel for gastric cancer detection based on an autoimmune response signature

Abstract Background: Gastric cancer is frequently diagnosed at an advanced stage and is associated with poor outcomes. Harnessing the immune response to tumor antigens which occurs early during tumor development has potential for the early detection of gastric cancer. Methods: Plasma samples from gastric cancer patients were collected before and after treatment and from healthy controls for identification of immunoglobin (Ig)-bound antigens using mass spectrometry. Ingenuity pathway analysis(IPA) was utilized to investigate networks and pathways associated with Ig bound proteins identified in plasmas from cases. A combined biomarker panel was established based on a logistic-regression model and ROC analysis was used to assess panel performance. Results: A total of 65 Ig bound proteins were identified that were upregulated in gastric cancer patients’ plasma compared with healthy control. Canonical pathways involving these immunogenic proteins included HIF-1α, VEGF, actin cytoskeleton, and immunogenic cell death signaling which play important roles in both gastric cancer development and progression. Four of the top ten performing proteins exhibited high mRNA levels in gastric cancer tissue. The association of these four Ig bound proteins in gastric cancer plasmas was further validated using as controls a cohort of MEN1 patients plasmas. A blood-biomarker panel based on these four biomarkers was established using a logistic-regression model which yielded an area under the curve (AUC) of 0.963 (95% confidential interval [CI] =0.89-1.00) for gastric cancer discrimination with a sensitivity of 0.889 at 95% specificity. Conclusion: Our data reveal a panel of plasma Ig-bound antigens associated with the development of gastric cancer with potential application for early detection and monitoring. Citation Format: Rongzhang Dou, Ehsan Irajizad, Yining Cai, Johannes F. Fahrmann, Jody V. Vykoukal, Yihui Chen, Hiroyuki Katayama, Ali H. Abdel Sater, Melissa Pizzi, Kohei Yamashita, Matheus Sewastjanow Da Silva, Edwin J. Ostrin, Jaffer Ajani, Samir M. Hanash. Development of a plasma biomarker panel for gastric cancer detection based on an autoimmune response signature. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5304.

A comparative study of magnifying endoscopy with narrow-band image and endocytoscopy in the diagnosis of gastric neoplasm: a pilot study.

Endoscopic technologies have recently advanced to optimize the detection and diagnosis of gastric lesions. Endocytoscopy aids in the virtual realization of histology. Herein, we aimed to investigate gastric lesions using single-stain endocytoscopy and compare them using magnifying endoscopy with narrow-band imaging (ME-NBI) in terms of diagnostic yield in vivo. In the present prospective study, we registered 24 patients with gastric neoplasms and retrospectively reviewed their images. Three endoscopists reviewed the images of gastric neoplasms using white light, ME-NBI, and endocytoscopy. The diagnostic yield of endocytoscopy in early gastric cancer (EGC) was assessed using histopathology as the gold standard. Endocytoscopy was performed in 24 patients with gastric neoplasms. Of these, 15 patients had adenocarcinomas, while nine patients had low-grade dysplasia. The sensitivity, specificity, and accuracy of endocytoscopy for EGC detection were reported as 80.0% [95% confidence interval (CI), 51.9-95.7], 66.7% (95% CI, 58.4-91.9), and 75.0% (95% CI, 53.3-90.2) by endoscopist A; 80.0% (95% CI, 51.9-95.7), 44.4% (95% CI, 13.7-78.8), and 66.7% (95% CI, 44.7-84.4) by endoscopist B; and 93.3% (95% CI, 68.1-99.8), 55.6% (95% CI, 21.2-86.3), and 79.2% (95% CI, 57.9-92.8) by endoscopist C; these findings were not inferior to NBI. The inter-observer agreement, κ statistic = 0.67 (95% CI, 0.43-0.90) was favorable. Endocytoscopy aid in the diagnosis of EGC because of its better sensitivity and accuracy compared to NBI or white-light imaging. However, further large-scale studies are required to confirm our findings.

Intensive Systematic “Train-the-Trainer” Course as an Effective Strategy to Improve Detection of Early Gastric Cancer: A Multicenter Retrospective Study

BackgroundTo improve the diagnosis of early gastric cancer (EGC), a train-the-trainer (TTT) course was developed. This trial aimed to investigate whether TTT courses from trained trainers could improve trainees’ EGC detection. MethodsIn this multi-center, retrospective study, the training was carried out 8 times in one year. Clinical records one year before (“2016”), during (“2017”), and after (“2018”) the course were collected. The primary endpoint was the improvement of EGC detection rate after TTT courses. ResultsTwenty-four trainees from 17 hospitals were included in this study. A total of 123,416 esophagogastroduodenoscopy and 65,570 colonoscopy procedures were analyzed. The early gastric cancer detection rate (EDR) was 0.101% in 2016, which significantly increased to 0.338% in 2018 (p = 0.015). The early gastric cancer ratio (ECR, ratio of newly detected EGCs to all newly detected gastric cancers) in 2016 was 8.440%, which consistently increased to 11.853% and 19.778% in 2017 and 2018 (p = 0.006), respectively. In contrast, the advanced gastric cancer detection rate (ADR) was similar before, during, or after the course (p = 0.987). The 3-year EDR, ECR, and ADR in esophageal and colorectal cancer were not significantly different. ConclusionsThe systematic training course can improve EGC detection rate and may be an effective educational strategy to reduce gastrointestinal cancers mortality.

A Liquid Biopsy Signature for the Early Detection of Gastric Cancer in Patients

Diagnosing gastric cancer (GC) while the disease remains eligible for surgical resection is challenging. In view of this clinical challenge, novel and robust biomarkers for early detection thus improving prognosis of GC are necessary. The present study is to develop a blood-based long noncoding RNA (LR) signature for the early-detection of GC. The present 3-step study incorporated data from 2141 patients, including 888 with GC, 158 with chronic atrophic gastritis, 193 with intestinal metaplasia, 501 healthy donors, and 401 with other gastrointestinal cancers. The LR profile of stage I GC tissue samples were analyzed using transcriptomic profiling in discovery phase. The extracellular vesicle (EV)-derived LR signature was identified with a training cohort (n= 554) and validated with 2 external cohorts (n= 429 and n= 504) and a supplemental cohort (n= 69). In discovery phase, one LR (GClnc1) was found to be up-regulated in both tissue and circulating EV samples with an area under the curve (AUC) of 0.9369 (95% confidence interval [CI], 0.9073-0.9664) for early-stage GC (stage I/II). The diagnostic performance of this biomarker was further confirmed in 2 external validation cohorts (Xi'an cohort, AUC: 0.8839; 95% CI: 0.8336-0.9342; Beijing cohort, AUC: 0.9018; 95% CI: 0.8597-0.9439). Moreover, EV-derived GClnc1 robustly distinguished early-stage GC from precancerous lesions (chronic atrophic gastritis and intestinal metaplasia) and GC with negative traditional gastrointestinal biomarkers (CEA, CA72-4, and CA19-9). The low levels of this biomarker in postsurgery and other gastrointestinal tumor plasma samples indicated its GC specificity. EV-derived GClnc1 serves as a circulating biomarker for the early detection of GC, thus providing opportunities for curative surgery and improved survival outcomes.

Comparative analysis of the outcomes of gastrectomy vs. endoscopic mucosal resection or endoscopic submucosal dissection for the treatment of gastric tube cancer after esophagectomy.

This study investigated the clinical characteristics of patients with gastric tube cancer following esophagectomy at our hospital, and to examine the outcomes of gastrectomy versus endoscopic submucosal dissection. Of 49 patients who underwent treatment for gastric tube cancer that developed 1 year or more after esophagectomy, 30 patients underwent subsequent gastrectomy (Group A), and 19 patients underwent endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) (Group B). The characteristics and outcomes of these two groups were compared. The interval between esophagectomy and diagnosis of gastric tube cancer ranged from 1 to 30 years. The most common location was the lesser curvature of the lower gastric tube. When the cancer was detected early, EMR or ESD was performed, and the cancer did not recur. In advanced tumors, gastrectomy was performed but the gastric tube was difficult to approach and lymph node dissection was difficult; two patients died as a result of the gastrectomy. In Group A, recurrence occurred most often as axillary lymph node, bone, or liver metastases; in Group B, no recurrence or metastases were observed. In addition to recurrence and metastasis, gastric tube cancer is often observed after esophagectomy. The present findings highlight the importance of early detection of gastric tube cancer after esophagectomy and that the EMR and ESD procedures are safe and have significantly fewer complications compared with gastrectomy. Follow-up examinations should be scheduled with consideration given to the most frequent sites of gastric tube cancer occurrence and the time elapsed since esophagectomy.

Identification of PRTN3 as a novel biomarker for the diagnosis of early gastric cance

Gastric cancer (GC) remains one of the most common types of cancer worldwide and has a high mortality rate. However, tools for the early detection of gastric cancer are still lacking. Isobaric tagging for relative and absolute quantitation (iTRAQ) proteomic assays were conducted to identify and quantify the differentially expressed proteins (DEPs) in the gastric mucosal tissues of GC patients at different stages. Bioinformatics analysis was used to identify the pathways enriched among the DEPs and candidate marker proteins. The expression levels and distribution of candidate proteins were confirmed by parallel reaction monitoring (PRM) analysis. In this study, by using the iTRAQ quantitative proteomic strategy, we identified 727 and 502 DEPs that were upregulated in EGC vs. PGC and EGC vs. NGC, respectively. These DEPs were mainly involved in the innate immune response and RNA binding. PRTN3 was identified as a marker of early gastric cancer by Gene Ontology enrichment analysis. Furthermore, the PRM assay confirmed the significant overexpression of PRTN3 in EGC gastric mucosa compared to PGC and NGC mucosa. Our data demonstrated that PRTN3 in the gastric mucosa could be used as a novel biomarker to identify patients with early gastric cancer via endoscopy. SignificanceGastric cancer remains one of the most common types of cancer worldwide and has a high mortality rate. Patients with progressive gastric cancer and gastroesophageal junction cancer have a poor prognosis, with a 5-year relative survival rate of 6%. Therefore, early detection and diagnosis of gastric cancer is a key step toward improving the survival rate.The present study identified PRTN3 as a marker of early gastric cancer by an iTRAQ quantitative proteomic strategy. The PRM assay confirmed the significant overexpression of PRTN3 in EGC gastric mucosa compared to PGC and NGC mucosa. This study discovered that PRTN3 in the gastric mucosa could be used as a novel biomarker to identify patients with early gastric cancer via endoscopy.

Application of Aptamer-based Biosensors in Early Diagnosis of Gastric Cancer: A Promising Method for Early Detection

Personalized medicine is a relatively new approach that addresses differences between patients based on unique features such as genetic make-up, environment, and physiology. Aptamers are synthetic sequences of single-stranded DNA or RNA with a particular three-dimensional conformation that binds to a target. Aptamer-based biosensors are promising tools to detect disease markers, especially in cancer. Gastric cancer is the third most common cause of cancer-related deaths worldwide and has very high prevalence in Asia. Currently, there is a lack of effective screening tools for the early detection of gastric cancer. Thus, identifying new methods to detect markers of gastric cancer is crucial. In this study, the role of aptamer-based biomarkers in early diagnosis of gastric cancer is reviewed.

A novel detection technology for early gastric cancer based on Raman spectroscopy

Despite universal endoscopic screening, early detection of gastric cancer is challenging, led researchers to seek for a novel approach in detecting. Raman spectroscopy measurements as a fingerprint of biochemical structure, enable accurate prediction of gastric lesions non-destructively. This study aimed to evaluate the diagnostic power of Raman spectroscopy in early gastric cancer (EGC), and reveal dynamic biomolecular changes in vitro from normal to EGC. To clarify the biochemical alterations in Correa’s cascade, Raman spectra of human normal gastric mucosa, intestinal metaplasia, dysplasia, and adenocarcinoma were compared at tissue and cellular levels based on a self-developed data processing program. For effectively identify EGC, Raman spectroscopy was used combined with multiple machine learning methods, including partial least-squares discriminant analysis (PLS-DA), support vector machine (SVM), and convolutional neural network (CNN) with leave-one-out (LOO) cross validation. A total of 450 Raman spectra were investigated in this study. The upregulation of νsym(O-P-O) backbone (p < 0.001) was identified as a favorable factor for the diagnosis of EGC, the area under the ROC curve (AUC) was up to 0.918. In addition, higher levels of lactic acid (p < 0.001), lipids (p < 0.001), phenylalanine (p = 0.002), and carotenoids (p < 0.001) were detected in EGC. Multivariate machine learning methods for diagnosis of EGC based on Raman spectroscopy, the sensitivity, specificity, accuracy, and AUC were 91.0%, 100%, 94.8%, and 95.8% for SVM, and 84.8%, 92.0%, 88.8%, and 95.5% for CNN, respectively. Raman spectroscopy can be used as a powerful tool for detecting EGC while elucidating biomolecular dynamics in tumorigenesis. (Chictr.org.cn, ChiCTR2200060720.)

A multiplex DNA methylation assay for noninvasive early detection of gastric cancer.

300 Background: Gastric cancer (GC) is the fifth leading cancer that ranked fourth in cancer mortality worldwide. Early screening and detection can significantly improve patients’ prognosis and survival. Although in standard GC diagnosis, gastroscopy provides sufficient diagnostic power for identifying GC in early stages, the invasive and costly procedure is not suitable for screening and has low compliance in countries with high GC incidence and large populations. Furthermore, the serum-based GC related biomarkers and non-invasive tests (serum pepsinogen I and II, gastrin-17, and anti-Helicobacter pylori IgG antibody tests) showed insufficient evidence as cost-effective GC screening approaches. A non-invasive DNA methylation assay with high sensitivity was aimed to develop and validate as a potential alternative for GC early detection. Methods: A multiplex quantitative PCR (qPCR) assay for detecting GC-specific DNA methylation markers in plasma was developed, in which the markers were further optimized from a reported seven-marker panel based on a pilot case-control study with 63 GC and 130 non-GC samples. The GC detection model of the assay was developed and locked down in the first cohort consisting of 54 GC and 79 non-GC samples. The second validation cohort with 117 GC and 309 non-GC samples was used to evaluate the assay performance. All plasma samples used in the two cohorts were prospectively collected from patients diagnosed with GC or other gastrointestinal diseases or from healthy donors. Results: The multiplex DNA methylation assay showed an overall sensitivity and specificity of 80% (95% CI: 77–83%) and 65% (95% CI: 63–66%) in the model development cohort, respectively, with a sensitivity of 80% (95% CI: 65–95%, n=10) in the detection of stage I GC. The assay revealed similar performance characteristics in the validation cohort, with an overall sensitivity of 82% (95% CI: 80–83%), a specificity of 69% (95% CI: 68–69%), and a sensitivity of 78% (95% CI: 74–82%, n=41) for the stage I GC detection. The DNA methylation assay outperformed the reported serum-based GC related biomarkers and non-invasive tests (sensitivity of 69.6-70.7% and specificity of 66.8-67.8%) with higher sensitivity, particularly in the stage I GC with a comparable specificity. Conclusions: The multiplex DNA methylation assay provides a potential cost-effective tool that features high sensitivity for GC early detection.

Linked Color Imaging for Stomach.

Image-enhanced endoscopy (IEE) plays an important role in the detection and further examination of gastritis and early gastric cancer (EGC). Linked color imaging (LCI) is also useful for detecting and evaluating gastritis, gastric intestinal metaplasia as a pre-cancerous lesion, and EGC. LCI provides a clear excellent endoscopic view of the atrophic border and the demarcation line under various conditions of gastritis. We could recognize gastritis as the lesions of the diffuse redness to purple color area with LCI. On the other hand, EGCs are recognized as the lesions of the orange-red, orange, or orange-white color area in the lesion of the purple color area, which is the surround atrophic mucosa with LCI. With further prospective randomized studies, we will be able to evaluate the diagnosis ability for EGC by IEE, and it will be necessary to evaluate the role of WLI/IEE and the additional effects of the diagnostic ability by adding IEE to WLI in future.

The Role of Artificial Intelligence in Gastric Cancer: Surgical and Therapeutic Perspectives: A Comprehensive Review.

Stomach cancer has a high annual mortality rate worldwide necessitating early detection and accurate treatment. Even experienced specialists can make erroneous judgments based on several factors. Artificial intelligence (AI) technologies are being developed rapidly to assist in this field. Here, we aimed to determine how AI technology is used in gastric cancer diagnosis and analyze how it helps patients and surgeons. Early detection and correct treatment of early gastric cancer (EGC) can greatly increase survival rates. To determine this, it is important to accurately determine the diagnosis and depth of the lesion and the presence or absence of metastasis to the lymph nodes, and suggest an appropriate treatment method. The deep learning algorithm, which has learned gastric lesion endoscopyimages, morphological characteristics, and patient clinical information, detects gastric lesions with high accuracy, sensitivity, and specificity, and predicts morphological characteristics. Through this, AI assists the judgment of specialists to help select the correct treatment method among endoscopic procedures and radical resections and helps to predict the resection margins of lesions. Additionally, AI technology has increased the diagnostic rate of both relatively inexperienced and skilled endoscopic diagnosticians. However, there were limitations in the data used for learning, such as the amount of quantitatively insufficient data, retrospective study design, single-center design, and cases of non-various lesions. Nevertheless, this assisted endoscopic diagnosis technology that incorporates deep learning technology is sufficiently practical and future-oriented and can play an important role in suggesting accurate treatment plans to surgeons for resection of lesions in the treatment of EGC.

Analysis of high risk factors for chronic atrophic gastritis.

Screening for chronic atrophic gastritis (CAG) is crucial for the prevention and early detection of gastric cancer. Endoscopy is the main method of CAG diagnosis, with high training requirements and limited accuracy, making it difficult to popularize. The study attempts to improve the positive rate and accuracy of CAG screening through non-invasive testing. A total of 2564 patients who underwent gastroscopy were included in this study. The results of gastroscopic evaluation, histological biopsy results (including H. pylori biopsy), urea breath test (UBT) results, serum pepsinogen, and testosterone were statistically analyzed. We found significant differences in the diagnosis of CAG between endoscopy and histological biopsy. Pepsinogen II and pepsinogen I/II ratio were more useful for the diagnosis of CAG compared with pepsinogen I. The risk of CAG was increased when pepsinogen II exceeded 11.05 μg/L, and the pepsinogen I/II ratio was less than 3.75. CAG positivity was higher in patients with positive H. pylori infection on UBT screening. In addition, higher levels of testosterone, SHBG and HSD17B2, and lower level of GNRH1 were found in CAG mucosa. Patients with high serum testosterone had a higher risk of CAG. CAG screening should be combined with endoscopic evaluation, biopsy, and other non-invasive tests. Non-invasive tests include the combination of serum pepsinogen II protein and pepsinogen I/II ratio and high level of serum testosterone. UBT combined with serum pepsinogen testing may improve the positive rate of CAG and reduce gastric mucosal damage from multiple biopsies.

Dynamic gene screening enabled identification of a 10-gene panel for early detection and progression assessment of gastric cancer

Early diagnosis and progression assessment are critical for the timely detection and treatment of gastric cancer (GC) patients. Identification of diagnostic biomarkers for early detection of GC represents an unmet clinical need, and how these markers further influence GC progression is explored rarely. We performed dynamic gene screening based on high-throughput data analysis from patients with precancerous lesions and early gastric cancer (EGC) and identified a 10-gene panel by the lasso regression model. This panel demonstrated good diagnostic performance in TCGA (AUC = 0.95, sensitivity = 86.67 %, specificity = 90.63 %) and GEO (AUC = 0.84, sensitivity = 91.67 %, specificity = 78.13 %) cohorts. Moreover, three GC subtypes were clustered based on this panel, in which cluster 2 (C2) demonstrated the highest tumor progression level with a high expression of 10 genes, showing a decreased tumor mutation burden, significantly enriched epithelial-mesenchymal transition hallmark and increased immune exclusion/exhausted features. Finally, the cell localization of these panel genes was explored in scRNA-seq data based on more than 40,000 cells. The 10-gene panel is expected to be a new clinical early detection signature for GC and may aid in progression assessment and personalized treatment of patients.

A system based on deep convolutional neural network improves the detection of early gastric cancer.

Early gastric cancer (EGC) has a high survival rate, but it is difficult to diagnosis. Recently, artificial intelligence (AI) based on deep convolutional neural network (DCNN) has made significant progress in the field of gastroenterology. The purpose of this study was to establish a DCNN assist system to improve the detection of EGC. 3400 EGC and 8600 benign images were collected to train the DCNN to detect EGC. Subsequently, its diagnostic ability was compared to that of endoscopists using an independent internal test set (ITS, including 1289 images) and an external test set (ETS, including 542 images) come from three digestive center. The diagnostic time of DCNN and endoscopists were 0.028s, 8.05 ± 0.21s, 7.69 ± 0.25s in ITS, and 0.028s, 7.98 ± 0.19s, 7.50 ± 0.23s in ETS, respectively. In ITS, the diagnostic sensitivity and accuracy of DCNN are 88.08%(95% confidence interval,95%CI,85.24%-90.44%), 88.60% (95%CI,86.74%-90.22%), respectively. In ETS, the diagnostic sensitivity and accuracy are 92.08% (95%CI, 87.91%- 94.94%),92.07%(95%CI, 89.46%-94.08%),respectively. DCNN outperformed all endoscopists in ETS, and had a significantly higher sensitivity than the junior endoscopists(JE)(by18.54% (95%CI, 15.64%-21.84%) in ITS, also higher than JE (by21.67%,95%CI, 16.90%-27.32%) and senior endoscopists (SE) (by2.08%, 95%CI, 0.75%-4.92%)in ETS. The accuracy of DCNN model was higher (by10.47%,95%CI, 8.91%-12.27%) than that of JE in ITS, and also higher (by14.58%,95%CI, 11.84%-17.81%; by 1.94%,95%CI,1.25%-2.96%, respectively) than JE and SE in ETS. The DCNN can detected more EGC images in a shorter time than the endoscopists. It will become an effective tool to assist in the detection of EGC in the near future.

Early detection of gastric cancer via high-resolution terahertz imaging system.

Terahertz (THz) wave has demonstrated a good prospect in recent years, but the resolution is still one of the problems that restrict the application of THz technology in medical imaging. Paraffin-embedded samples are mostly used in THz medical imaging studies, which are thicker and significantly different from the current gold standard slice pathological examination in sample preparation. In addition, THz absorption in different layers of normal and cancerous tissues also remains to be further explored. In this study, we constructed a high-resolution THz imaging system to scan non-tumorous adjacent tissue slices and gastric cancer (GC) tissue slices. In this system, a THz quantum cascade laser emitted a pulsed 3THz signal and the transmitted THz wave was received by a THz detector implemented in a 65nm CMOS process. The slice thickness was only 20μm, which was close to that of the medical pathology examination. We successfully found THz transmittance differences between different layers of normal gastric tissues based on THz images, and the resolution could reach 60μm for the first time. The results indicated that submucosa had a lower THz transmittance than that of mucosa and muscular layer in non-tumorous adjacent tissue. However, in GC tissue, THz transmittance of mucosa and submucosa was similar, caused by the decreased transmittance of mucosa, where the cancer occurs. Therefore, we suppose that the similar terahertz transmittance between gastric mucosa and submucosa may indicate the appearance of cancerization. The images obtained from our THz imaging system were clearer than those observed with naked eyes, and can be directly compared with microscopic images. This is the first application of THz imaging technology to identify non-tumorous adjacent tissue and GC tissue based on the difference in THz wave absorption between different layers in the tissue. Our present work not only demonstrated the potential of THz imaging to promote early diagnosis of GC, but also suggested a new direction for the identification of normal and cancerous tissues by analyzing differences in THz transmittance between different layers of tissue.

Early detection of gastric cancer in China: progress and opportunities.

In the past decade, the Chinese government has committed to promoting gastric cancer (GC) prevention and control[1][1],[2][2]. GC is a major malignancy globally, and nearly half of GC cases and specific deaths worldwide occur in China; moreover, GC is frequently diagnosed in local advanced or

Interventional gastroenterology in oncology.

Cancer is one of the foremost health problems worldwide and is among the leading causes of death in the United States. Gastrointestinal tract cancers account for almost one third of the cancer-related mortality globally, making it one of the deadliest groups of cancers. Early diagnosis and prompt management are key to preventing cancer-related morbidity and mortality. With advancements in technology and endoscopic techniques, endoscopy has become the core in diagnosis and management of gastrointestinal tract cancers. In this extensive review, the authors discuss the role endoscopy plays in early detection, diagnosis, and management of esophageal, gastric, colorectal, pancreatic, ampullary, biliary tract, and small intestinal cancers.

Urine proteomic signatures predicting the progression from premalignancy to malignant gastric cancer.

Early detection of gastric cancer (GC) remains challenging. We aimed to examine urine proteomic signatures and identify protein biomarkers that predict the progression of gastric lesions and risk of GC. A case-control study was initially designed, covering subjects with GC and gastric lesions of different stages. Subjects were aged 40-69 years, without prior diagnosis of renal or urological diseases. We enrolled a total of 255 subjects, with 123 in the discovery stage from Linqu, China, a high-risk area for GC and 132 in the validation stage from Linqu and Beijing. A prospective study was further designed for a subset of 60 subjects with gastric lesions, which were followed for 297-857 days. We identified 43 differentially expressed urine proteins in subjects with GC vs. mild or advanced gastric lesions. Baseline urinary levels of ANXA11, CDC42, NAPA and SLC25A4 were further positively associated with risk of gastric lesion progression. Three of them, except for SLC25A4, also had higher expression in GC than non-GC tissues. Integrating these four proteins showed outstanding performance in predicting the progression of gastric lesions (AUC (95% CI): 0.92 (0.83-1.00)) and risk of GC (AUC (95% CI): 0.81 (0.73-0.89) and 0.84 (0.77-0.92) for GC vs. mild or advanced gastric lesions respectively). This study revealed distinct urine proteomic profiles and a panel of proteins that may predict the progression of gastric lesions and risk of GC. These biomarkers in a non-invasive approach may have translational significance for defining high-risk populations of GC and its early detection. Funders are listed in the Acknowledgement.

Application of artificial intelligence in the endoscopic diagnosis of early gastric cancer, atrophic gastritis, and Helicobacter pylori infection.

Gastric cancer (GC) is one of the most serious health problems worldwide. Chronic atrophic gastritis (CAG) is most commonly caused by Helicobacter pylori (H. pylori) infection. Currently, endoscopic detection of early gastric cancer (EGC) and CAG remains challenging for endoscopists, and the diagnostic accuracy of H. pylori infection by endoscopy is approximately 70%. Artificial intelligence (AI) can assist endoscopic diagnosis including detection, prediction of depth of invasion, boundary delineation, and anatomical location of EGC, and has achievable diagnostic ability even comparable to experienced endoscopists. In this review we summarized various AI-assisted systems in the diagnosis of EGC, CAG, and H. pylori infection.