Narrow-Band Imaging: Clinical Application in Gastrointestinal Endoscopy

Narrow-Band Imaging in Head and Neck Carcinomas: A Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of this study was to assess the diagnostic performance of narrow-band imaging (NBI) in monitoring patients with head and neck carcinomas posttreatment and to compare it with that of white light endoscopy (WLE).

DATA SOURCES

PubMed, Embase, Web of Science (WOS), Cochrane Library, China Biology Medicine disc (CBM disc), China National Knowledge Internet (CNKI), Wanfang Data, China Science and Technology Journal Database (CSTJ), Chinese Clinical Trial Register.

REVIEW METHODS

Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), literature published before July 2024 was searched. Patients who underwent surgery, radiotherapy (RT), or chemo-RT for head and neck carcinomas with posttreatment follow-up using NBI were analyzed. The main outcomes were sensitivity, specificity, and diagnostic odds ratio (DOR) for NBI and WLE in posttreatment follow-up.

RESULTS

The sensitivity, specificity, and DOR for NBI and WLE in posttreatment follow-up for head and neck carcinomas were 95% (95% confidence interval [CI]: 88%-98%), 96% (95% CI: 92%-98%), 433 (95% CI: 120-1560) and 72% (95% CI: 49%-87%), 72% (95% CI: 4%-99%), 7 (95% CI: 0-191). Additionally, the area under the curve (AUC) values for NBI and WLE were 0.99 (95% CI: 0.97-0.99) and 0.75 (95% CI: 0.71-0.79), respectively. The number of lesions and patients, treatment modality, follow-up time, disease, and endoscopic system might be sources of heterogeneity.

CONCLUSION

Compared to WLE, NBI demonstrated superior diagnostic performance in follow-up patients with head and neck carcinoma posttreatment. NBI offers technical support and a clinical foundation for early detection of head and neck carcinoma recurrence.

LEVEL OF EVIDENCE

NA Laryngoscope, 135:34-44, 2025.

A Review of Application of Deep Learning in Endoscopic Image Processing

Deep learning, particularly convolutional neural networks (CNNs), has revolutionized endoscopic image processing, significantly enhancing the efficiency and accuracy of disease diagnosis through its exceptional ability to extract features and classify complex patterns. This technology automates medical image analysis, alleviating the workload of physicians and enabling a more focused and personalized approach to patient care. However, despite these remarkable achievements, there are still opportunities to further optimize deep learning models for endoscopic image analysis, including addressing limitations such as the requirement for large annotated datasets and the challenge of achieving higher diagnostic precision, particularly for rare or subtle pathologies. This review comprehensively examines the profound impact of deep learning on endoscopic image processing, highlighting its current strengths and limitations. It also explores potential future directions for research and development, outlining strategies to overcome existing challenges and facilitate the integration of deep learning into clinical practice. Ultimately, the goal is to contribute to the ongoing advancement of medical imaging technologies, leading to more accurate, personalized, and optimized medical care for patients.

Narrow band imaging: Important tool for early diagnosis, management, and improved outcomes in gastrointestinal lesions

BACKGROUND

Narrow band imaging-magnifying endoscopy (NBI-ME) is used to identify changes in mucosal or vascular pattern observed on GI endoscopy in real time on the basis of optical image enhancement.It has a significant role in early detection of dysplasia, premalignant, and Malignant lesions along with its depth of invasion.

MATERIALS AND METHODS

Upper and lower GI endoscopy performed in 1742 patients who presented with gastrointestinal symptoms at this tertiary center over 5 years out of which 1623 were evaluated with both NBI-ME and histopathology. Real time endoscopic assessment was performed. Targeted biopsies were taken for comparative analysis.

RESULTS

Of the 1742 patients, 119 were excluded from the study. 807 underwent upper GI endoscopy and 816 underwent lower GI endoscopy. Mean age of presentation was 38 +/- 2.7 years. Sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of NBI-ME for neoplastic esophageal lesions were 96.3%, 90.6%, 91.1%, 96.03%, respectively. For Barrett's esophagus it was 95.4%, 90.7%, 86.1%, and 90.7%; For gastric neoplastic lesions the values were 96.1%, 91.04%, 83.8%, and 97.9%. For colorectal it was 96.7%, 91.3%, 88.0%, and 97.7%. Overall sensitivity, specificity, PPV, and NPV of NBI-ME for neoplastic lesions (both upper and lower GI) were 96.2%, 91.0%, 96.2%, and 97.2%. Of the 1623 patients, 951 received medical management with regular surveillance and 672 patients with high-grade dysplasia, premalignant, and malignant conditions underwent interventions in form of either endoscopy or surgery.

CONCLUSION

NBI-ME has a greater role and can be considered as an effective tool in making early diagnosis and guiding optimum treatment.

Precision endoscopy in colorectal polyps' characterization and planning of endoscopic therapy

Precision endoscopy in the management of colorectal polyps and early colorectal cancer has emerged as the standard of care. It includes optical characterization of polyps and estimation of submucosal invasion depth of large nonpedunculated colorectal polyps to select the appropriate endoscopic resection modality. Over time, several imaging modalities have been implemented in endoscopic practice to improve optical performance. Among these, image-enhanced endoscopy systems and magnification endoscopy represent now well-established tools. New advanced technologies, such as endocytoscopy and confocal laser endomicroscopy, have recently shown promising results in predicting the histology of colorectal polyps. In recent years, artificial intelligence has continued to enhance endoscopic performance in the characterization of colorectal polyps, overcoming the limitations of other imaging modes. In this review we retrace the path of precision endoscopy, analyzing the yield of various endoscopic imaging techniques in personalizing management of colorectal polyps and early colorectal cancer.

Design and Evaluation of ScanCap: A Low-Cost, Reusable Tethered Capsule Endoscope with Blue-Green Illumination Imaging for Unsedated Screening and Early Detection of Barrett's Esophagus

Esophageal carcinoma is the sixth-leading cause of cancer death worldwide. A precursor to esophageal adenocarcinoma (EAC) is Barrett's Esophagus (BE). Early-stage diagnosis and treatment of esophageal neoplasia (Barrett's with high-grade dysplasia/intramucosal cancer) increase the five-year survival rate from 10% to 98%. BE is a global challenge; however, current endoscopes for early BE detection are costly and require extensive infrastructure for patient examination and sedation. We describe the design and evaluation of the first prototype of ScanCap, a high-resolution optical endoscopy system with a reusable, low-cost tethered capsule, designed to provide high-definition, blue-green illumination imaging for the early detection of BE in unsedated patients. The tethered capsule (12.8 mm diameter, 35.5 mm length) contains a color camera and rotating mirror and is designed to be swallowed; images are collected as the capsule is retracted manually via the tether. The tether provides electrical power and illumination at wavelengths of 415 nm and 565 nm and transmits data from the camera to a tablet. The ScanCap prototype capsule was used to image the oral mucosa in normal volunteers and ex vivo esophageal resections; images were compared to those obtained using an Olympus CV-180 endoscope. Images of superficial capillaries in intact oral mucosa were clearly visible in ScanCap images. Diagnostically relevant features of BE, including irregular Z-lines, distorted mucosa, and dilated vasculature, were clearly visible in ScanCap images of ex vivo esophageal specimens.

Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks

The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model's performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5's design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2-87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1-54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6-83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0-51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.

Artificial intelligence-aided diagnostic imaging: A state-of-the-art technique in precancerous screening

BACKGROUND AND AIM

Chromoendoscopy with the use of indigo carmine (IC) dye is a crucial endoscopic technique to identify gastrointestinal neoplasms. However, its performance is limited by the endoscopist's skill, and no standards are available for lesion identification. Thus, we developed an artificial intelligence (AI) model to replace chromoendoscopy.

METHODS

This pilot study assessed the feasibility of our novel AI model in the conversion of white-light images (WLI) into virtual IC-dyed images based on a generative adversarial network. The predictions of our AI model were evaluated against the assessments of five endoscopic experts who were blinded to the purpose of this study with a staining quality rating from 1 (unacceptable) to 4 (excellent).

RESULTS

The AI model successfully transformed the WLI of polyps with different morphologies and different types of lesions in the gastrointestinal tract into virtual IC-dyed images. The quality ratings of the real IC-dyed and AI images did not significantly differ concerning surface structure (AI vs IC: 3.08 vs 3.00), lesion border (3.04 vs 2.98), and overall contrast (3.14 vs 3.02) from 10 sets of images (10 AI images and 10 real IC-dyed images). Although the score depended significantly on the evaluator, the staining methods (AI or real IC) and evaluators had no significant interaction (P > 0.05) with each other.

CONCLUSION

Our results demonstrated the feasibility of employing AI model's virtual IC staining, increasing the possibility of being employed in daily practice. This novel technology may facilitate gastrointestinal lesion identification in the future.

Application of artificial intelligence in colorectal cancer screening by colonoscopy: Future prospects (Review)

Colorectal cancer (CRC) has become a severe global health concern, with the third‑high incidence and second‑high mortality rate of all cancers. The burden of CRC is expected to surge to 60% by 2030. Fortunately, effective early evidence‑based screening could significantly reduce the incidence and mortality of CRC. Colonoscopy is the core screening method for CRC with high popularity and accuracy. Yet, the accuracy of colonoscopy in CRC screening is related to the experience and state of operating physicians. It is challenging to maintain the high CRC diagnostic rate of colonoscopy. Artificial intelligence (AI)‑assisted colonoscopy will compensate for the above shortcomings and improve the accuracy, efficiency, and quality of colonoscopy screening. The unique advantages of AI, such as the continuous advancement of high‑performance computing capabilities and innovative deep‑learning architectures, which hugely impact the control of colorectal cancer morbidity and mortality expectancy, highlight its role in colonoscopy screening.

Advances on diagnosis of Helicobacter pylori infections

The association of Helicobacter pylori to several gastric diseases, such as chronic gastritis, peptic ulcer disease, and gastric cancer, and its high prevalence worldwide, raised the necessity to use methods for a proper and fast diagnosis and monitoring the pathogen eradication. Available diagnostic methods can be classified as invasive or non-invasive, and the selection of the best relies on the clinical condition of the patient, as well as on the sensitivity, specificity, and accessibility of the diagnostic test. This review summarises all diagnostic methods currently available, including the invasive methods: endoscopy, histology, culture, and molecular methods, and the rapid urease test (RUT), as well as the non-invasive methods urea breath test (UBT), serological assays, biosensors, and microfluidic devices and the stool antigen test (SAT). Moreover, it lists the diagnostic advantages and limitations, as well as the main advances for each methodology. In the end, research on the development of new diagnostic methods, such as bacteriophage-based H. pylori diagnostic tools, is also discussed.

Case report: Streptomycin combined with dacronin in painless magnifying endoscopy

RATIONALE

To analyze the effect of streptomyces protease combined with dyclonine in painless magnifying endoscopy.

PATIENT CONCERNS

A total of 100 patients who underwent magnification endoscopy in our hospital from January 2021 to June 2022 were retrospectively analyzed.

DIAGNOSES

The diagnoses were made by painless magnifying endoscopy and narrow-band imaging combined with pathological findings.

INTERVENTIONS

The patients were divided into the observation group and control group, the observation group was streptomyces protease combined with dyclonine group, and the control group was dyclonine group, 50 cases in each group. The visibility score under gastroscopy was compared between the 2 groups, and the morphological classification of gastric pit and collecting veins was observed. The detection rates of small lesions and early cancer were compared between the 2 groups. The examination time and adverse reactions were compared between the 2 groups.

OUTCOMES

Compared with the control group, the streptomyces protease combined with dyclonine group had better clear visibility and a higher detection rate of small lesions, but there was no significant difference in early cancer detection rate between the 2 groups. The examination time of streptomyces protease combined with dyclonine group was relatively prolonged, but there was no significant difference in the incidence of adverse reactions between the 2 groups.

LESSONS

Streptomyces protease combined with dyclonine plays a certain role in painless magnifying gastroscopy, which can improve the visibility of gastroscopy, improve the detection rate of small lesions, help to find gastric dysplasia and early gastric cancer diagnosis, and does not increase the incidence of adverse reactions.

Narrow Band Imaging versus White Light for the Detection of Sessile Serrated Colorectal Lesions: A Randomized Clinical Trial

Background

Colorectal cancer (CRC) is a leading cause of cancer. The detection of pre-malignant lesions by colonoscopy is associated with reduced CRC incidence and mortality. Narrow band imaging has shown promising but conflicting results for the detection of serrated lesions.

Methods

We performed a randomized clinical trial to compare the mean detection of serrated lesions and hyperplastic polyps ≥10 mm with NBI or high-definition white light (HD-WL) withdrawal. We also compared all sessile serrated lesions (SSLs), adenoma, and polyp prevalence and rates.

Results

Overall, 782 patients were randomized (WL group 392 patients; NBI group 390 patients). The average number of serrated lesions and hyperplastic polyps ≥10 mm detected per colonoscopy (primary endpoint) was similar between the HD-WL and NBI group (0.118 vs. 0.156, p = 0.44). Likewise, the adenoma detection rate (55.2% vs. 53.2%, p = 0.58) and SSL detection rate (6.8% vs. 7.5%, p = 0.502) were not different between the two study groups. Withdrawal time was higher in the NBI group (10.88 vs. 9.47 min, p = 0.004), with a statistically nonsignificant higher total procedure time (20.97 vs. 19.30 min, p = 0.052).

Conclusions

The routine utilization of narrow band imaging does not improve the detection of serrated class lesions or any pre-malignant lesion and increases the withdrawal time.

Next-Generation Endoscopy in Inflammatory Bowel Disease

Endoscopic healing is recognized as a primary treatment goal in Inflammatory Bowel Disease (IBD). However, endoscopic remission may not reflect histological remission, which is crucial to achieving favorable long-term outcomes. The development of new advanced techniques has revolutionized the field of IBD assessment and management. These tools can accurately assess vascular and mucosal features, drawing endoscopy closer to histology. Moreover, they can enhance the detection and characterization of IBD-related dysplasia. Given the persistent challenge of interobserver variability, a more standardized approach to endoscopy is warranted, and the integration of artificial intelligence (AI) holds promise for addressing this limitation. Additionally, although molecular endoscopy is still in its infancy, it is a promising tool to forecast response to therapy. This review provides an overview of advanced endoscopic techniques, including dye-based and dye-less chromoendoscopy, and in vivo histological examinations with probe-based confocal laser endomicroscopy and endocytoscopy. The remarkable contribution of these tools to IBD management, especially when integrated with AI, is discussed. Specific attention is given to their role in improving disease assessment, detection, and characterization of IBD-associated lesions, and predicting disease-related outcomes.

Preparing Well for Esophageal Endoscopic Detection Using a Hybrid Model and Transfer Learning

Early detection of esophageal cancer through endoscopic imaging is pivotal for effective treatment. However, the intricacies of endoscopic diagnosis, contingent on the physician's expertise, pose challenges. Esophageal cancer features often manifest ambiguously, leading to potential confusions with other inflammatory esophageal conditions, thereby complicating diagnostic accuracy. In recent times, computer-aided diagnosis has emerged as a promising solution in medical imaging, particularly within the domain of endoscopy. Nonetheless, contemporary AI-based diagnostic models heavily rely on voluminous data sources, limiting their applicability, especially in scenarios with scarce datasets. To address this limitation, our study introduces novel data training strategies based on transfer learning, tailored to optimize performance with limited data. Additionally, we propose a hybrid model integrating EfficientNet and Vision Transformer networks to enhance prediction accuracy. Conducting rigorous evaluations on a carefully curated dataset comprising 1002 endoscopic images (comprising 650 white-light images and 352 narrow-band images), our model achieved exceptional outcomes. Our combined model achieved an accuracy of 96.32%, precision of 96.44%, recall of 95.70%, and f1-score of 96.04%, surpassing state-of-the-art models and individual components, substantiating its potential for precise medical image classification. The AI-based medical image prediction platform presents several advantageous characteristics, encompassing superior prediction accuracy, a compact model size, and adaptability to low-data scenarios. This research heralds a significant stride in the advancement of computer-aided endoscopic imaging for improved esophageal cancer diagnosis.

Endoscopic advances in the management of gastric cancer and premalignant gastric conditions

Gastric cancer is the fifth most common cancer and in 2018, it was the third most common cause of cancer-related deaths worldwide. Endoscopic advances continue to be made for the diagnosis and management of both early gastric cancer and premalignant gastric conditions. In this review, we discuss the epidemiology and risk factors of gastric cancer and emphasize the differences in early vs late-stage gastric cancer outcomes. We then discuss endoscopic advances in the diagnosis of early gastric cancer and premalignant gastric lesions. This includes the implementation of different imaging modalities such as narrow-band imaging, chromoendoscopy, confocal laser endomicroscopy, and other experimental techniques. We also discuss the use of endoscopic ultrasound in the diagnosis and staging of early gastric cancer. We then discuss the endoscopic advances made in the treatment of these conditions, including endoscopic mucosal resection, endoscopic submucosal dissection, and hybrid techniques such as laparoscopic endoscopic cooperative surgery. Finally, we comment on the current suggested recommendations for surveillance of both gastric cancer and its premalignant conditions.

Hereditary polyposis syndromes remain a challenging disease entity: Old dilemmas and new insights

In this editorial we present an overview and insights of the management of hereditary polyposis syndromes. The primary focus was on familial adenomatous polyposis, juvenile polyposis syndrome and Peutz-Jegher syndrome. Genetic testing has become increasingly available and is easier than ever to integrate into clinical practice. Furthermore, several genes have been added to the expanding list of genes associated with hereditary polyposis syndromes, allowing for precise diagnostics and tailored follow-up. Endoscopic evaluation of patients with hereditary polyposis syndromes is paramount in the surveillance strategies. Current endoscopic procedures include both diagnostic procedures and surveillance as well as therapeutic interventions. Recommendations for endoscopic procedures in the upper and lower gastrointestinal canal were described. Surgery is still a key component in the management of patients with hereditary polyposis syndromes. The increased cancer risk in these patients often render prophylactic procedures or intended curative procedures in the case of cancer development. Surgical interventions in the upper and lower gastrointestinal canal were described with relevant considerations. Development of chemopreventive medications is ongoing. Few drugs have been investigated, including nonsteroidal anti-inflammatory drugs. It has been demonstrated that cyclooxygenase-2 inhibitors may lower the number of polyps. Other medications are currently under investigation, but none have, to date, consistently been able to prevent development of disease.

Diagnostic value of artificial intelligence-assisted endoscopy for chronic atrophic gastritis: a systematic review and meta-analysis

Background and aims

The diagnosis of chronic atrophic gastritis (CAG) under normal white-light endoscopy depends on the endoscopist's experience and is not ideal. Artificial intelligence (AI) is increasingly used to diagnose diseases with good results. This review aimed to evaluate the accuracy of AI-assisted diagnosis of CAG through a meta-analysis.

Methods

We conducted a comprehensive literature search of four databases: PubMed, Embase, Web of Science, and the Cochrane Library. Studies published by November 21, 2022, on AI diagnosis CAG with endoscopic images or videos were included. We assessed the diagnostic performance of AI using meta-analysis, explored the sources of heterogeneity through subgroup analysis and meta-regression, and compared the accuracy of AI and endoscopists in diagnosing CAG.

Results

Eight studies that included a total of 25,216 patients of interest, 84,678 image training set images, and 10,937 test set images/videos were included. The results of the meta-analysis showed that the sensitivity of AI in identifying CAG was 94% (95% confidence interval [CI]: 0.88-0.97, I² = 96.2%), the specificity was 96% (95% CI: 0.88-0.98, I² = 98.04%), and the area under the summary receiver operating characteristic curve was 0.98 (95% CI: 0.96-0.99). The accuracy of AI in diagnosing CAG was significantly higher than that of endoscopists.

Conclusions

AI-assisted diagnosis of CAG in endoscopy has high accuracy and clinical diagnostic value.

Systematic review registration

http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42023391853.

Value of serum pepsinogen, gastrin, and cadherin-17 detection combined with narrowband imaging magnifying endoscopy in distinguishing early gastric cancer and precancerous lesions

BACKGROUND

The early screening of cancer has always been the focus of cancer research. As a common cancer in the world, gastric cancer has had a high incidence rate and mortality for many years, and canceration is difficult to detect. Current detection methods are not ideal for early detection of gastric cancer and precancerous lesions.

AIM

To assess the value of detection of serum pepsinogen (PG), gastrin (G-17), and hepato-intestinal cadherin-17 (CDH-17) combined with narrowband imaging magnifying endoscopy in the identification of early gastric cancer and precancerous lesions.

METHODS

The clinical data of patients with early gastric cancer (106 cases) and precancerous lesions (134 cases) at our hospital from January 2018 to January 2021 were retrospectively analyzed. All patients underwent narrow-band imaging magnifying endoscopy and detection of serum PG (including PGⅠ and PGⅡ), G-17, and CDH-17 levels. The sensitivity, specificity, and accuracy of PG, G-17, CDH-17, and narrow-band imaging magnifying endoscopy, alone and in combination, in the diagnosis of early gastric cancer and precancerous lesions were calculated.

RESULTS

In the early gastric cancer group, the incidence of lesions with border limits, mucosal microvascular irregularities, irregular surface ducts, and increased glandular spacing as detected by narrow-band imaging magnifying endoscopy was higher than that in the precancerous lesion group (P < 0.05). The diagnostic coincidence rates of magnifying endoscopy for early gastric cancer and precancerous lesions were 83.02% and 85.07%, respectively, and the difference was not statistically significant (P > 0.05). Serum PGⅠ level in the early gastric cancer group was lower than that of the precancerous lesion group, but there was no significant difference in serum PGⅡ between the two groups (P > 0.05); serum G-17 and CDH-17 levels were higher than those of the precancerous lesion group (P < 0.05). There was no statistically significant difference in the diagnostic coincidence rates of serum PGI, G-17, and CDH-17 alone (P > 0.05). The sensitivity, specificity and accuracy of serum PGⅠ, G-17, and CDH-17 combined with narrow-band imaging magnifying endoscopy in the diagnosis of early gastric cancer and precancerous lesions were higher than those of any serum index alone (PGⅠ/G-17/CDH-17) or narrowband imaging magnifying endoscopy alone (P < 0.05).

CONCLUSION

The detection of serum PG, G-17, and CDH-17 combined with narrow-band imaging magnifying endoscopy has high sensitivity, specificity, and accuracy in the differential diagnosis of early gastric cancer and precancerous lesions.

Adenoma detection rate using narrow-band imaging is inferior to high-definition white light colonoscopy in screening and surveillance colonoscopies in daily clinical care: A randomized controlled trial

BACKGROUND

Despite recent advances in endoscopic technology adenoma miss rate still is up to 20% contributing to interval cancers. Improved imaging modalities have been introduced to increase adenoma detection rate (ADR). Recently, narrow-band imaging (NBI) (Exera II series, Olympus Corporation) was not significantly better than high-definition white light colonoscopy (HD-WLC). An improved second generation of NBI (190-NBI) is characterized by better illumination of the bowel lumen and may be associated with a higher ADR.

METHODS

We performed a prospective randomized study on patients referred to the Jena University Hospital for screening or surveillance colonoscopy between January 2015 and April 2017. Participating endoscopists were divided into 2 subgroups depending on their individual experience. Colonoscopy was performed by use of HD-WLC or 190-NBI upon withdrawal.

RESULTS

Five hundred fifty-three patients participated in the study. Eighty patients were excluded (insufficient bowel cleansing [n = 34], anticoagulation precluding polypectomy [n=15], partial colonic resection [n=9], other reasons [n = 22]). Mean age was 66.9 years (standard deviation 10.3 years), and 253 patients were male (53.5%). Bowel preparation and withdrawal time were not different. ADR among all subgroups was 39.4% using HD-WLC, but only 29.1% were using 190-NBI (P = .02). Number of polyps per patient was lower using 190-NBI than with HD-WLC (0.58 vs 0.86; P = .02). Subgroup analysis revealed that 190-NBI was inferior to HD-WLC only in unexperienced endoscopists.

CONCLUSION

In our stud,y ADR was lower by use of 190-NBI. These differences persisted only in unexperienced investigators. 190-NBI seems to be more challenging regarding ADR, requiring more intensive training prior to implementing this technology in daily clinical care.

REGISTRATION

ClinicalTrials.gov (identifier: NCT03081975).

Polymeric nanocarriers: A promising tool for early diagnosis and efficient treatment of colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the third most prevalent type of cancer for incidence and second for mortality worldwide. Late diagnosis and inconvenient and expensive current diagnostic tools largely contribute to the progress of the disease. The use of chemotherapy in the management of CRC significantly reduces tumor growth, metastasis, and morbidity rates. However, poor solubility, low cellular uptake, nonspecific distribution, multiple drug resistance and unwanted adverse effects are still among the major drawbacks of chemotherapy that limit its clinical significance in the treatment of CRC. Owing to their remarkable advantages over conventional therapies, the use of nanotechnology-based delivery systems especially polymeric nanocarriers (PNCs) has revolutionized many fields including disease diagnosis and drug delivery.

AIM OF REVIEW

In this review, we shed the light on the current status of using PNCs in the diagnosis and treatment of CRC with a special focus on targeting strategies, surface modifications and safety concerns for different types of PNCs in colonic cancer delivery.

KEY SCIENTIFIC CONCEPTS OF REVIEW

The review explores the current progress on the use of PNCs in the diagnosis and treatment of CRC with a special focus on the role of PNCs in improvement of cellular uptake, drug targeting and co-delivery of chemotherapeutic agents. Possible toxicity and biocompatibility issues related to the use of PNCs and imitations and future recommendation for the use of those smart carriers in the diagnosis and treatment of CRC are also discussed.

Artificial intelligence and machine learning in colorectal cancer

Colorectal cancer (CRC) is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US. Artificial intelligence (AI) with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types. This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them. Furthermore, we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions. Considering the evolving transition to more personalized and tailored treatment strategies for CRC, the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes. We also discuss the constraints and limitations of the use of AI systems. While the medical profession remains enthusiastic about the future of AI and machine learning, large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used.

Endoscopic submucosal dissection for superficial gastrointestinal lesions: European Society of Gastrointestinal Endoscopy (ESGE) Guideline - Update 2022

ESGE recommends that the evaluation of superficial gastrointestinal (GI) lesions should be made by an experienced endoscopist, using high definition white-light and chromoendoscopy (virtual or dye-based).ESGE does not recommend routine performance of endoscopic ultrasonography (EUS), computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET)-CT prior to endoscopic resection.ESGE recommends endoscopic submucosal dissection (ESD) as the treatment of choice for most superficial esophageal squamous cell and superficial gastric lesions.For Barrett's esophagus (BE)-associated lesions, ESGE suggests the use of ESD for lesions suspicious of submucosal invasion (Paris type 0-Is, 0-IIc), for malignant lesions > 20 mm, and for lesions in scarred/fibrotic areas.ESGE does not recommend routine use of ESD for duodenal or small-bowel lesions.ESGE suggests that ESD should be considered for en bloc resection of colorectal (but particularly rectal) lesions with suspicion of limited submucosal invasion (demarcated depressed area with irregular surface pattern or a large protruding or bulky component, particularly if the lesions are larger than 20 mm) or for lesions that otherwise cannot be completely removed by snare-based techniques.ESGE recommends that an en bloc R0 resection of a superficial GI lesion with histology no more advanced than intramucosal cancer (no more than m2 in esophageal squamous cell carcinoma), well to moderately differentiated, with no lymphovascular invasion or ulceration, should be considered a very low risk (curative) resection, and no further staging procedure or treatment is generally recommended.ESGE recommends that the following should be considered to be a low risk (curative) resection and no further treatment is generally recommended: an en bloc R0 resection of a superficial GI lesion with superficial submucosal invasion (sm1), that is well to moderately differentiated, with no lymphovascular invasion, of size ≤ 20 mm for an esophageal squamous cell carcinoma or ≤ 30 mm for a stomach lesion or of any size for a BE-related or colorectal lesion, and with no lymphovascular invasion, and no budding grade 2 or 3 for colorectal lesions.ESGE recommends that, after an endoscopically complete resection, if there is a positive horizontal margin or if resection is piecemeal, but there is no submucosal invasion and no other high risk criteria are met, this should be considered a local-risk resection and endoscopic surveillance or re-treatment is recommended rather than surgery or other additional treatment.ESGE recommends that when there is a diagnosis of lymphovascular invasion, or deeper infiltration than sm1, or positive vertical margins, or undifferentiated tumor, or, for colorectal lesions, budding grade 2 or 3, this should be considered a high risk (noncurative) resection, and complete staging and strong consideration for additional treatments should be considered on an individual basis in a multidisciplinary discussion.ESGE recommends scheduled endoscopic surveillance with high definition white-light and chromoendoscopy (virtual or dye-based) with biopsies of only the suspicious areas after a curative ESD.

20-Year Trends in Detection Rates of Cardia Cancer via Endoscopic Surveillance in Tianjin, China: A Hospital-Based Study

Background

To analyze the time trends in cardia cancer detection rates using endoscopic surveillance from 1999 to 2019 in a high-volume Chinese hospital.

Methods

In this retrospective, single-center study, data were collected from the Endoscopy Center of the Department of Gastroenterology, Tianjin Medical University General Hospital, from 1999 to 2019. Cases of cardia cancer (n = 1567) were extracted from a database of patients who underwent endoscopy. Clinical and epidemiological characteristics of patients with cardia cancer were analyzed, including sex, age, and proportion of early gastric cancer and degree of tumor differentiation. The joinpoint regression method was used to identify change points in incidence trends. Annual percent change (APC) values, with 95% confidence intervals (CI), were calculated for time periods before and after change points.

Results

Of the 343942 patients who underwent endoscopy during 1999–2009, 1567 (4.6%) were identified with cardia cancer. The overall cardia adenocarcinoma detection rate decreased significantly from 1999 to 2004 (APC = −37.3, 95% CI: -20.9, -6.4), followed by a relatively slower decline rate from 2004 to 2019 (APC = −7.7, 95% CI: -4.4, -7.6). The crude rate of detection of early cardia cancer could not be determined by joinpoint analysis. Rates of detection reduced significantly in patients aged 60–69 and 70-79 years (APC = −8.3, 95% CI: -9.8, -6.8 and APC = −7.3, 95% CI: -8.8, -5.8, respectively). The detection rate in males decreased rapidly from 1999 to 2004 (APC = −35.9, 95% CI: -18.2, 5.6, P < 0.05), while the decline rate was relatively slow from 2005 to 2019 (APC = −6.9, 95% CI: -3.4, -6.1, P < 0.05). Among females, the detection rates also decreased from 1999 to 2004 (APC = −21.2, 95% CI: -28.1, -13.7), but remained stable from 2007 to 2019 (APC = −3.8, 95% CI: -7.9, -0.5). Detection of poorly differentiated cardia cancer also declined from 2009 to 2019 (APC = −12.8, 95% CI: -15.3, -10.0).

Conclusions

The detection rate of cardia cancer among gastric cancers has been stable from 2008 to 2019. The trend of detection rate of early cardia cancer showed no significant statistical meaning; hence, it remains necessary to carefully observe the cardia area during endoscopy examination.

Diagnosis of Esophageal Lesions by Multi-Classification and Segmentation Using an Improved Multi-Task Deep Learning Model

It is challenging for endoscopists to accurately detect esophageal lesions during gastrointestinal endoscopic screening due to visual similarities among different lesions in terms of shape, size, and texture among patients. Additionally, endoscopists are busy fighting esophageal lesions every day, hence the need to develop a computer-aided diagnostic tool to classify and segment the lesions at endoscopic images to reduce their burden. Therefore, we propose a multi-task classification and segmentation (MTCS) model, including the Esophageal Lesions Classification Network (ELCNet) and Esophageal Lesions Segmentation Network (ELSNet). The ELCNet was used to classify types of esophageal lesions, and the ELSNet was used to identify lesion regions. We created a dataset by collecting 805 esophageal images from 255 patients and 198 images from 64 patients to train and evaluate the MTCS model. Compared with other methods, the proposed not only achieved a high accuracy (93.43%) in classification but achieved a dice similarity coefficient (77.84%) in segmentation. In conclusion, the MTCS model can boost the performance of endoscopists in the detection of esophageal lesions as it can accurately multi-classify and segment the lesions and is a potential assistant for endoscopists to reduce the risk of oversight.

Hesitate between confocal laser endomicroscopy and narrow-band imaging: how to choose a better method in the detection of focal precancerous state of gastric cancer

BACKGROUND

With a high incidence globally, deaths form gastric cancer (GC) are not rare. Early diagnosis is crucial to ameliorate its prognosis. Confocal laser endomicroscopy (CLE) and narrow band imaging (NBI) have been extensively applied in gastroscopy, particularly when it comes to the detection and management of premalignant gastric lesion. Our meta-analysis intends to appraise the diagnostic capability and compare the efficacy of NBI and CLE for focal precancerous state of gastric cancer.

METHODS

We performed a literature search up to November 5, 2020 in online databases and major conferences. Two investigators assessed the methodological bias by QUADAS-2, followed by sophisticated study selection and data exaction to make a comparison between sensitivity, specificity, positive and negative likelihood values, and diagnostic odds ratio. A symmetric summary receiver-operating curve (sROC) and its area under the curve (AUC) were used to estimate threshold effect. Additionally, we evaluated the publication bias by Deeks' asymmetry test.

RESULTS AND CONCLUSIONS

Four studies involved 248 patients and 526 lesions. In analysis drawn from every lesion, the NBI's pooled sensitivity and specificity were 87% (95% CI: 0.80-0.92) and 85% (95% CI: 0.75-0.91), and those of CLE were 90% (95% CI: 0.85-0.91) and 87% (95% CI: 0.83-0.91). CLE illustrated that the pooled two were slightly higher than NBI when compared at the level of every lesion. The AUC for NBI and CLE was 0.92 (0.90-0.94) and 0.95 (0.92-0.96), and there might be a threshold effect, according to the shoulder-like distribution of scatter points in the sROC. We did not find obvious publication bias in our meta-analysis.

Multi-Task Model for Esophageal Lesion Analysis Using Endoscopic Images: Classification with Image Retrieval and Segmentation with Attention

The automatic analysis of endoscopic images to assist endoscopists in accurately identifying the types and locations of esophageal lesions remains a challenge. In this paper, we propose a novel multi-task deep learning model for automatic diagnosis, which does not simply replace the role of endoscopists in decision making, because endoscopists are expected to correct the false results predicted by the diagnosis system if more supporting information is provided. In order to help endoscopists improve the diagnosis accuracy in identifying the types of lesions, an image retrieval module is added in the classification task to provide an additional confidence level of the predicted types of esophageal lesions. In addition, a mutual attention module is added in the segmentation task to improve its performance in determining the locations of esophageal lesions. The proposed model is evaluated and compared with other deep learning models using a dataset of 1003 endoscopic images, including 290 esophageal cancer, 473 esophagitis, and 240 normal. The experimental results show the promising performance of our model with a high accuracy of 96.76% for the classification and a Dice coefficient of 82.47% for the segmentation. Consequently, the proposed multi-task deep learning model can be an effective tool to help endoscopists in judging esophageal lesions.

Near-focus narrow-band imaging classification of villous atrophy in suspected celiac disease: development and international validation

BACKGROUND AND AIMS

There are no agreed-on endoscopic signs for the diagnosis of villous atrophy (VA) in celiac disease (CD), necessitating biopsy sampling for diagnosis. Here we evaluated the role of near-focus narrow-band imaging (NF-NBI) for the assessment of villous architecture in suspected CD with the development and further validation of a novel NF-NBI classification.

METHODS

Patients with a clinical indication for duodenal biopsy sampling were prospectively recruited. Six paired NF white-light endoscopy (NF-WLE) and NF-NBI images with matched duodenal biopsy sampling including the bulb were obtained from each patient. Histopathology grading used the Marsh-Oberhuber classification. A modified Delphi process was performed on 498 images and video recordings by 3 endoscopists to define NF-NBI classifiers, resulting in a 3-descriptor classification: villous shape, vascularity, and crypt phenotype. Thirteen blinded endoscopists (5 expert, 8 nonexpert) then undertook a short training module on the proposed classification and evaluated paired NF-WLE-NF-NBI images.

RESULTS

One hundred consecutive patients were enrolled (97 completed the study; 66 women; mean age, 51.2 ± 17.3 years). Thirteen endoscopists evaluated 50 paired NF-WLE and NF-NBI images each (24 biopsy-proven VAs). Interobserver agreement among all validators for the diagnosis of villous morphology using the NF-NBI classification was substantial (κ = .71) and moderate (κ = .46) with NF-WLE. Substantial agreement was observed between all 3 NF-NBI classification descriptors and histology (weighted κ = 0.72-.75) compared with NF-WLE to histology (κ = .34). A higher degree of confidence using NF-NBI was observed when assessing the duodenal bulb.

CONCLUSIONS

We developed and validated a novel NF-NBI classification to reliably diagnose VA in suspected CD. There was utility for expert and nonexpert endoscopists alike, using readily available equipment and requiring minimal training. (Clinical trial registration number: NCT04349904.).

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.

Artificial intelligence in the diagnosis of gastric precancerous conditions by image-enhanced endoscopy: a multicenter, diagnostic study (with video)

BACKGROUND AND AIMS

Gastric precancerous conditions, including gastric atrophy (GA) and intestinal metaplasia (IM), play an important role in the development of gastric cancer. Image-enhanced endoscopy (IEE) shows great potential in diagnosing gastric precancerous conditions and adenocarcinoma. In this study, a deep convolutional neural network system, named ENDOANGEL, was constructed to detect gastric precancerous conditions by IEE.

METHODS

Endoscopic images were retrospectively obtained from 5 hospitals in China for the development, validation, and internal and external test of the system. Prospective consecutive patients receiving IEE were enrolled from January 13, 2020 to October 29, 2020 in Renmin Hospital of Wuhan University to assess in real time the applicability of the proposed computer-aided detection (CADe) system in clinical practice, and the performance of CADe was compared with that of endoscopists.

RESULTS

Six thousand two hundred fifty endoscopic images from 760 patients and 98 video clips from 77 individuals undergoing IEE were enrolled in this study. The diagnostic accuracy of GA was .901 (95% confidence interval [CI], .883-.917) in the internal test set, .864 (95% CI, .842-.884) in the multicenter external test set, and .878 (95% CI, .796-.935) in the prospective video test set. The diagnostic accuracy of IM was .908 (95% CI, .889-.924) in the internal test set, .859 (95% CI, .837-.880) in the multicenter external test set, and .898 (95% CI, .820-.950) in the prospective video test set. CADe achieved similar diagnostic accuracy to that of the experts for detecting GA (.869 [95% CI, .790-.927] vs .846 [95% CI, .808-.879], P = .396) and IM (.888 [95% CI, .812-.941] vs .820 [95% CI, .780-.855], P = .117) and was superior to that of nonexperts for GA (.750 [95% CI, .711-.786], P = .008) and IM (.736 [95% CI, .697-.773], P = .028).

CONCLUSIONS

CADe achieved high diagnostic accuracy in gastric precancerous conditions, which was similar to that of experts and superior to that of nonexperts. Thus, CADe provides possibilities for a wide application in assisting in the diagnosis of gastric precancerous conditions.

Endoscopic management of Barrett's esophagus: Western perspective of current status and future prospects

Barrett's esophagus (BE) is a precursor to esophageal adenocarcinoma and current practice is to establish endoscopic surveillance once diagnosed, in order to identify early dysplasia and neoplasia that has the potential to undergo endoscopic eradication therapy (EET). Before embarking upon EET the clinical team has a duty to consider all viable options and come to a plan based on recent evidence. The therapeutic approach varies greatly but largely adheres to the mantra of 'Detect-Resect-Ablate', in which high-quality endoscopy identifies BE associated pathology, associated lesions (if present) undergo safe endoscopic resection and remaining intestinal metaplasia in the esophagus is ablated to prevent recurrence of dysplasia. In this review, current practice, pitfalls, complications, and the future perspectives on practice in this field are discussed. The Western perspective is focused on here, with an outline of the differences in clinical practice with Asian nations and attempts to bridge these differences.

Crush cytology: an expeditious diagnostic tool for gastrointestinal tract malignancy

Background and study aims  Crush cytology is a simple and rapid method used for diagnosis of central nervous system lesions. We have evaluated the diagnostic accuracy of crush cytology for gastrointestinal tract lesions. Patients and methods  This was a prospective, cross-sectional, single center study, conducted on the patients who had suspected malignant lesions between August 2018 and March 2020. The crush cytologic diagnoses were correlated with histology to determine the diagnostic accuracy. Results  During the period of interest, a total of 451 patients (26.4 % esophagus & GE junction, 16.6 % stomach, 5.9 % ampulla & duodenum, and 50.9 % colorectal) had a suspected malignant lesion on endoscopic examination. Histology confirmed 92.9 % cases as malignant lesions and 7.1 % as nonmalignant. On crush cytology, 84.5 % were positive for malignancy, 8.9 % were negative for malignancy and 6.6 % were reported as suspicious for malignancy. The overall sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of crush cytology were 97.3 %, 90 %, 99.2 %, 72.5 % and 96.9 %, respectively. Conclusions  Crush cytology is a highly sensitive, specific, rapid and cost effective technique to diagnose gastrointestinal malignancies in endoscopically suspected malignant lesions. However, it cannot entirely substitute histopathological examination for definite tumor typing, grading, confirming invasion and in cases in which cytology is suspicious. Crush cytology is an added asset to the histology to maximize diagnostic accuracy and accelerating decision making for the management of lesions.

Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era

The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.

Macroscopic and microscopic imaging modalities for diagnosis and monitoring of urogenital schistosomiasis

Urogenital schistosomiasis remains a major global challenge. Optimal management of this infection depends upon imaging-based assessment of sequelae. Although established imaging modalities such as ultrasonography, plain radiography, magnetic resonance imaging (MRI), narrow band imaging, and computerized tomography (CT) have been used to determine tissue involvement by urogenital schistosomiasis, newer refinements in associated technologies may lead to improvements in patient care. Moreover, application of investigational imaging methods such as confocal laser endomicroscopy and two-photon microscopy in animal models of urogenital schistosomiasis are likely to contribute to our understanding of this infection's pathogenesis. This review discusses prior use of imaging in patients with urogenital schistosomiasis and experimentally infected animals, the advantages and limitations of these modalities, the latest radiologic developments relevant to this infection, and a proposed future diagnostic standard of care for management of afflicted patients.

WATS3D versus forceps biopsy in screening for Barrett's esophagus: experience in community endoscopy centers

Background

Barrett’s esophagus (BE) is a premalignant condition diagnosed using systematic 4-quadrant forceps biopsies (FB) during endoscopy. This method is fraught with errors due to the randomness of sampling and variability among operators. Wide-area transepithelial sampling with 3-dimensional computer-assisted analysis (WATS^3D) is an emerging technique used to collect esophageal samples. The aim of this study was to evaluate WATS^3D as a diagnostic tool for detecting BE in addition to FB, compared to FB alone.

Methods

A retrospective observational cohort study was conducted and included patients who underwent screening for BE with WATS^3D and FB between January 2015 and January 2019 across 3 endoscopy centers in Wichita, Kansas. The FB specimens were reviewed by community pathologists, while the WATS^3D samples were sent to CDX technology labs, NY.

Results

A total of 108 patients were screened for BE using both modalities concurrently. FB and WATS^3D detected 62 (57.4%) and 83 (76%) cases of BE, respectively. The absolute difference of 21 cases (18.6%) of BE was attributed to the addition of WATS^3D. The number needed to test with WATS^3D was 5. We divided the sample into 4 groups to compare the agreement across all groups: (FB–; WATS^3D+), (FB–; WATS^3D–), (FB+; WATS^3D+), and (FB+ and WATS^3D–). Overall agreement by kappa statistic was 0.74.

Conclusion

WATS^3D identified 21 cases of BE missed by FB. Using WATS^3D in addition to FB increased the yield of BE during surveillance endoscopy, with no increase in complications.

Narrow band imaging observed oral mucosa microvasculature as a tool to detect early oral cancer: an Indian experience

BACKGROUND

Narrow band imaging (NBI) is a novel method with the potential to improve the diagnostic capability of white-light.

METHODS

A prospective observational study of 50 consecutive patients, with suspicious malignant/premalignant lesions. White-light images were assessed as suspicious for malignancy/negative for malignancy, whereas NBI images were classified based on the IPCL patterns. All lesions underwent biopsy and accuracy was compared with the histopathology (Fig. 1). Fig. 1 Representative images of the IPCL patterns from Types I-IV RESULTS: 25 lesions (49%) were positive for malignancy, 2 (3.9%) lesions showed severe dysplasia, and 24(47%) were considered negative on histopathology. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of white light and NBI in detecting invasive carcinoma was 74.07%, 79.17%, 80.00%, 73.08% and 76.47%, and 92.67%, 90.16%, 92.56%, 91.67% and 92.16% respectively. The NBI group had a significantly better sensitivity and specificity to white light. The interobserver concordance was κ = 0.881.

CONCLUSION

NBI is a highly effective tool to detect invasive carcinomas amongst suspicious lesions of the oral cavity.

Diagnostic Accuracy of EndoFaster® and Narrow-Band Imaging Endoscopy in Patients with Impaired Gastric Acid Secretion: A Real-Time Prospective Study

BACKGROUND

EndoFaster® analyzes gastric juice in real time during gastroscopy allowing the detection of hypo-achlorhydric conditions, like corpus atrophic gastritis. Narrow-band imaging (NBI) endoscopy allows to accurately detect and perform target biopsies in areas of intestinal metaplasia, a histological change often associated to corpus atrophic gastritis.

AIMS

To compare the diagnostic accuracy of EndoFaster® with histological evaluation for corpus atrophic gastritis through high-resolution (HR) NBI targeted biopsies.

METHODS

Prospective study on consecutive adult patients undergoing gastroscopy between April and November 2018. Patients in therapy with proton pump inhibitors, previous gastric surgery, and/or known gastric neoplasia were excluded. At the beginning of gastroscopy, gastric juice was aspirated and analyzed by EndoFaster® in 15 seconds. Endoscopists were blinded to the report of EndoFaster®. Evaluation of gastric mucosa in HR-white light was firstly performed, then with HR-NBI allowing to perform targeted biopsies on areas suspected for intestinal metaplasia; otherwise, biopsies were performed according to the updated Sydney System protocol and sent for histopathological evaluation.

RESULTS

Overall, 124 patients were included [64% F; 56 (18-85) years]. Corpus atrophic gastritis was present in 41.9% of patients. EndoFaster® showed an accuracy for corpus atrophic gastritis diagnosis, compared to histopathological evaluation as gold standard, of 87.1% and a sensitivity, specificity, PPV, and NPV of 78.8%, 93.1%, 89.1%, and 85.9%, respectively. pH showed a positive correlation with the severity score of atrophy (r = 0.67, 95% CI: 0.73-0.81, and p < 0.0001). EndoFaster® allowed to diagnose corpus atrophic gastritis in 3.7% of patients negative to NBI (corpus atrophic gastritis without intestinal metaplasia).

CONCLUSION

EndoFaster® seems a promising tool to diagnose corpus atrophic gastritis. The evaluation of hypo-achlorhydria during gastroscopy can address bioptic sampling in corpus atrophic gastritis patients without intestinal metaplasia.

Image-enhanced endoscopy for gastric preneoplastic conditions and neoplastic lesions: a systematic review and meta-analysis

BACKGROUND : Image-enhanced endoscopy (IEE) improves the accuracy of endoscopic diagnosis. We aimed to assess the value of IEE for gastric preneoplastic conditions and neoplastic lesions. METHODS : Medline and Embase were searched until December 2018. Studies allowing calculation of diagnostic measures were included. Risk of bias and applicability were assessed using QUADAS-2. Subgroup analysis was performed to explore heterogeneity. RESULTS : 44 studies met the inclusion criteria. For gastric intestinal metaplasia (GIM), narrow-band imaging (NBI) obtained a pooled sensitivity and specificity of 0.79 (95 %CI 0.72-0.85) and 0.91 (95 %CI 0.88-0.94) on per-patient basis; on per-biopsy basis, it was 0.84 (95 %CI 0.81-0.86) and 0.95 (95 %CI 0.94-0.96), respectively. Tubulovillous pattern was the most accurate marker to detect GIM and it was effectively assessed without high magnification. For dysplasia, NBI showed a pooled sensitivity and specificity of 0.87 (95 %CI 0.84-0.89) and 0.97 (95 %CI 0.97-0.98) on per-biopsy basis. The use of magnification improved the performance of NBI to characterize early gastric cancer (EGC), especially when the vessel plus surface (VS) classification was applied. Regarding other technologies, trimodal imaging also obtained a high accuracy for dysplasia (sensitivity 0.93 [95 %CI 0.85-0.98], specificity 0.98 [95 %CI 0.92-1.00]). For atrophic gastritis, no specific pattern was noted and none of the technologies reached good diagnostic yield. CONCLUSION : NBI is highly accurate for GIM and dysplasia. The presence of tubulovillous pattern and the VS classification seem to be useful to detect GIM and characterize EGC, respectively. These features should be used in current practice and to standardize endoscopic criteria for other technologies.

Antibody-Targeted Imaging of Gastric Cancer

The specificity of antibodies for antigens overexpressed or uniquely expressed in tumor cells makes them ideal candidates in the development of bioconjugates for tumor imaging. Molecular imaging can aid clinicians in the diagnosis of gastric tumors and in selecting patients for therapies targeting receptors with a heterogeneous intratumoral or intertumoral expression. Antibodies labeled with an imaging radiometal can be used to detect primary tumors and metastases using whole-body positron emission tomography (PET) or single photon emission computed tomography (SPECT), both during diagnosis and monitoring disease response. Conjugated with fluorescent dyes, antibodies can image tumors by targeted optical imaging. This review provides an overview of the most recent advances in the use of antibodies labeled with radiometals or conjugated with fluorescent dyes for gastric cancer imaging.

Current Perspectives in Atrophic Gastritis

PURPOSE OF THE REVIEW

Atrophic gastritis is a complex syndrome with gastric atrophy as a common trait. Helicobacter pylori infection and autoimmunity are the two main contexts in which it develops. It is slightly symptomatic, affects various aspects of general health, and remains a predisposing factor for gastric cancer. This review will update current knowledge and progress on atrophic gastritis.

RECENT FINDINGS

Atrophic gastritis affects mostly adults with persistent dyspepsia, deficient anemia, autoimmunity disease, long-term proton pump inhibitor use, and a family history of gastric cancer. Gastric biopsies, expressed as Sydney system grade and OLGA/OLGIM classifications, represent the gold standard for diagnosis and cancer risk stage, respectively. Recently, electronic chromoendoscopy has allowed "targeted biopsies" of intestinal metaplasia. The associated hypochlorhydria affects the gastric microbiota composition suggesting that non-Helicobacter pylori microbiota may participate in the development of gastric cancer. Physicians should be aware of multifaceted clinical presentation of atrophic gastritis. It should be endoscopically monitored by targeted gastric biopsies. Autoimmune and Helicobacter pylori-induced atrophic gastritis are associated with different gastric microbial profiles playing different roles in gastric tumorigenesis.

Multicolor fluorescence imaging using a single RGB-IR CMOS sensor for cancer detection with smURFP-labeled probiotics

A multicolor fluorescence imaging device was recently developed for image-guided surgery. However, conventional systems are typically bulky and function with two cameras. To overcome these issues, we developed an economical home-built fluorescence imaging device based on a single RGB-IR sensor that can acquire both color and fluorescence images simultaneously. The technical feasibility of RGB-IR imaging was verified ex vivo in chicken breast tissue using fluorescein isothiocyanate (FITC), cyanine 5 (Cy5), and indocyanine green (ICG) as fluorescent agents. The minimum sensitivities for FITC, Cy5, and ICG were 0.200 µM, 0.130 µM, and 0.065 µM, respectively. In addition, we validated the fluorescence imaging of this device in vitro during a minimally invasive procedure using smURFP-labeled probiotics, which emit a spectrum similar to that of Cy5. Our preliminary study of the ex vivo tissue suggests that Cy5 and ICG are good candidates for deep tissue imaging. In addition, the tumor-specific amplification process was visualized using cancer cells incubated with probiotics that had been labeled with a fluorescent protein. Our approach indicates the potential for in vivo screening of tumors in rodent tumor models.

A current clinical overview of atrophic gastritis

Introduction: Atrophic gastritis (AG) is a complex syndrome which arises as a consequence of H. pylori infection or in the context of gastric autoimmunity. It often deserves a benign course but may lead to potentially life-threatening complications: cancer and anemia. This review aims to address traditional and innovative knowledge on this often under-diagnosed disorder.Areas covered: This review covers clinical presentation, risk factors, diagnosis, and management of AG and provides an updated resource for clinicians to get insight into this challenging disorder. Updated literature was searched in PubMed. Manual search from reference lists of publications was performed.Expert opinion: A case-finding strategy may be beneficial in individuals with anemia, dyspepsia, autoimmune thyropaties and type 1 diabetes, and family history of gastric cancer. AG is linked to gastric cancer risk and endoscopic surveillance is indicated according to topography of gastric atrophy and risk factors. The direction for future research in AG is summarized.

Chronic atrophic gastritis: Natural history, diagnosis and therapeutic management. A position paper by the Italian Society of Hospital Gastroenterologists and Digestive Endoscopists [AIGO], the Italian Society of Digestive Endoscopy [SIED], the Italian Society of Gastroenterology [SIGE], and the Italian Society of Internal Medicine [SIMI]

Chronic atrophic gastritis (CAG) is an underdiagnosed condition characterised by translational features going beyond the strict field of gastroenterology as it may manifest itself by a variable spectrum of gastric and extra-gastric symptoms and signs. It is relatively common among older adults in different parts of the world, but large variations exist. Helicobacter pylori-related CAG [multifocal] and autoimmune CAG (corpus-restricted) are apparently two different diseases, but they display overlapping features. Patients with cobalamin and/or iron deficiency anaemia or autoimmune disorders, including autoimmune thyroiditis and type 1 diabetes mellitus, should be offered screening for CAG. Pepsinogens, gastrin-17, and anti-H. pylori antibodies serum assays seem to be reliable non-invasive screening tools for the presence of CAG, helpful to identify individuals to refer to gastroscopy with five standard gastric biopsies in order to obtain histological confirmation of diagnosis. Patients with CAG are at increased risk of developing gastric cancer, and they should be estimated with histological staging systems (OLGA or OLGIM). H. pylori eradication may be beneficial by modifying the natural history of atrophy, but not that of intestinal metaplasia. Patients with advanced stages of CAG (Stage III/IV OLGA or OLGIM) should undergo endoscopic surveillance every three years, those with autoimmune CAG every three-five years. In patients with CAG, a screening for autoimmune thyroid disease and micronutrient deficiencies, including iron and vitamin B₁₂, should be performed. The optimal treatment for dyspeptic symptoms in patients with CAG remains to be defined. Proton pump inhibitors are not indicated in hypochlorhydric CAG patients.