Narrow-band Imaging Images Research Articles

Computer-aided endoscopic diagnostic system modified with hyperspectral imaging for the classification of esophageal neoplasms

IntroductionThe early detection of esophageal cancer is crucial to enhancing patient survival rates, and endoscopy remains the gold standard for identifying esophageal neoplasms. Despite this fact, accurately diagnosing superficial esophageal neoplasms poses a challenge, even for seasoned endoscopists. Recent advancements in computer-aided diagnostic systems, empowered by artificial intelligence (AI), have shown promising results in elevating the diagnostic precision for early-stage esophageal cancer.MethodsIn this study, we expanded upon traditional red–green–blue (RGB) imaging by integrating the YOLO neural network algorithm with hyperspectral imaging (HSI) to evaluate the diagnostic efficacy of this innovative AI system for superficial esophageal neoplasms. A total of 1836 endoscopic images were utilized for model training, which included 858 white-light imaging (WLI) and 978 narrow-band imaging (NBI) samples. These images were categorized into three groups, namely, normal esophagus, esophageal squamous dysplasia, and esophageal squamous cell carcinoma (SCC).ResultsAn additional set comprising 257 WLI and 267 NBI images served as the validation dataset to assess diagnostic accuracy. Within the RGB dataset, the diagnostic accuracies of the WLI and NBI systems for classifying images into normal, dysplasia, and SCC categories were 0.83 and 0.82, respectively. Conversely, the HSI dataset yielded higher diagnostic accuracies for the WLI and NBI systems, with scores of 0.90 and 0.89, respectively.ConclusionThe HSI dataset outperformed the RGB dataset, demonstrating an overall diagnostic accuracy improvement of 8%. Our findings underscored the advantageous impact of incorporating the HSI dataset in model training. Furthermore, the application of HSI in AI-driven image recognition algorithms significantly enhanced the diagnostic accuracy for early esophageal cancer.

Multi-Instance Learning for Vocal Fold Leukoplakia Diagnosis Using White Light and Narrow-Band Imaging: A Multicenter Study.

Vocal fold leukoplakia (VFL) is a precancerous lesion of laryngeal cancer, and its endoscopic diagnosis poses challenges. We aim to develop an artificial intelligence (AI) model using white light imaging (WLI) and narrow-band imaging (NBI) to distinguish benign from malignant VFL. A total of 7057 images from 426 patients were used for model development and internal validation. Additionally, 1617 images from two other hospitals were used for model external validation. Modeling learning based on WLI and NBI modalities was conducted using deep learning combined with a multi-instance learning approach (MIL). Furthermore, 50 prospectively collected videos were used to evaluate real-time model performance. A human-machine comparison involving 100 patients and 12 laryngologists assessed the real-world effectiveness of the model. The model achieved the highest area under the receiver operating characteristic curve (AUC) values of 0.868 and 0.884 in the internal and external validation sets, respectively. AUC in the video validation set was 0.825 (95% CI: 0.704-0.946). In the human-machine comparison, AI significantly improved AUC and accuracy for all laryngologists (p < 0.05). With the assistance of AI, the diagnostic abilities and consistency of all laryngologists improved. Our multicenter study developed an effective AI model using MIL and fusion of WLI and NBI images for VFL diagnosis, particularly aiding junior laryngologists. However, further optimization and validation are necessary to fully assess its potential impact in clinical settings. 3 Laryngoscope, 134:4321-4328, 2024.

Narrow band imaging-based radiogenomics for predicting radiosensitivity in nasopharyngeal carcinoma

ObjectivesThis study aims to assess the efficacy of narrow band imaging (NBI) endoscopy in utilizing radiomics for predicting radiosensitivity in nasopharyngeal carcinoma (NPC), and to explore the associated molecular mechanisms. MaterialsThe study included 57 NPC patients who were pathologically diagnosed and underwent RNA sequencing. They were categorized into complete response (CR) and partial response (PR) groups after receiving radical concurrent chemoradiotherapy. We analyzed 267 NBI images using ResNet50 for feature extraction, obtaining 2048 radiomic features per image. Using Python for deep learning and least absolute shrinkage and selection operator for feature selection, we identified differentially expressed genes associated with radiomic features. Subsequently, we conducted enrichment analysis on these genes and validated their roles in the tumor immune microenvironment through single-cell RNA sequencing. ResultsAfter feature selection, 54 radiomic features were obtained. The machine learning algorithm constructed from these features showed that the random forest algorithm had the highest average accuracy rate of 0.909 and an area under the curve of 0.961. Correlation analysis identified 30 differential genes most closely associated with the radiomic features. Enrichment and immune infiltration analysis indicated that tumor-associated macrophages are closely related to treatment responses. Three key NBI differentially expressed immune genes (NBI-DEIGs), namely CCL8, SLC11A1, and PTGS2, were identified as regulators influencing treatment responses through macrophages. ConclusionNBI-based radiomics models introduce a novel and effective method for predicting radiosensitivity in NPC. The molecular mechanisms may involve the functional states of macrophages, as reflected by key regulatory genes.

Computer-aided diagnosis improves characterization of Barrett’s neoplasia by general endoscopists (with video)

Characterization of visible abnormalities in patients with Barrett's esophagus (BE) can be challenging, especially for inexperienced endoscopists. This results in suboptimal diagnostic accuracy and poor interobserver agreement. Computer-aided diagnosis (CADx) systems may assist endoscopists. We aimed to develop, validate, and benchmark a CADx system for BE neoplasia. The CADx system received pretraining with ImageNet and then consecutive domain-specific pretraining with GastroNet, which includes 5 million endoscopic images. It was subsequently trained and internally validated using 1758 narrow-band imaging (NBI) images of early BE neoplasia (352 patients) and 1838 NBI images of nondysplastic BE (173 patients) from 8 international centers. CADx was tested prospectively on corresponding image and video test sets with 30 cases (20 patients) of BE neoplasia and 60 cases (31 patients) of nondysplastic BE. The test set was benchmarked by 44 general endoscopists in 2 phases (phase 1, no CADx assistance; phase 2, with CADx assistance). Ten international BE experts provided additional benchmark performance. Stand-alone sensitivity and specificity of the CADx system were 100% and 98% for images and 93% and 96% for videos, respectively. CADx outperformed general endoscopists without CADx assistance in terms of sensitivity (P= .04). Sensitivity and specificity of general endoscopists increased from 84% to 96% and 90% to 98% with CAD assistance (P< .001). CADx assistance increased endoscopists' confidence in characterization (P< .001). CADx performance was similar to that of the BE experts. CADx assistance significantly increased characterization performance of BE neoplasia by general endoscopists to the level of expert endoscopists. The use of this CADx system may thereby improve daily Barrett surveillance.

Assessment of Narrow-Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer: Part II, Detection and Classification of Esophageal Cancer

Esophageal carcinoma (EC) is a prominent contributor to cancer-related mortality since it lacks discernible features in its first phases. Multiple studies have shown that narrow-band imaging (NBI) has superior accuracy, sensitivity, and specificity in detecting EC compared to white light imaging (WLI). Thus, this study innovatively employs a color space linked to décor to transform WLIs into NBIs, offering a novel approach to enhance the detection capabilities of EC in its early stages. In this study a total of 3415 WLI along with the corresponding 3415 simulated NBI images were used for analysis combined with the YOLOv5 algorithm to train the WLI images and the NBI images individually showcasing the adaptability of advanced object detection techniques in the context of medical image analysis. The evaluation of the model’s performance was based on the produced confusion matrix and five key metrics: precision, recall, specificity, accuracy, and F1-score of the trained model. The model underwent training to accurately identify three specific manifestations of EC, namely dysplasia, squamous cell carcinoma (SCC), and polyps demonstrates a nuanced and targeted analysis, addressing diverse aspects of EC pathology for a more comprehensive understanding. The NBI model effectively enhanced both its recall and accuracy rates in detecting dysplasia cancer, a pre-cancerous stage that might improve the overall five-year survival rate. Conversely, the SCC category decreased its accuracy and recall rate, although the NBI and WLI models performed similarly in recognizing the polyp. The NBI model demonstrated an accuracy of 0.60, 0.81, and 0.66 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it attained a recall rate of 0.40, 0.73, and 0.76 in the same categories. The WLI model demonstrated an accuracy of 0.56, 0.99, and 0.65 in the dysplasia, SCC, and polyp categories, respectively. Additionally, it obtained a recall rate of 0.39, 0.86, and 0.78 in the same categories, respectively. The limited number of training photos is the reason for the suboptimal performance of the NBI model which can be improved by increasing the dataset.

Advantage of magnifying narrow-band imaging for the diagnosis of colorectal neoplasia associated with sessile serrated lesions.

This study aimed to extract endoscopic findings for diagnosing colorectal neoplasia associated with sessile serrated lesions (SSLs), which are of significant interest. To compare the magnifying narrow-band imaging (NBI) findings with microscopic morphology, we classified SSLs into two groups: Group A SSLs included the majority of uniform SSLs and any dysplasia other than that classified as group B SSLs. Group B SSLs included SSLs with intramucosal and invasive carcinoma. We also quantitatively assessed visible vessels using ImageJ software. This study included 47 patients with 50 group B SSLs who underwent endoscopic resection between 2012 and 2020. The results were retrospectively compared with those of 237 patients with 311 group A SSLs that underwent endoscopic resection. Using conventional white-light endoscopy, significantly more group B SSLs had uneven shapes and some reddening compared to group A SSLs. The diagnostic odds ratios for group B SSLs were as follows: lesions with a diameter ≥10 mm, 9.76; uneven shape, 3.79; reddening, 15.46; and visible vessels with NBI, 11.32. Regarding visible vessels with NBI, the specificity and diagnostic accuracy for group B SSLs were 94.9% and 93.1%, respectively. The percentage of the vascular tonal area of NBI images was significantly larger for group B SSLs than for group A SSLs (3.97% vs. 0.29%; p < 0.01). SSLs with reddening and/or a diameter ≥10 mm are suspected to contain cancerous components. Moreover, visible vessels observed using magnifying NBI can serve as objective indicators for diagnosing SSLs with cancerous components with a high degree of accuracy.

First report from the International Evaluation of Endoscopic classification Japan NBI Expert Team: International multicenter web trial.

Narrow-band imaging (NBI) contributes to real-time optical diagnosis and classification of colorectal lesions. The Japan NBI Expert Team (JNET) was introduced in 2011. The aim of this study was to explore the diagnostic accuracy of JNET when applied by European and Japanese endoscopists not familiar with this classification. This study was conducted by 36 European Society of Gastrointestinal Endoscopy (ESGE) and 49 Japan Gastroenterological Endoscopy Society (JGES) non-JNET endoscopists using still images of 150 lesions. For each lesion, nonmagnified white-light, nonmagnified NBI, and magnified NBI images were presented. In the magnified NBI, the evaluation area was designated by region of interest (ROI). The endoscopists scored histological prediction for each lesion. In ESGE members, the sensitivity, specificity, and accuracy were respectively 73.3%, 94.7%, and 93.0% for JNET Type 1; 53.0%, 64.9%, and 62.1% for Type 2A; 43.9%, 67.7%, and 55.1% for Type 2B; and 38.1%, 93.7%, and 85.1% for Type 3. When Type 2B and 3 were considered as one category of cancer, the sensitivity, specificity, and accuracy for differentiating high-grade dysplasia and cancer from the others were 59.9%, 72.5%, and 63.8%, respectively. These trends were the same for JGES endoscopists. The diagnostic accuracy of the JNET classification was similar between ESGE and JGES and considered to be sufficient for JNET Type 1. On the other hand, the accuracy for Types 2 and 3 is not sufficient; however, JNET 2B lesions should be resected en bloc due to the risk of cancers and JNET 3 can be treated by surgery due to its high specificity.

Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data

BackgroundLaryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural network-based Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPC in both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.MethodsAll 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance of the LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS.ResultsLPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965–0·987. In the laryngologist-machine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications.ConclusionsLPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists.

Assessment of Narrow Band Imaging Algorithm for Video Capsule Endoscopy Based on Decorrelated Color Space for Esophageal Cancer.

Video capsule endoscopy (VCE) is increasingly used to decrease discomfort among patients owing to its small size. However, VCE has a major drawback of not having narrow band imaging (NBI) functionality. The current VCE has the traditional white light imaging (WLI) only, which has poor performance in the computer-aided detection (CAD) of different types of cancer compared to NBI. Specific cancers, such as esophageal cancer (EC), do not exhibit any early biomarkers, making their early detection difficult. In most cases, the symptoms are unnoticeable, and EC is diagnosed only in later stages, making its 5-year survival rate below 20% on average. NBI filters provide particular wavelengths that increase the contrast and enhance certain features of the mucosa, thereby enabling early identification of EC. However, VCE does not have a slot for NBI functionality because its size cannot be increased. Hence, NBI image conversion from WLI can presently only be achieved in post-processing. In this study, a complete arithmetic assessment of the decorrelated color space was conducted to generate NBI images from WLI images for VCE of the esophagus. Three parameters, structural similarity index metric (SSIM), entropy, and peak-signal-to-noise ratio (PSNR), were used to assess the simulated NBI images. Results show the good performance of the NBI image reproduction method with SSIM, entropy difference, and PSNR values of 93.215%, 4.360, and 28.064 dB, respectively.

DESIGN AND ANALYSIS OF IODINE STAINING AND NBI IN THE DIAGNOSTIC MODEL AND DIAGNOSIS OF EARLY ESOPHAGEAL CANCER AND ITS PRECANCEROUS LESIONS

Objective: This study aimed to compare the differences between narrowband imaging (NBI) and Lugo’s iodine staining endoscopy (LCE) for detecting and elucidating the site, clarity, and diagnostic accuracy of early esophageal cancer and precancerous lesions. Methods: We included patients with a high risk of developing esophageal cancer who visited the Zhongjiang County People’s Hospital between October 2020 and October 2022. Endoscopic examination was performed on each study participant. White-light endoscopy was used to observe and locate the diseased mucosa, after which NBI mode and LCE staining were used to observe the boundary between the diseased and normal mucosa. Abnormal lesions were found, and biopsies were performed on the identifiable diseased parts for pathological examination. Inflammation, LGIN, HGIN, and early esophageal cancer were diagnosed, and the differences between them were compared in terms of the two enhanced-image endoscopy techniques. A semantic segmentation model based on deep learning was adopted to assist in the diagnosis of early esophageal cancer and accurately locate cancerous areas. In order to improve its accurate diagnostic rate, we also built a semantic segmentation network model to assist in the computer-aided diagnosis of early esophageal cancer. Results: A total of 69 cases of early esophageal cancer were included in this study. The patients were aged 40–75 years, with an average age of [Formula: see text] years. Most early esophageal cancer lesions were located in the middle of the esophagus in 45/69 (65.2%) cases. LCE obtained higher clarity of lesion boundaries than NBI (59.4% versus 45.0% and 27.7% versus 15.9%, respectively), and the proportion of unclear lesions was lower (8.6% versus 21.7% and 4.3% versus 17.4%, respectively; [Formula: see text]). For early esophageal cancer and precancerous lesions, the missed-diagnosis rate of white-light endoscopy was 20%, that of NBI was 3.15%, and that of LCE was 0%, with statistical significance ([Formula: see text]). The rate of missed diagnosis of LGIN (three cases) by NBI was 3.15%, which was not significantly different from that of LCE ([Formula: see text]). We found that the lesion area could be more accurately determined using deep learning models to segment NBI images. By constructing a deep learning model for the diagnosis and classification of esophageal cancer, its diagnostic rate rose to 99.5%. Conclusion: The age range of patients with early esophageal cancer in Zhongjiang County, Sichuan Province, was 40–75 years, and tumors mostly occurred in the middle of the esophagus. The boundary clarity of early esophageal carcinomas was higher on LCE than that on NBI. The diagnostic accuracies of NBI and LCE are much higher than those of conventional white-light endoscopy. Therefore, both LCE and NBI are helpful for detecting early esophageal cancer and precancerous lesions. Moreover, the diagnostic rate of clinical esophageal cancer can be effectively improved using a dedicated deep learning network model.

Benchmarking Polyp Segmentation Methods in Narrow-Band Imaging Colonoscopy Images.

In recent years, there has been significant progress in polyp segmentation in white-light imaging (WLI) colonoscopy images, particularly with methods based on deep learning (DL). However, little attention has been paid to the reliability of these methods in narrow-band imaging (NBI) data. NBI improves visibility of blood vessels and helps physicians observe complex polyps more easily than WLI, but NBI images often include polyps with small/flat appearances, background interference, and camouflage properties, making polyp segmentation a challenging task. This paper proposes a new polyp segmentation dataset (PS-NBI2K) consisting of 2,000 NBI colonoscopy images with pixel-wise annotations, and presents benchmarking results and analyses for 24 recently reported DL-based polyp segmentation methods on PS-NBI2K. The results show that existing methods struggle to locate polyps with smaller sizes and stronger interference, and that extracting both local and global features improves performance. There is also a trade-off between effectiveness and efficiency, and most methods cannot achieve the best results in both areas simultaneously. This work highlights potential directions for designing DL-based polyp segmentation methods in NBI colonoscopy images, and the release of PS-NBI2K aims to drive further development in this field.

The Detection of Nasopharyngeal Carcinomas Using a Neural Network Based on Nasopharyngoscopic Images.

To construct and validate a deep convolutional neural network (DCNN)-based artificial intelligence (AI) system for the detection of nasopharyngeal carcinoma (NPC) using archived nasopharyngoscopic images. We retrospectively collected 14107 nasopharyngoscopic images (7108 NPCs and 6999 noncancers) to construct a DCNN model and prepared a validation dataset containing 3501 images (1744 NPCs and 1757 noncancers) from a single center between January 2009 and December 2020. The DCNN model was established using the You Only Look Once (YOLOv5) architecture. Four otolaryngologists were asked to review the images of the validation set to benchmark the DCNN model performance. The DCNN model analyzed the 3501 images in 69.35 s. For the validation dataset, the precision, recall, accuracy, and F1 score of the DCNN model in the detection of NPCs on white light imaging (WLI) and narrow band imaging (NBI) were 0.845 ± 0.038, 0.942 ± 0.021, 0.920 ± 0.024, and 0.890 ± 0.045, and 0.895 ± 0.045, 0.941 ± 0.018, and 0.975 ± 0.013, 0.918 ± 0.036, respectively. The diagnostic outcome of the DCNN model on WLI and NBI images was significantly higher than that of two junior otolaryngologists (p < 0.05). The DCNN model showed better diagnostic outcomes for NPCs than those of junior otolaryngologists. Therefore, it could assist them in improving their diagnostic level and reducing missed diagnoses. 3 Laryngoscope, 134:127-135, 2024.

A trial deep learning-based model for four-class histologic classification of colonic tumor from narrow band imaging

Narrow band imaging (NBI) has been extensively utilized as a diagnostic tool for colorectal neoplastic lesions. This study aimed to develop a trial deep learning (DL) based four-class classification model for low-grade dysplasia (LGD); high-grade dysplasia or mucosal carcinoma (HGD); superficially invasive submucosal carcinoma (SMs) and deeply invasive submucosal carcinomas (SMd) and evaluate its potential as a diagnostic tool. We collected a total of 1,390 NBI images as the dataset, including 53 LGD, 120 HGD, 20 SMs and 17 SMd. A total of 598,801 patches were trimmed from the lesion and background. A patch-based classification model was built by employing a residual convolutional neural network (CNN) and validated by three-fold cross-validation. The patch-based validation accuracy was 0.876, 0.957, 0.907 and 0.929 in LGD, HGD, SMs and SMd, respectively. The image-level classification algorithm was derived from the patch-based mapping across the entire image domain, attaining accuracies of 0.983, 0.990, 0.964, and 0.992 in LGD, HGD, SMs, and SMd, respectively. Our CNN-based model demonstrated high performance for categorizing the histological grade of dysplasia as well as the depth of invasion in routine colonoscopy, suggesting a potential diagnostic tool with minimal human inputs.

A monocular SLAM system based on SIFT features for gastroscope tracking

During flexible gastroscopy, physicians have extreme difficulties to self-localize. Camera tracking method such as simultaneous localization and mapping (SLAM) has become a research hotspot in recent years, allowing tracking of the endoscope. However, most of the existing solutions have focused on tasks in which sufficient texture information is available, such as laparoscope tracking, and cannot be applied to gastroscope tracking since gastroscopic images have fewer textures than laparoscopic images. This paper proposes a new monocular SLAM framework based on scale-invariant feature transform (SIFT) and narrow-band imaging (NBI), which extracts SIFT features instead of oriented features from accelerated segment test (FAST) and rotated binary robust independent elementary features (BRIEF) features from gastroscopic NBI images, and performs feature retention based on the response sorting strategy for achieving more matches. Experimental results show that the root mean squared error of the proposed algorithm can reach a minimum of 2.074mm, and the pose accuracy can be improved by up to 25.73% compared with oriented FAST and rotated BRIEF (ORB)-SLAM. SIFT features and response sorting strategy can achieve more accurate matching in gastroscopic NBI images than other features and homogenization strategy, and the proposed algorithm can also run successfully on real clinical gastroscopic data. The proposed algorithm has the potential clinical value to assist physicians in locating the gastroscope during gastroscopy.

Artificial intelligence for detecting and delineating the extent of superficial esophageal squamous cell carcinoma and precancerous lesions under narrow-band imaging (with video)

Although narrow-band imaging (NBI) is a useful modality for detecting and delineating esophageal squamous cell carcinoma (ESCC), there is a risk of incorrectly determining the margins of some lesions even with NBI. This study aimed to develop an artificial intelligence (AI) system for detecting superficial ESCC and precancerous lesions and delineating the extent of lesions under NBI. Nonmagnified NBI images from 4 hospitals were collected and annotated. Internal and external image test datasets were used to evaluate the detection and delineation performance of the system. The delineation performance of the system was compared with that of endoscopists. Furthermore, the system was directly integrated into the endoscopy equipment, and its real-time diagnostic capability was prospectively estimated. The system was trained and tested using 10,047 still images and 140 videos from 1112 patients and 1183 lesions. In the image testing, the accuracy of the system in detecting lesions in internal and external tests was 92.4% and 89.9%, respectively. The accuracy of the system in delineating extents in internal and external tests was 88.9% and 87.0%, respectively. The delineation performance of the system was superior to that of junior endoscopists and similar to that of senior endoscopists. In the prospective clinical evaluation, the system exhibited satisfactory performance, with an accuracy of 91.4% in detecting lesions and an accuracy of 85.9% in delineating extents. The proposed AI system could accurately detect superficial ESCC and precancerous lesions and delineate the extent of lesions under NBI.

Hide and Seek: Dysplasia Camouflage in a Patient With Barrett’s Esophagus

Question: An 84-year-old man presenting with fatigue was referred for endoscopy owing to anemia. On his esophagogastroduodenoscopy, a segment of Barrett’s esophagus (BE) was seen (Prague classification C2M4). Random biopsies were obtained as per the Seattle protocol. Histology revealed at least one specimen harboring high-grade dysplasia. He had ascending colon adenocarcinoma on colonoscopy. The BE segment was subsequently reassessed using white light, narrow band imaging (NBI), acetic acid chromoendoscopy, and high magnification. In a proximal tongue, there was an area appeared to have an irregular microvasculature. This was presumed to be the dysplastic lesion (Figure A; NBI). A clear demarcation line was noted between the distal nondysplastic segment and the irregular microvasculature of the proximal dysplastic area (Figure B; NBI and near focus). A decision was made to initially resect the BE segment using the endoscopic submucosal dissection (ESD) technique and operate the right-sided colon cancer at the same season in the following weeks. He continued his proton pump inhibitor (PPI) in the interim. While reassessing the lesion before performing ESD 0 month later, the previously identified dysplastic lesion was no longer identified (Figure C; white light). What is the most likely explanation and what is the best way to manage the case? See the Gastroenterology web site () for more information on submitting your favorite image to Clinical Challenges and Images in GI. Further careful examination with NBI and magnification revealed an area of brownish discoloration below re-epithelialized mucosa (Figure D). The area to resect could not be ascertained, but the NBI and magnification images combined with the previously known location of the dysplastic area were used as landmarks to proceed with an ESD (Figure E, F). On histology, the dysplastic BE was buried underneath normal appearing squamous cell epithelium (Figure G; stain: hematoxylin and eosin). The dysplastic glandular proliferation showed focally fused glands formed by atypical, mitotically active epithelial cells without goblet cells. The dysplastic elements did not involve the muscularis mucosae nor the submucosa. This unique case demonstrates that re-epithelialization of a Barrett’s mucosa can occur within a short time interval. While managing BE, endoscopists should be aware of the acceleration of re-epithelization and the potential concealing of dysplastic cells under normal-appearing epithelial tissue such as presented here. Some evidence has shown that acid suppression can lead to this occurrence. A study of 26 patients examining the effect of high-dose lansoprazole on BE found that squamous islands were developed in 77% of the patients by the final visit.1Sampliner R.E. Effect of up to 3 years of high-dose lansoprazole on Barrett’s esophagus.Am J Gastroenterol. 1994; 89: 1844-1848PubMed Google Scholar In the study, squamous re-epithelization and subsequent burying of the dysplasia below the surface could be attributed to high-dose PPI use. It is, thus, paramount to carefully inspect the whole BE segment and beyond its obvious extent, especially in patients on a PPI. Likewise, this observation highlights the utmost importance of careful inspection and precise documentation of the location of dysplasia in BE during endoscopy. Applying advanced imaging techniques in addition to the white light may facilitate the visibility and improve the diagnostic and interventional precision.2Qumseya B. Sultan S. Bain P. et al.ASGE guideline on screening and surveillance of Barrett’s esophagus.GIE Journal. 2019; : 335-359Google Scholar

A comparative study of demarcation line diagnostic performance between non-magnifying observation with white light and non-magnifying observation with narrow-band light for early gastric cancer.

With the improvement in endoscopic equipment functions, narrow-band imaging (NBI) for endoscopic observation of the stomach, which is an organ with a large lumen, is now feasible. Studies evaluating the NBI utility without magnifying endoscopy to diagnose the invasion extent for the demarcation line identification in early gastric cancer have not been reported. This study aimed to investigate the demarcation line diagnostic performance of NBI in early gastric cancer compared to that of white-light imaging (WLI) using prospectively collected consecutive specimens from early gastric cancer patients. Thirty consecutive lesions were collected from patients who underwent endoscopic submucosal dissection for early gastric cancer. Next, 30 NBI and 30 WLI images, each with the same degree of gastric wall extension, angle, and layout for one lesion, were selected, and a total of 60 images were prepared for testing. The early gastric cancer invasion ranges in the endoscopic images was plotted using the web-developed software, and 264 independent endoscopists, unaware of the diagnosis, performed the web tests, with the concordance rates between the ranges of responses. After estimating the actual early gastric cancer invasion ranges, the NBI and WLI results were compared. The concordance rates for NBI and WLI images were 43.1% (95% confidence interval [CI] 42.5-43.7%) and 37.2% (95% CI 36.6-37.7%), respectively, showing that the concordance rate for NBI was significantly higher than that for WLI. This study suggested that NBI was more useful for identifying demarcation lines than WLI.

Real-Time Artificial Intelligence-Based Histologic Classifications of Colorectal Polyps Using Narrow-Band Imaging.

Background and AimsWith the development of artificial intelligence (AI), we have become capable of applying real-time computer-aided detection (CAD) in clinical practice. Our aim is to develop an AI-based CAD-N and optimize its diagnostic performance with narrow-band imaging (NBI) images.MethodsWe developed the CAD-N model with ResNeSt using NBI images for real-time assessment of the histopathology of colorectal polyps (type 1, hyperplastic or inflammatory polyps; type 2, adenomatous polyps, intramucosal cancer, or superficial submucosal invasive cancer; type 3, deep submucosal invasive cancer; and type 4, normal mucosa). We also collected 116 consecutive polyp videos to validate the accuracy of the CAD-N.ResultsA total of 10,573 images (7,032 images from 650 polyps and 3,541 normal mucous membrane images) from 478 patients were finally chosen for analysis. The sensitivity, specificity, PPV, NPV, and accuracy for each type of the CAD-N in the test set were 89.86%, 97.88%, 93.13%, 96.79%, and 95.93% for type 1; 93.91%, 95.49%, 91.80%, 96.69%, and 94.94% for type 2; 90.21%, 99.29%, 90.21%, 99.29%, and 98.68% for type 3; and 94.86%, 97.28%, 94.73%, 97.35%, and 96.45% for type 4, respectively. The overall accuracy was 93%. We also built models for polyps ≤5 mm, and the sensitivity, specificity, PPV, NPV, and accuracy for them were 96.81%, 94.08%, 95%, 95.97%, and 95.59%, respectively. Video validation results showed that the sensitivity, specificity, and accuracy of the CAD-N were 84.62%, 86.27%, and 85.34%, respectively.ConclusionsWe have developed real-time AI-based histologic classifications of colorectal polyps using NBI images with good accuracy, which may help in clinical management and documentation of optical histology results.

Narrow band imaging in early diagnosis of laryngopharyngeal malignant and premalignant lesions

Objectives: (1) To assess the diagnostic accuracy of narrow-band imaging (NBI) in the assessment of benign, premalignant, and malignant vocal fold lesions. (2) To evaluate the diagnostic value of NBI in detection of recurrence in upper aero-digestive tract malignancy.Methods: This was a prospective, observational study done between December 2018- November 2019 in the Department of Otolaryngology. One hundred six patients of suspected benign, premalignant, malignant lesions of larynx and hypopharynx along with recurrence cases of upper aerodigestive tract malignancy who have completed chemoradiation therapy. All patients were subjected to white light endoscopy (WLE), NBI, and biopsy for histopathological diagnosis. In WLE, laryngeal lesions were classified into three types: malignant, suspected malignant, and benign. NBI images were classified into 5 types based on the intrapapillary capillary loop (IPCL) patterns: Type I-IV were considered benign and Type V lesion was considered malignant. At the end, WLE and NBI findings were correlated with histopathological reports.Results: The diagnostic accuracy of NBI in diagnosing benign, premalignant, and malignant lesions is significantly better than WLE. The sensitivity of NBI in detecting malignant lesions is significantly greater than WLE. NBI has proved to be of great value in identifying lesions that could be missed by WLE alone, as combined sensitivity of NBI and WLE is significantly higher than WLE alone.Conclusion: NBI is an excellent technique for early detection of laryngopharyngeal malignancies with significantly higher sensitivity than WLE alone.

Deep Learning for nasopharyngeal Carcinoma Identification Using Both White Light and Narrow-Band Imaging Endoscopy.

To develop a deep-learning-based automatic diagnosis system for identifying nasopharyngeal carcinoma (NPC) from noncancer (inflammation and hyperplasia), using both white light imaging (WLI) and narrow-band imaging (NBI) nasopharyngoscopy images. Retrospective study. A total of 4,783 nasopharyngoscopy images (2,898 WLI and 1,885 NBI) of 671 patients were collected and a novel deep convolutional neural network (DCNN) framework was developed named Siamese deep convolutional neural network (S-DCNN), which can simultaneously utilize WLI and NBI images to improve the classification performance. To verify the effectiveness of combining the above-mentioned two modal images for prediction, we compared the proposed S-DCNN with two baseline models, namely DCNN-1 (only considering WLI images) and DCNN-2 (only considering NBI images). In the threefold cross-validation, an overall accuracy and area under the curve of the three DCNNs achieved 94.9% (95% confidence interval [CI] 93.3%-96.5%) and 0.986 (95% CI 0.982-0.992), 87.0% (95% CI 84.2%-89.7%) and 0.930 (95% CI 0.906-0.961), and 92.8% (95% CI 90.4%-95.3%) and 0.971 (95% CI 0.953-0.992), respectively. The accuracy of S-DCNN is significantly improved compared with DCNN-1 (P-value <.001) and DCNN-2 (P-value=.008). Using the deep-learning technology to automatically diagnose NPC under nasopharyngoscopy can provide valuable reference for NPC screening. Superior performance can be obtained by simultaneously utilizing the multimodal features of NBI image and WLI image of the same patient. 3 Laryngoscope, 132:999-1007, 2022.