Ensembled CNN with artificial bee colony optimization method for esophageal cancer stage classification using SVM classifier.

BACKGROUND: Esophageal cancer (EC) is aggressive cancer with a high fatality rate and a rapid rise of the incidence globally. However, early diagnosis of EC remains a challenging task for clinicians. OBJECTIVE: To help address and overcome this challenge, this study aims to develop and test a new computer-aided diagnosis (CAD) network that combines several machine learning models and optimization methods to detect EC and classify cancer stages. METHODS: The study develops a new deep learning network for the classification of the various stages of EC and the premalignant stage, Barrett's Esophagus from endoscopic images. The proposed model uses a multi-convolution neural network (CNN) model combined with Xception, Mobilenetv2, GoogLeNet, and Darknet53 for feature extraction. The extracted features are blended and are then applied on to wrapper based Artificial Bee Colony (ABC) optimization technique to grade the most accurate and relevant attributes. A multi-class support vector machine (SVM) classifies the selected feature set into the various stages. A study dataset involving 523 Barrett's Esophagus images, 217 ESCC images and 288 EAC images is used to train the proposed network and test its classification performance. RESULTS: The proposed network combining Xception, mobilenetv2, GoogLeNet, and Darknet53 outperforms all the existing methods with an overall classification accuracy of 97.76% using a 3-fold cross-validation method. CONCLUSION: This study demonstrates that a new deep learning network that combines a multi-CNN model with ABC and a multi-SVM is more efficient than those with individual pre-trained networks for the EC analysis and stage classification.

Artificial intelligence in endoscopy: Overview, applications, and future directions.

Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.

Microarchitectures of Barrett's esophagus associated with DNA methylation status.

Aim: DNA methylation is involved in esophageal adenocarcinoma (EAC) and Barrett's esophagus (BE). Microarchitectures of on-neoplastic BE associated with DNA methylation status were examined using magnifying narrow-band imaging (NBI) endoscopy. Patients and methods: Using biopsies from non-neoplastic BE without cancer (n = 66; N group), with EAC (n = 27; ADJ group) and EAC tissue (n = 22; T group), methylation of N33, DPYS, SLC16A12, miR124a3 and miR34bc genes were quantified. Magnifying NBI features of non-neoplastic BE were classified according to their morphologies. Results: The ADJ and T groups presented higher DNA methylation compared with the N group. Magnifying NBI endoscopic features of non-neoplastic BE also correlated with DNA methylation as an independent factor. Conclusion: Microarchitectures of BE visualized by magnifying NBI endoscopy correlated with DNA methylation.

Diagnosis and management of barrett esophagus: european society of gastrointestinal endoscopy (ESGE) guideline

This Guideline is an official statement of the European Society of Gastrointestinal Endoscopy (ESGE). It is an update of the previous (2017) Position Statement on the endoscopic management of Barrett esophagus.

Barrett's oesophagus and stage 1 oesophageal adenocarcinoma: monitoring and management

This guideline covers monitoring, treatment and follow-up for people aged 18 and over with Barrett's oesophagus and stage 1 oesophageal adenocarcinoma. It includes advice on endoscopic and non-endoscopic techniques. It aims to improve outcomes by ensuring the most effective investigations and treatments are used.

Detection of Early Esophageal Neoplastic Barrett Lesions with Quantified Fluorescence Molecular Endoscopy Using Cetuximab-800CW.

Esophageal adenocarcinoma causes 6% of cancer-related deaths worldwide. Near-infrared fluorescence molecular endoscopy (NIR-FME) uses a tracer that targets overexpressed proteins. In this study, we aimed to investigate the feasibility of an epidermal growth factor receptor (EGFR)-targeted tracer, cetuximab-800CW, to improve detection of early-stage esophageal adenocarcinoma. Methods: We validated EGFR expression in 73 esophageal tissue sections. Subsequently, we topically administered cetuximab-800CW and performed high-definition white-light endoscopy (HD-WLE), narrow-band imaging, and NIR-FME in 15 patients with Barrett esophagus (BE). Intrinsic fluorescence values were quantified using multidiameter single-fiber reflectance and single-fiber fluorescence spectroscopy. Back-table imaging, histopathologic examination, and EGFR immunohistochemistry on biopsy samples collected during NIR-FME procedures were performed and compared with in vivo imaging results. Results: Immunohistochemical preanalysis showed high EGFR expression in 67% of dysplastic tissue sections. NIR-FME visualized all 12 HD-WLE-visible lesions and 5 HD-WLE-invisible dysplastic lesions, with increased fluorescence signal in visible dysplastic BE lesions compared with nondysplastic BE as shown by multidiameter single-fiber reflectance/single-fiber fluorescence, reflecting a target-to-background ratio of 1.5. Invisible dysplastic lesions also showed increased fluorescence, with a target-to-background ratio of 1.67. Immunohistochemistry analysis showed EGFR overexpression in 16 of 17 (94%) dysplastic BE lesions, which all showed fluorescence signal. Conclusion: This study has shown that NIR-FME using cetuximab-800CW can improve detection of dysplastic lesions missed by HD-WLE and narrow-band imaging.

Highlighting the Undetectable - Fluorescence Molecular Imaging in Gastrointestinal Endoscopy.

Flexible high-definition white-light endoscopy is the current gold standard in screening for cancer and its precursor lesions in the gastrointestinal tract. However, miss rates are high, especially in populations at high risk for developing gastrointestinal cancer (e.g., inflammatory bowel disease, Lynch syndrome, or Barrett's esophagus) where lesions tend to be flat and subtle. Fluorescence molecular endoscopy (FME) enables intraluminal visualization of (pre)malignant lesions based on specific biomolecular features rather than morphology by using fluorescently labeled molecular probes that bind to specific molecular targets. This strategy has the potential to serve as a valuable tool for the clinician to improve endoscopic lesion detection and real-time clinical decision-making. This narrative review presents an overview of recent advances in FME, focusing on probe development, techniques, and clinical evidence. Future perspectives will also be addressed, such as the use of FME in patient stratification for targeted therapies and potential alliances with artificial intelligence. KEY MESSAGES: • Fluorescence molecular endoscopy is a relatively new technology that enables safe and real-time endoscopic lesion visualization based on specific molecular features rather than on morphology, thereby adding a layer of information to endoscopy, like in PET-CT imaging. • Recently the transition from preclinical to clinical studies has been made, with promising results regarding enhancing detection of flat and subtle lesions in the colon and esophagus. However, clinical evidence needs to be strengthened by larger patient studies with stratified study designs. • In the future fluorescence molecular endoscopy could serve as a valuable tool in clinical workflows to improve detection in high-risk populations like patients with Barrett's esophagus, Lynch syndrome, and inflammatory bowel syndrome, where flat and subtle lesions tend to be malignant up to five times more often. • Fluorescence molecular endoscopy has the potential to assess therapy responsiveness in vivo for targeted therapies, thereby playing a role in personalizing medicine. • To further reduce high miss rates due to human and technical factors, joint application of artificial intelligence and fluorescence molecular endoscopy are likely to generate added value.

Artificial intelligence system for the detection of Barrett's esophagus.

BACKGROUND: Barrett's esophagus (BE), which has increased in prevalence worldwide, is a precursor for esophageal adenocarcinoma. Although there is a gap in the detection rates between endoscopic BE and histological BE in current research, we trained our artificial intelligence (AI) system with images of endoscopic BE and tested the system with images of histological BE. AIM: To assess whether an AI system can aid in the detection of BE in our setting. METHODS: Endoscopic narrow-band imaging (NBI) was collected from Chung Shan Medical University Hospital and Changhua Christian Hospital, resulting in 724 cases, with 86 patients having pathological results. Three senior endoscopists, who were instructing physicians of the Digestive Endoscopy Society of Taiwan, independently annotated the images in the development set to determine whether each image was classified as an endoscopic BE. The test set consisted of 160 endoscopic images of 86 cases with histological results. RESULTS: Six pre-trained models were compared, and EfficientNetV2B2 (accuracy [ACC]: 0.8) was selected as the backbone architecture for further evaluation due to better ACC results. In the final test, the AI system correctly identified 66 of 70 cases of BE and 85 of 90 cases without BE, resulting in an ACC of 94.37%. CONCLUSION: Our AI system, which was trained by NBI of endoscopic BE, can adequately predict endoscopic images of histological BE. The ACC, sensitivity, and specificity are 94.37%, 94.29%, and 94.44%, respectively.

Expanding beyond endoscopy: A review of non-invasive modalities in Barrett's esophagus screening and surveillance.

Barrett's esophagus (BE) is a condition that results from replacement of the damaged normal squamous esophageal mucosa to intestinal columnar mucosa and is the most significant predisposing factor for development of esophageal adenocarcinoma. Current guidelines recommend endoscopic evaluation for screening and surveillance based on various risk factors which has limitations such as invasiveness, availability of a trained specialist, patient logistics and cost. Trans-nasal endoscopy is a less invasive modality but still has similar limitations such as limited availability of trained specialist and costs. Non-endoscopic modalities, in comparison, require minimal intervention, can be done in an office visit and has the potential to be a more ideal choice for mass public screening and surveillance, particularly in patents at low risk for BE. These include newer generations of esophageal capsule endoscopy which provides direct visualization of BE, and tethered capsule endomicroscopy which can obtain high-resolution images of the esophagus. Various cell collection devices coupled with biomarkers have been used for BE screening. Cytosponge, in combination with TFF3, as well as EsophaCap and EsoCheck have shown promising results in various studies when used with various biomarkers. Other modalities including circulatory microRNAs and volatile organic compounds that have demonstrated favorable outcomes. Use of these cell collection methods for BE surveillance is a potential area of future research.

Artificial intelligence-enhanced volumetric laser endomicroscopy improves dysplasia detection in Barrett's esophagus in a randomized cross-over study.

Volumetric laser endomicroscopy (VLE) is an advanced endoscopic imaging tool that can improve dysplasia detection in Barrett's esophagus (BE). However, VLE scans generate 1200 cross-sectional images that can make interpretation difficult. The impact of a new VLE artificial intelligence algorithm called Intelligent Real-time Image Segmentation (IRIS) is not well-characterized. This is a randomized prospective cross-over study of BE patients undergoing endoscopy who were randomized to IRIS-enhanced or unenhanced VLE first followed by the other (IRIS-VLE vs. VLE-IRIS, respectively) at expert BE centers. The primary outcome was image interpretation time, which served as a surrogate measure for ease of interpretation. The secondary outcome was diagnostic yield of dysplasia for each imaging modality. 133 patients were enrolled. 67 patients were randomized to VLE-IRIS and 66 to IRIS-VLE. Total interpretation time did not differ significantly between groups (7.8 min VLE-IRIS vs. 7 min IRIS-VLE, P = 0.1), however unenhanced VLE interpretation time was significantly shorter in the IRIS-VLE group (2.4 min vs. 3.8 min, P < 0.01). When IRIS was used first, 100% of dysplastic areas were identified, compared with 76.9% when VLE was the first interpretation modality (P = 0.06). IRIS-enhanced VLE reduced the time of subsequent unenhanced VLE interpretation, suggesting heightened efficiency and improved dysplasia detection. It was also able to identify all endoscopically non-visible dysplastic areas.

Need for improvement in the evaluation of pre-malignant upper gastro-intestinal lesions in India: Results of a nationwide survey.

BACKGROUND AND AIM: Gastric and esophageal cancers are associated with high morbidity in India. In the absence of formal screening programs in India, it is essential that all elective esophago-gastro-duodenoscopies (EGDs), irrespective of indication, be also considered an opportunity to screen for premalignant lesions. With this premise, we tried to assess the adherence to best practices in the detection of premalignant upper gastro-intestinal lesions (PMUGIL) among endoscopists in India. We also evaluated the adequacy of training, availability of appropriate facilities, and differences between teaching and non-teaching centers. METHODS: We disbursed a survey among endoscopists working in India, through the membership database of the Society of Gastrointestinal Endoscopists of India, by email and instant messaging. The responses were collected and subsequently analyzed. RESULTS: We obtained a total of 422 eligible responses. The adherence to best practices assessed was lower than the set threshold in all except one parameter in both teaching centers and non-teaching centers. Only 58.5% of endoscopists had received training in the detection of PMUGIL. Appropriate image enhanced endoscopy (IEE) facilities were available to only 58.05% of surveyed endoscopists. CONCLUSIONS: Strategies to improve detection of PMUGIL should be directed at improving adherence to best practices, ensuring adequate training of endoscopists in the evaluation of PMUGIL and improving infrastructure.

A new artificial intelligence system successfully detects and localises early neoplasia in Barrett's esophagus by using convolutional neural networks.

BACKGROUND AND AIMS: Seattle protocol biopsies for Barrett's Esophagus (BE) surveillance are labour intensive with low compliance. Dysplasia detection rates vary, leading to missed lesions. This can potentially be offset with computer aided detection. We have developed convolutional neural networks (CNNs) to identify areas of dysplasia and where to target biopsy. METHODS: 119 Videos were collected in high-definition white light and optical chromoendoscopy with i-scan (Pentax Hoya, Japan) imaging in patients with dysplastic and non-dysplastic BE (NDBE). We trained an indirectly supervised CNN to classify images as dysplastic/non-dysplastic using whole video annotations to minimise selection bias and maximise accuracy. The CNN was trained using 148,936 video frames (31 dysplastic patients, 31 NDBE, two normal esophagus), validated on 25,161 images from 11 patient videos and tested on 264 iscan-1 images from 28 dysplastic and 16 NDBE patients which included expert delineations. To localise targeted biopsies/delineations, a second directly supervised CNN was generated based on expert delineations of 94 dysplastic images from 30 patients. This was tested on 86 i-scan one images from 28 dysplastic patients. FINDINGS: The indirectly supervised CNN achieved a per image sensitivity in the test set of 91%, specificity 79%, area under receiver operator curve of 93% to detect dysplasia. Per-lesion sensitivity was 100%. Mean assessment speed was 48 frames per second (fps). 97% of targeted biopsy predictions matched expert and histological assessment at 56 fps. The artificial intelligence system performed better than six endoscopists. INTERPRETATION: Our CNNs classify and localise dysplastic Barrett's Esophagus potentially supporting endoscopists during surveillance.

Advanced imaging and artificial intelligence for Barrett's esophagus: What we should and soon will do.

Barrett's esophagus (BE) is a well-established risk factor for esophageal adenocarcinoma. It is recommended that patients have regular endoscopic surveillance, with the ultimate goal of detecting early-stage neoplastic lesions before they can progress to invasive carcinoma. Detection of both dysplasia or early adenocarcinoma permits curative endoscopic treatments, and with this aim, thorough endoscopic assessment is crucial and improves outcomes. The burden of missed neoplasia in BE is still far from being negligible, likely due to inappropriate endoscopic surveillance. Over the last two decades, advanced imaging techniques, moving from traditional dye-spray chromoendoscopy to more practical virtual chromoendoscopy technologies, have been introduced with the aim to enhance neoplasia detection in BE. As witnessed in other fields, artificial intelligence (AI) has revolutionized the field of diagnostic endoscopy and is set to cover a pivotal role in BE as well. The aim of this commentary is to comprehensively summarize present evidence, recent research advances, and future perspectives regarding advanced imaging technology and AI in BE; the combination of computer-aided diagnosis to a widespread adoption of advanced imaging technologies is eagerly awaited. It will also provide a useful step-by-step approach for performing high-quality endoscopy in BE, in order to increase the diagnostic yield of endoscopy in clinical practice.

Detection of Barrett's neoplasia with a near-infrared fluorescent heterodimeric peptide.

BACKGROUND: Esophageal adenocarcinoma (EAC) is a molecularly heterogeneous disease with poor prognosis that is rising rapidly in incidence. We aimed to demonstrate specific binding by a peptide heterodimer to Barrett's neoplasia in human subjects. METHODS: Peptide monomers specific for EGFR and ErbB2 were arranged in a heterodimer configuration and labeled with IRDye800. This near-infrared (NIR) contrast agent was topically administered to patients with Barrett's esophagus (BE) undergoing either endoscopic therapy or surveillance. Fluorescence images were collected using a flexible fiber accessory passed through the instrument channel of an upper gastrointestinal endoscope. Fluorescence images were collected from 31 BE patients. A deep learning model was used to segment the target (T) and background (B) regions. RESULTS: The mean target-to-background (T/B) ratio was significantly greater for high grade dysplasia (HGD) and EAC versus BE, low grade dysplasia (LGD), and squamous epithelium. At a T/B ratio of 1.5, sensitivity and specificity of 94.1 % and 92.6 %, respectively, were achieved for the detection of Barrett's neoplasia with an area under the curve of 0.95. No adverse events attributed to the heterodimer were found. EGFR and ErbB2 expression were validated in the resected specimens. CONCLUSIONS: This "first-in-human" clinical study demonstrates the feasibility of detection of early Barrett's neoplasia using a NIR-labeled peptide heterodimer.

Esophageal OCT Imaging Using a Paddle Probe Externally Attached to Endoscope.

BACKGROUND AND AIMS: Endoscopic surveillance of Barrett's esophagus (BE) by white light examination is insufficient to diagnose dysplastic change. In this work, we describe an optical imaging method to obtain high-resolution cross-sectional imaging using a paddle-shaped probe affixed to the endoscope tip. METHODS: We integrated Optical Coherence Tomography (OCT), an optical imaging method that produces cross-sectional images, into a paddle probe attached to video endoscope. We acquired images of esophageal epithelium from patients undergoing routine upper GI endoscopy. Images were classified by a reviewer blinded to patient identity and condition, and these results were compared with clinical diagnosis. RESULTS: We successfully captured epithelial OCT images from 30 patients and identified features consistent with both squamous epithelium and Barrett's esophagus. Our blinded image reviewer classified BE versus non-BE with 91.5% accuracy (65/71 image regions), including sensitivity of 84.6% for BE (11/13) and a specificity of 93.1% (54/58). However, in 16 patients, intubation of the probe into the esophagus could not be achieved. CONCLUSIONS: A paddle probe is a feasible imaging format for acquiring cross-sectional OCT images from the esophagus and can provide a structural assessment of BE and non-BE tissue. Probe form factor is the current limiting obstacle, but could be addressed by further miniaturization.

Use of a Cytosponge biomarker panel to prioritise endoscopic Barrett's oesophagus surveillance: a cross-sectional study followed by a real-world prospective pilot.

BACKGROUND: Endoscopic surveillance is recommended for patients with Barrett's oesophagus because, although the progression risk is low, endoscopic intervention is highly effective for high-grade dysplasia and cancer. However, repeated endoscopy has associated harms and access has been limited during the COVID-19 pandemic. We aimed to evaluate the role of a non-endoscopic device (Cytosponge) coupled with laboratory biomarkers and clinical factors to prioritise endoscopy for Barrett's oesophagus. METHODS: We first conducted a retrospective, multicentre, cross-sectional study in patients older than 18 years who were having endoscopic surveillance for Barrett's oesophagus (with intestinal metaplasia confirmed by TFF3 and a minimum Barrett's segment length of 1 cm [circumferential or tongues by the Prague C and M criteria]). All patients had received the Cytosponge and confirmatory endoscopy during the BEST2 (ISRCTN12730505) and BEST3 (ISRCTN68382401) clinical trials, from July 7, 2011, to April 1, 2019 (UK Clinical Research Network Study Portfolio 9461). Participants were divided into training (n=557) and validation (n=334) cohorts to identify optimal risk groups. The biomarkers evaluated were overexpression of p53, cellular atypia, and 17 clinical demographic variables. Endoscopic biopsy diagnosis of high-grade dysplasia or cancer was the primary endpoint. Clinical feasibility of a decision tree for Cytosponge triage was evaluated in a real-world prospective cohort from Aug 27, 2020 (DELTA; ISRCTN91655550; n=223), in response to COVID-19 and the need to provide an alternative to endoscopic surveillance. FINDINGS: The prevalence of high-grade dysplasia or cancer determined by the current gold standard of endoscopic biopsy was 17% (92 of 557 patients) in the training cohort and 10% (35 of 344) in the validation cohort. From the new biomarker analysis, three risk groups were identified: high risk, defined as atypia or p53 overexpression or both on Cytosponge; moderate risk, defined by the presence of a clinical risk factor (age, sex, and segment length); and low risk, defined as Cytosponge-negative and no clinical risk factors. The risk of high-grade dysplasia or intramucosal cancer in the high-risk group was 52% (68 of 132 patients) in the training cohort and 41% (31 of 75) in the validation cohort, compared with 2% (five of 210) and 1% (two of 185) in the low-risk group, respectively. In the real-world setting, Cytosponge results prospectively identified 39 (17%) of 223 patients as high risk (atypia or p53 overexpression, or both) requiring endoscopy, among whom the positive predictive value was 31% (12 of 39 patients) for high-grade dysplasia or intramucosal cancer and 44% (17 of 39) for any grade of dysplasia. INTERPRETATION: Cytosponge atypia, p53 overexpression, and clinical risk factors (age, sex, and segment length) could be used to prioritise patients for endoscopy. Further investigation could validate their use in clinical practice and lead to a substantial reduction in endoscopy procedures compared with current surveillance pathways. FUNDING: Medical Research Council, Cancer Research UK, Innovate UK.

Targeted Hsp70 fluorescence molecular endoscopy detects dysplasia in Barrett's esophagus.

PURPOSE: The incidence of esophageal adenocarcinoma (EAC) has been increasing for decades without significant improvements in treatment. Barrett's esophagus (BE) is best established risk factor for EAC, but current surveillance with random biopsies cannot predict progression to cancer in most BE patients due to the low sensitivity and specificity of high-definition white light endoscopy. METHODS: Here, we evaluated the membrane-bound highly specific Hsp70-specific contrast agent Tumor-Penetrating Peptide (Hsp70-TPP) in guided fluorescence molecular endoscopy biopsy. RESULTS: Hsp70 was significantly overexpressed as determined by IHC in dysplasia and EAC compared with non-dysplastic BE in patient samples (n = 12) and in high-grade dysplastic lesions in a transgenic (L2-IL1b) mouse model of BE. In time-lapse microscopy, Hsp70-TPP was rapidly taken up and internalized  by human BE dysplastic patient-derived organoids. Flexible fluorescence endoscopy of the BE mouse model allowed a specific detection of Hsp70-TPP-Cy5.5 that corresponded closely with the degree of dysplasia but not BE. Ex vivo application of Hsp70-TPP-Cy5.5 to freshly resected whole human EAC specimens revealed a high (> 4) tumor-to-background ratio and a specific detection of previously undetected tumor infiltrations. CONCLUSION: In summary, these findings suggest that Hsp70-targeted imaging using fluorescently labeled TPP peptide may improve tumor surveillance in BE patients.