Barrett's Adenocarcinoma of the Esophagus

Behrens, Angelika; Pech, Oliver; Graupe, F; May, Andrea; Lorenz, D.; Ell, Christian

doi:10.3238/arztebl.2011.0313

Cited by 17 publications

(4 citation statements)

References 44 publications

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“…Therefore, only one out of eight esophageal cancers is identified at an early stage (T1) [ 1 ]. Typical symptoms of esophageal carcinoma are dysphagia (reduction of esophagus lumen to 50%) [ 17 ], vomiting, loss of body weight, and gastrointestinal bleeding [ 18 ].…”

Section: Diagnosismentioning

confidence: 99%

Diagnostics and Early Diagnosis of Esophageal Cancer

Meves¹,

Behrens²,

Pohl³

2015

Visc Med

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In the esophagus two different kinds of primary neoplasias may arise: squamocellular carcinomas (SCC) and esophageal adenocarcinomas (EAC). Although both types of carcinoma are rare diseases, especially the incidence of EAC rose in the last years. The management of esophageal cancer is challenging. There are no specific symptoms of early esophageal cancers. Due to this fact, most of the esophageal cancers are found incidentally, and only 12.5% of esophageal tumors are endoscopically resectable. Gastroscopy is the gold standard for the diagnosis of esophageal cancer. The sensitivity of detecting early-stage carcinoma may be improved by adjunct techniques such as chromoendoscopy, virtual chromoendoscopy, magnification endoscopy, and other advanced endoscopic imaging techniques. The diagnosis of esophageal cancer can be verified with targeted biopsies. Accurate staging information is crucial for establishing appropriate treatment choices for esophageal cancer, while the depth of the tumor determines the feasibility of therapy. In terms of staging, endosonography, abdominal ultrasound, and computed tomography scan of the thorax and abdomen should thus be performed before initiation of therapy.

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Section: Diagnosismentioning

confidence: 99%

Diagnostics and Early Diagnosis of Esophageal Cancer

Meves¹,

Behrens²,

Pohl³

2015

Visc Med

Self Cite

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“…As a result, only one out of every eight esophageal tumors are detected at an early-stage (T1) 19 . Typical symptoms occur only after 50% of the lumen size has been reduced which clinically presents as progressive dysphagia, weight loss (typically ≥ 10 kgs in 1 month), vomiting, and hematemesis (hematochezia and melena) 20 . High-resolution white-light endoscopy reveals mucosal abnormalities during gastroscopy.…”

Section: The Current State Of the Diagnosis Of Esophageal Cancer And ...mentioning

confidence: 99%

Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma – a narrative review

Mohan,

Asghar,

Abid

et al. 2023

Annals of Medicine &Amp; Surgery

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Esophageal cancer is a major cause of cancer-related mortality worldwide, with significant regional disparities. Early detection of precursor lesions is essential to improve patient outcomes. Artificial intelligence (AI) techniques, including deep learning (DL) and machine learning (ML), have proved to be of assistance to both gastroenterologists and pathologists in the diagnosis and characterization of upper gastrointestinal malignancies by corelating with the histopathology. The primary diagnostic method in gastroenterology is white light endoscopic evaluation, but conventional endoscopy is partially inefficient in detecting esophageal cancer. However, other endoscopic modalities, such as narrow-band imaging, endocytoscopy, and endomicroscopy, have shown improved visualization of mucosal structures and vasculature that provides a set of baseline data to develop efficient AI-assisted predictive models for quick interpretation. The main challenges in managing esophageal cancer are identifying high-risk patients and the disease’s poor prognosis. Thus, AI techniques can play a vital role in improving the early detection and diagnosis of precursor lesions, assisting gastroenterologists in performing targeted biopsies and real-time decisions of endoscopic mucosal resection or endoscopic submucosal dissection. Combining AI techniques and endoscopic modalities can enhance the diagnosis and management of esophageal cancer, improving patient outcomes and reducing cancer-related mortality rates. The aim of this review is to grasp a better understanding of application of AI in diagnosis, treatment and prognosis of esophageal cancer and how Computer-Aided diagnosis (CADx) and Computer-Aided detection (CADe) can act as vital tools for clinicians in the long run.

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“…However, almost all patients with esophageal adenocarcinoma are diagnosed at the end of the disease, and their prognosis is poor [2]. Currently, most diagnoses of esophageal squamous epithelial cell carcinoma are made using white-light imaging (WLI) endoscopy, and if dysplastic tissue is detected early, it can be treated with endoscopic mucosal resection and radio-frequency ablation [3]. Therefore, early detection and diagnosis are important for the survival and prognosis of patients with esophageal cancer [4].…”

Section: Introductionmentioning

confidence: 99%

Early detection of esophageal cancer using multi-institutional data: An evaluation of AI detection algorithms based on narrowband and white-light imaging data

Kim,

Baik,

Lee

et al. 2024

Preprint

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Esophageal cancer is one of the most common cancers worldwide. In particular, esophageal squamous epithelial cell carcinoma is primarily diagnosed at the end stage and has a poor prognosis. For the early diagnosis and detection of esophageal cancer, this study aimed to develop an algorithm for detecting tumors in esophageal endoscopic images using innovative artificial intelligence (AI) technology. White-light images and narrowband image data collected at Gachon University Gil Hospital were used, and lesions were detected by applying the YOLOv5 and RetinaNet detection models. This study evaluated the performance of the model using precision, sensitivity, and the number of misdetections per image as evaluation indicators in a large-scale dataset. Additionally, the generalizability of the model was verified using external data collected from various institutions. The results showed that the AI model exhibited high precision and sensitivity in white-light and narrowband images by analyzing not only polyps but also superficial esophageal cancer, and the RetinaNet model exhibited excellent performance. These results are expected to contribute to a reduction in the misdiagnosis rate by enabling a more precise and rapid diagnosis. This study presents an effective method for detecting esophageal tumors through AI-based esophageal endoscopic image analysis, and it is expected to be helpful in investigating AI models that predict the depth of lesion invasion by incorporating real-time video-based detection and synthesis algorithms. These efforts are expected to contribute significantly to the effective diagnosis and treatment of esophageal cancer, thereby promoting the standardization of medical services.

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Barrett's Adenocarcinoma of the Esophagus

Cited by 17 publications

References 44 publications

Diagnostics and Early Diagnosis of Esophageal Cancer

Diagnostics and Early Diagnosis of Esophageal Cancer

Revolutionizing healthcare by use of artificial intelligence in esophageal carcinoma – a narrative review

Early detection of esophageal cancer using multi-institutional data: An evaluation of AI detection algorithms based on narrowband and white-light imaging data

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