Detection Of Esophageal Neoplasms Research Articles

Accuracy of artificial intelligence-assisted detection of esophageal cancer and neoplasms on endoscopic images: A systematic review and meta-analysis.

ObjectiveTo investigate systematically previous studies on the accuracy of artificial intelligence (AI)‐assisted diagnostic models in detecting esophageal neoplasms on endoscopic images so as to provide scientific evidence for the effectiveness of these models.MethodsA literature search was conducted on the PubMed, EMBASE and Cochrane Library databases for studies on the AI‐assisted detection of esophageal neoplasms on endoscopic images published up to December 2020. A bivariate mixed‐effects regression model was used to calculate the pooled diagnostic efficacy of AI‐assisted system. Subgroup analyses and meta‐regression analyses were performed to explore the sources of heterogeneity. The effectiveness of AI‐assisted models was also compared with that of the endoscopists.ResultsSixteen studies were included in the systematic review and meta‐analysis. The pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio and area under the summary receiver operating characteristic curve regarding AI‐assisted detection of esophageal neoplasms were 94% (95% confidence interval [CI] 92%‐96%), 85% (95% CI 73%‐92%), 6.40 (95% CI 3.38‐12.11), 0.06 (95% CI 0.04‐0.10), 98.88 (95% CI 39.45‐247.87) and 0.97 (95% CI 0.95‐0.98), respectively. AI‐based models performed better than endoscopists in terms of the pooled sensitivity (94% [95% CI 84%‐98%] vs 82% [95% CI 77%‐86%, P < 0.01).ConclusionsThe use of AI results in increased accuracy in detecting early esophageal cancer. However, most of the included studies have a retrospective study design, thus further validation with prospective trials is required.

Acetic Acid for the Detection of Esophageal Neoplasms

Acetic Acid for the Detection of Esophageal Neoplasms

Efficacy of narrow-band imaging in detecting early esophageal cancer and risk factors for its occurrence.

The aim of this study is to evaluate the efficacy of narrow-band imaging (NBI) in the detecting early esophageal cancer and precancerous lesions and to investigate the risk factors for its occurrence. The esophagus was examined with ordinary endoscopy, NBI, and iodine staining. All the lesions were confirmed by histopathologically as the gold standard; NBI and intrapapillary capillary scale (IPCL) scale were compared with pathologic diagnosis. The accuracy, sensitivity, specificity, positive and negative predictive values (PPV, NPV) were calculated. Subgroup analysis was performed between the elderly vs. younger group, and head and neck squamous cell cancer (HNSCC) vs. non-HNSCC patients. Ninety lesions were detected with ordinary endoscopy, 108 with NBI, and 120 with iodine staining. All esophageal cancers were detected both by NBI and by iodine staining. Accuracy, sensitivity, and specificity for esophageal cancer and precancerous lesion were 67.8%, 58.1%, and 76.6%; 92%, 89.7%, and 96%; 93.4%, 93.4%, and 93.2%, respectively. NBI endoscopy and iodine staining were superior to ordinary endoscopy for detecting esophageal cancer and precancerous lesions (p < 0.05). NBI showed better detection of esophageal neoplasms in the elderly patients (p < 0.001). The incidence of multiple squamous cell cancers (SCCs) was significantly higher in non-elderly group (p = 0.009). NBI can also detect more esophageal neoplastic lesions in patients with head and neck cancers (p = 0.003). NBI endoscopy appears as effective as Lugol staining to detect and screen the early esophageal cancer. NBI shows better detection of esophageal neoplasms in the elderly patients. The incidence of multiple SCCs was much higher in non-elderly patients.