Probe-based confocal laser endomicroscopy (pCLE), also known as optical biopsy, is a new endoscopic technique that provides real-time magnification of 1000 × microscopic tissue information to diagnose indeterminate biliary strictures. Tissue sampling by endoscopic retrograde cholangiopancreatography (ERCP) is routinely performed to evaluate indeterminate biliary strictures. To evaluate the accuracy of pCLE and tissue sampling by ERCP in the diagnosis of indeterminate biliary strictures, 18 articles were included from 2008 to 2021 through Embase, PubMed, Web of Science, and Cochrane library databases. The summary estimates for the pCLE diagnosis of indeterminate biliary strictures were: sensitivity 0.88 (95% confidence interval (CI), 0.84–0.91); specificity 0.79 (95% CI 0.74–0.83); and Diagnostic Odds Ratio (DOR) 24.63 (95% CI 15.76–38.48). The summary estimates for tissue sampling by ERCP diagnosis for indeterminate biliary strictures were: sensitivity 0.54 (95% CI 0.49–0.59); specificity 0.96 (95% CI 0.94–0.98); and DOR 11.31 (95% CI 3.90–32.82). The area under the sROC curve of pCLE diagnosis of indeterminate biliary strictures is 0.90 higher than 0.65 of tissue sampling by ERCP. The pCLE is a better approach than tissue sampling by ERCP for the diagnosis of indeterminate biliary strictures by providing real-time microscopic images of the bile ducts.
BackgroundCirculating tumor DNA (ctDNA) is an emerging biomarker for locally advanced rectal cancer (LARC), giving hope for stratified treatment. As the completed studies have small sample sizes and different experimental methods, systematic review and meta‐analysis were performed to explore their role in predicting pathological complete response (pCR), tumor recurrence, and prognosis.MethodsPubMed, Embase, and the Web of Science were searched for potentially eligible studies published up to September 6, 2022. Pooled relative risk (RR) was calculated to predict pCR and tumor recurrence, and pooled hazard ratio (HR) was calculated to evaluate the prognosis of overall survival (OS), recurrence‐free survival (RFS), and metastasis‐free survival (MRS).ResultsTwelve studies published between 2018 and 2022 included 931 patients, and 2544 serum samples were eventually included in the meta‐analysis. The pooled revealed that ctDNA‐negative patients were more likely to have a pCR (RR = 1.64, 95% confidence interval [CI]: 1.26–2.12). The pooled revealed that ctDNA‐positive patients were at high risk of recurrence (RR = 3.37, 95% CI: 2.34–4.85) and had a poorer prognosis for OS (HR = 3.03, 95% CI: 1.86–4.95), RFS (HR = 7.08, 95% CI: 4.12–12.14), and MRS (HR = 2.77, 95% CI: 2.01–3.83).ConclusionctDNA may be useful for stratifying treatment and assessing prognosis in patients with LARC, but its clinical application still needs to be confirmed in a prospective multicenter study with large samples.
Aim. As the completed studies have small sample sizes and different algorithms, a meta-analysis was conducted to assess the accuracy of WCE in identifying polyps using deep learning. Method. Two independent reviewers searched PubMed, Embase, the Web of Science, and the Cochrane Library for potentially eligible studies published up to December 8, 2021, which were analysed on a per-image basis. STATA RevMan and Meta-DiSc were used to conduct this meta-analysis. A random effects model was used, and a subgroup and regression analysis was performed to explore sources of heterogeneity. Results. Eight studies published between 2017 and 2021 included 819 patients, and 18,414 frames were eventually included in the meta-analysis. The summary estimates for the WCE in identifying polyps by deep learning were sensitivity 0.97 (95% confidence interval (CI), 0.95–0.98); specificity 0.97 (95% CI, 0.94–0.98); positive likelihood ratio 27.19 (95% CI, 15.32–50.42); negative likelihood ratio 0.03 (95% CI 0.02–0.05); diagnostic odds ratio 873.69 (95% CI, 387.34–1970.74); and the area under the sROC curve 0.99. Conclusion. WCE uses deep learning to identify polyps with high accuracy, but multicentre prospective randomized controlled studies are needed in the future.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.