Open AccessEfficacy of raman spectroscopy in the diagnosis of kidney cancer
Author(s) -
Hongyu Jin,
Xiao He,
Huaiyu Zhou,
Man Zhang,
Qingqing Tang,
Lede Lin,
Jihua Hao,
Rui Zeng
Publication year - 2020
Publication title -
medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.59
H-Index - 148
eISSN - 1536-5964
pISSN - 0025-7974
DOI - 10.1097/md.0000000000020933
Subject(s) - medicine , diagnostic odds ratio , cochrane library , meta analysis , confidence interval , odds ratio , receiver operating characteristic , likelihood ratios in diagnostic testing , medline , kidney cancer , cancer , area under the curve , political science , law
Objective: To comprehensively analyze the relative effectiveness of Raman spectroscopy (RS) in the diagnosis of suspected kidney cancer. Patients and methods: We performed a complete systematic review based on studies from PubMed/Medline, EMBASE, Web of Science, Ovid, Web of Knowledge, Cochrane Library and China National Knowledge Infrastructure. We identified 2413 spectra with strict criteria in 6 individual studies published between January 2008 and November 2018 in accordance to Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We summarized the test performance using random effects models. Results: General pooled diagnostic sensitivity and specificity of RS to kidney cancer were 0.96 (95% confidence interval [CI] 0.95–0.97) and 0.91 (95% CI 0.89–0.92). The pooled positive likelihood ratio (LR) was 9.57 (95% CI 5.73–15.46) while the negative LR was 0.04 (95% CI 0.02–0.11). The pooled diagnostic odds ratio was 238.06 (95% CI 77.79–728.54). The area under curve of summary receiver operator characteristics was 0.9466. Conclusion: Through this meta-analysis, we found a promisingly high sensitivity and specificity of RS in the diagnosis of suspected kidney masses and tumors. Other parameters like positive LR, negative LR, diagnostic odds ratio and area under curve of the summary receiver operator characteristics curve all helped to illustrate the high efficacy of RS in the diagnosis of kidney cancer.