Li Zhang1, Zhiqian Min2, Min Tang2, Sipan Chen2, Xiaoyan Lei2, Xiaoling Zhang3. 1. The Department of MRI, Shaanxi Provincial People's hospital, Beilin District, Xi'an City 710000,Shaanxi Province, China. Electronic address: 18202990355@139.com. 2. The Department of MRI, Shaanxi Provincial People's hospital, Beilin District, Xi'an City 710000,Shaanxi Province, China. 3. The Department of MRI, Shaanxi Provincial People's hospital, Beilin District, Xi'an City 710000,Shaanxi Province, China. Electronic address: zxl.822@163.com.
Abstract
OBJECTIVE: The aim of this meta-analysis was to predict the grades of cerebral gliomas using quantitative apparent diffusion coefficient (ADC) values. MATERIALS AND METHODS: A comprehensive search of the PubMed, EMBASE, Web of Science, and Cochrane Library databases was performed up to 8, 2016. The quality assessment of diagnostic accuracy studies (QUADAS 2) was used to evaluate the quality of studies. Statistical analyses included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio' (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy values of the included studies using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.3), and Meta-Disc 1.4 software programs. RESULTS: Fifteen studies were analyzed and included a total of 821 patients and 821 lesions. In regards to the diagnostic accuracy of ADC maps, the pooled SEN, SPE, PLR, NLR, and DOR with 95%CIs were 0.82 [95%CI: 0.76, 0.87] and 0.75 [95%CI: 0.67, 0.81], 3.24 [95%CI: 2.48, 4.24], 0.24 [95%CI: 0.17, 0.33], and 13.60 [95%CI: 8.37, 22.07], respectively. The SROC curve showed an AUC of 0.85. Deeks testing confirmed no significant publication bias in all studies. CONCLUSION: Our findings indicate that quantitative ADC values have high accuracy in separating high-grade from low-grade cerebral gliomas. Further studies using a standardized methodology may help guide the use of ADC values for clinical decision-making.
OBJECTIVE: The aim of this meta-analysis was to predict the grades of cerebral gliomas using quantitative apparent diffusion coefficient (ADC) values. MATERIALS AND METHODS: A comprehensive search of the PubMed, EMBASE, Web of Science, and Cochrane Library databases was performed up to 8, 2016. The quality assessment of diagnostic accuracy studies (QUADAS 2) was used to evaluate the quality of studies. Statistical analyses included pooling of sensitivity and specificity, positive likelihood ratio (PLR), negative likelihood ratio' (NLR), diagnostic odds ratio (DOR), and diagnostic accuracy values of the included studies using the summary receiver operating characteristic (SROC). All analyses were conducted using STATA (version 12.0), RevMan (version 5.3), and Meta-Disc 1.4 software programs. RESULTS: Fifteen studies were analyzed and included a total of 821 patients and 821 lesions. In regards to the diagnostic accuracy of ADC maps, the pooled SEN, SPE, PLR, NLR, and DOR with 95%CIs were 0.82 [95%CI: 0.76, 0.87] and 0.75 [95%CI: 0.67, 0.81], 3.24 [95%CI: 2.48, 4.24], 0.24 [95%CI: 0.17, 0.33], and 13.60 [95%CI: 8.37, 22.07], respectively. The SROC curve showed an AUC of 0.85. Deeks testing confirmed no significant publication bias in all studies. CONCLUSION: Our findings indicate that quantitative ADC values have high accuracy in separating high-grade from low-grade cerebral gliomas. Further studies using a standardized methodology may help guide the use of ADC values for clinical decision-making.
Authors: Francisco R Maldonado; Juan P Princich; Lucia Micheletti; María S Toronchik; José I Erripa; Carlos Rugilo Journal: Pediatr Radiol Date: 2020-09-08
Authors: Jun Zhang; Hong Peng; Yu-Lin Wang; Hua-Feng Xiao; Yuan-Yuan Cui; Xiang-Bing Bian; De-Kang Zhang; Lin Ma Journal: Front Oncol Date: 2021-05-13 Impact factor: 6.244