Diagnostic value of diffusion-weighted imaging/magnetic resonance imaging for peritoneal metastasis from malignant tumor: A systematic review and meta-analysis

Li Dong, Kuo Li, Taisong Peng, Li Dong, Kuo Li, Taisong Peng

Abstract

Background: Previous meta-analyses examined either multiple tools for the diagnosis of peritoneal metastases (PMs), but not diffusion-weighted imaging (DWI), or included only 1 tumor type. This study aimed to determine the summary diagnostic value of DWI/magnetic resonance imaging in determining PMs originating from various tumors.

Methods: PubMed, Embase, and Cochrane library were searched for available papers up to 2019/12. Pooled estimates for sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and accuracy were calculated using random-effects models.

Results: Ten studies were included and could be used to calculate the pooled sensitivity and specificity. The pooled sensitivity of DWI for PMs was 89% (95% confidence interval [CI]: 83%-93%). The pooled specificity was 86% (95% CI: 79%-91%). When considering only the retrospective studies, the pooled sensitivity of DWI for PMs was 85% (95% CI: 81%-89%). The pooled specificity was 84% (95% CI: 72%-92%). When considering only the studies about gastrointestinal tumors, the pooled sensitivity of DWI for PMs was 97% (95% CI: 68%-100%). The pooled specificity was 86% (95% CI: 69%-95%). No publication bias was observed (P = dd.27).

Conclusion: DWI magnetic resonance imaging is highly sensitive and specific for the detection of PMs from various abdominal cancers.

Conflict of interest statement

The authors have no conflicts of interest to disclose.

Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc.

Figures

Figure 1
Figure 1
Flowchart of the search process.
Figure 2
Figure 2
Forest plots of the pooled sensitivity and specificity analysis.
Figure 3
Figure 3
Summary ROC (SROC) curve of MRI for PM. MRI = magnetic resonance imaging, PM = peritoneal metastases.
Figure 4
Figure 4
. Forest plots of the pooled positive likelihood ratio and negative likelihood ratio analysis.
Figure 5
Figure 5
Forest plots of the pooled sensitivity and specificity analysis of prospective studies.
Figure 6
Figure 6
Summary ROC (SROC) curve of prospective studies.
Figure 7
Figure 7
Forest plots of the pooled sensitivity and specificity analysis of gastrointestinal tumors.
Figure 8
Figure 8
Summary ROC (SROC) curve of gastrointestinal tumors.
Figure 9
Figure 9
Deeks’ funnel plot with a regression line.

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Source: PubMed

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