Dataset Information

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

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.

SUBMITTER: Dong L

PROVIDER: S-EPMC7870229 | biostudies-literature | 2021 Feb

REPOSITORIES: biostudies-literature

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Publications

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

Dong Li L Li Kuo K Peng Taisong T

Medicine 20210201 5

<h4>Background</h4>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.<h4>Methods</h4>PubMed, Embase, and Cochrane library were searched for available papers up to 2019/12. Pooled estimates for sensitivity, specificity, positive l ...[more]

PMID: 33592867

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Project description:BackgroundThe apparent diffusion coefficient (ADC) is a highly diagnostic factor in discriminating malignant and benign breast masses in diffusion-weighted magnetic resonance imaging (DW-MRI). The combination of ADC and other pictorial characteristics has improved lesion type identification accuracy. The objective of this study was to reassess the findings on an independent patient group by changing the magnetic field from 1.5-Tesla to 3.0-Tesla.MethodsThis retrospective study consisted of a training group of 234 female patients, including 85 benign and 149 malignant lesions, imaged using 1.5-Tesla MRI, and a test group of 95 female patients, including 19 benign and 85 malignant lesions, imaged using 3.0-Tesla MRI. The lesion of interest was segmented from the raw image and four sets of measurements describing the morphology, kinetics, DW-MRI, and texture of the pictorial properties of each lesion were obtained. Each lesion was characterized by 28 features in total. Three classical machine-learning algorithms were used to build prediction models on the training group, which evaluated the prognostic performance of the multi-sided features in three scenarios. To reduce information redundancy, five highly diagnostic factors were selected to obtain a compact yet informative characterization of the lesion status.ResultsThree classification models were built on the training of 1.5-Tesla patients and were tested on the independent 3.0-Tesla test group. The following results were found. i) Characterization of breast masses in a multi-sided way dramatically increased prediction performance. The usage of all features gave a higher performance in both sensitivity and specificity than any individual feature groups or their combinations. ii) ADC was a highly effective factor in improving the sensitivity in discriminating malignant from benign masses. iii) Five features, namely ADC, Sum Average, Entropy, Elongation, and Sum Variance, were selected to achieve the highest performance in diagnosis of the 3.0-Tesla patient group.ConclusionsThe combination of ADC and other multi-sided characteristics can increase the capability of discriminating malignant and benign breast lesions, even under different imaging protocols. The selected compact feature subsets achieved a high diagnostic performance and thus are promising in clinical applications for discriminating lesion type and for personalized treatment planning.

Dataset Information

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

Background

Methods

Results

Conclusion

Publications

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

Similar Datasets

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