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. 2023 Jan 10:12:973104.
doi: 10.3389/fonc.2022.973104. eCollection 2022.

Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis

Yan-Wei Jiang  1 , Xiong-Jei Xu  1 , Rui Wang  1 , Chun-Mei Chen  1
Affiliations

Efficacy of non-enhanced computer tomography-based radiomics for predicting hematoma expansion: A meta-analysis

Yan-Wei Jiang et al. Front Oncol. .

Abstract

Background: This meta-analysis aimed to assess the efficacy of radiomics using non-enhanced computed tomography (NCCT) for predicting hematoma expansion in patients with spontaneous intracerebral hemorrhage.

Methods: Throughout the inception of the project to April 11, 2022, a comprehensive search was conducted on PubMed, Embase, and Cochrane Central Register of Controlled Trials. The methodological quality of studies in this analysis was assessed by the radiomics quality scoring system (RQS). A meta-analysis of radiomic studies based on NCCT for predicting hematoma expansion in patients with intracerebral hemorrhage was performed. The efficacy of the radiomics approach and non-contrast CT markers was compared using network meta-analysis (NMA).

Results: Ten articles comprising a total of 1525 patients were quantitatively analyzed for hematoma expansion after cerebral hemorrhage using radiomics. Based on the included studies, the mean RQS was 14.4. The AUC value (95% confidence interval) of the radiomics model was 0.80 (0.76-0.83). Five articles comprising 846 patients were included in the NMA. The results synthesized according to Bayesian NMA revealed that the predictive ability of the radiomics model outperformed most of the NCCT biomarkers.

Conclusions: The NCCT-based radiomics approach has the potential to predict hematoma expansion. Compared to NCCT biomarkers, we recommend a radiomics approach. Standardization of the radiomics approach is required for further clinical implementation.

Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=324034, identifier [CRD42022324034].

Keywords: hematoma expansion; meta-analysis; non-enhanced computer tomography; radiomics; spontaneous intracerebral hemorrhage.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of (NCCT-based radiomics. NCCT: non-enhanced computed tomography.
Figure 2
Figure 2
Flowchart for study selection.
Figure 3
Figure 3
Forest plot of radiomics model. CIs: confidence intervals; DAR: diagnostic accuracy rate; NPV: negative predictive value; PPV: positive predictive value; Se: sensitivity; Sp: specificity.
Figure 4
Figure 4
Network plot and SUCRA of biomarkers for ranking the accuracy of diagnosis. (A) The network plot of Bayesian network meta-analysis; (B) sensitivity; (C) specificity; (D) positive predictive value; (E) negative predictive value; (F) diagnosis accuracy rate. SUCRA: Surface under the cumulative ranking curve.
Figure 5
Figure 5
Network forest plot for biomarkers compared with radiomics model. Crl: credible interval.
Figure 6
Figure 6
Examples of biomarkers on non-enhanced computed tomography (A) The blend sign is defined as an area with relatively low attenuation adjacent to an area with high attenuation (white arrow); (B) The black hole sign represents a low-density area within a hematoma with high density (black arrow); (C) The satellite sign represents a small lesion completely separate from the main hemorrhage visible in at least one slice (white triangle).
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