Current status and quality of radiomics studies for predicting outcome in acute ischemic stroke patients: a systematic review and meta-analysis

Abstract

Background: Pre-treatment prediction of reperfusion and long-term prognosis in acute ischemic stroke (AIS) patients is crucial for effective treatment and decision-making. Recent studies have demonstrated that the inclusion of radiomics data can improve the performance of predictive models. This paper reviews published studies focused on radiomics-based prediction of reperfusion and long-term prognosis in AIS patients.

Methods: We systematically searched PubMed, Web of Science, and Cochrane databases up to September 9, 2023, for studies on radiomics-based prediction of AIS patient outcomes. The methodological quality of the included studies was evaluated using the phase classification criteria, the radiomics quality scoring (RQS) tool, and the Prediction model Risk Of Bias Assessment Tool (PROBAST). Two separate meta-analyses were performed of these studies that predict long-term prognosis and reperfusion in AIS patients.

Results: Sixteen studies with sample sizes ranging from 67 to 3,001 were identified. Ten studies were classified as phase II, and the remaining were categorized as phase 0 (n = 2), phase I (n = 1), and phase III (n = 3). The mean RQS score of all studies was 39.41%, ranging from 5.56 to 75%. Most studies (87.5%, 14/16) were at high risk of bias due to their retrospective design. The remaining two studies were categorized as low risk and unclear risk, respectively. The pooled area under the curve (AUC) was 0.88 [95% confidence interval (CI) 0.84-0.92] for predicting the long-term prognosis and 0.80 (95% CI 0.74-0.86) for predicting reperfusion in AIS.

Conclusion: Radiomics has the potential to predict immediate reperfusion and long-term outcomes in AIS patients. Further external validation and evaluation within the clinical workflow can facilitate personalized treatment for AIS patients. This systematic review provides valuable insights for optimizing radiomics prediction systems for both reperfusion and long-term outcomes in AIS patients.

Systematic review registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023461671, identifier CRD42023461671.

Keywords: acute ischemic stroke; prognosis prediction; radiomics; reperfusion prediction; systematic review and meta-analysis.

Figure 1

PRISMA flowchart of included studies.

Figure 2

Quality assessment of included studies. (A) Bar chart of the mean score of each study according to the radiomics quality scoring tool. (B) Pie chart of the mean score of studies according to the radiomics quality scoring tool. (C) Evaluation of Risk of bias.

Figure 3

Forest plots of the predictive performance of radiomics models. Forest plots showed the predictive performance of radiomics models in (A) long-term prognosis (mRS) and (B) immediate reperfusion (mTICI) of AIS patients. For long-term prognosis, the results of subgroup analysis according to single-center or multicenter factors are shown; Area under curve (AUC) for each study is presented as a black dot, with the horizontal line indicating the 95% confidence interval (CI). The pooled result for all studies is presented as a black diamond.

Figure 4

Funnel plot for publication bias of AUC. Funnel plot showed the publication bias of AUC in (A) long-term prognosis (mRS) and (B) immediate reperfusion (mTICI) of AIS patients.