MRI quantitative biomarkers focusing on apparent diffusion coefficient for predicting hemorrhagic transformation after thrombectomy: A PRISMA-DTA systematic review and meta-analysis
Bibliographic record
Abstract
BackgroundHemorrhagic transformation (HT) is a serious complication following mechanical thrombectomy in acute ischemic stroke (AIS), significantly impacting clinical outcomes. Magnetic resonance imaging (MRI)-based quantitative biomarkers, particularly the apparent diffusion coefficient (ADC), have been investigated as predictors of HT, but findings across studies remain inconsistent. This study aimed to evaluate the diagnostic performance of quantitative MRI biomarkers, especially ADC values, for predicting any HT in AIS patients undergoing mechanical thrombectomy.MethodsA systematic search of PubMed, Embase, Scopus, and Web of Science was performed for studies published up to 20 July 2025, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Risk of bias was assessed by QUADAS-2. Eligible studies assessed quantitative biomarkers based on pre-treatment MRI for predicting any HT post-thrombectomy. Data on sensitivity, specificity, area under the curve (AUC), and other diagnostic metrics were extracted. Pooled estimates were calculated using a bivariate random-effects model. Heterogeneity was assessed via I² statistics, and publication bias was evaluated using Deeks' funnel plot.ResultsEleven studies were included. The pooled sensitivity and specificity of models based on ADC for predicting HT were 0.75 (95% CI: 0.66-0.82, I²: 0%) and 0.73 (95% CI: 0.65-0.80, I²: 58.91%), respectively. The summary AUC was 0.79 (95% CI: 0.75-0.83), indicating strong diagnostic performance. Additional biomarkers such as infarct core volume, white matter hyperintensity and arterial spin labeling demonstrated potential but lacked sufficient data for meta-analysis.ConclusionsDiffusion-weighted imaging shows good diagnostic accuracy for predicting HT after mechanical thrombectomy. Integration of advanced imaging biomarkers into pre-thrombectomy protocols could enhance clinical decision-making and patient safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.044 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".