Diagnostic Accuracy of Dual-Energy CT in Detection of Acute Pulmonary Embolism: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
PURPOSE: In this systematic review and meta-analysis, we aimed to investigate the accuracy of dual-energy computed tomography (DECT) in the detection of acute pulmonary embolism (PE). METHODS: We searched Medline (via PubMed), EBSCO, Web of Science, Scopus, and the Cochrane Library for relevant published studies. We selected studies assessing the accuracy of DECT in the detection of PE. Quality assessment of bias and applicability was conducted using the Quality of Diagnostic Accuracy Studies-2 tool. Meta-analysis was performed to calculate mean estimates of sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR). The summary receiver operating characteristic (sROC) curve was drawn to get the Cochran Q-index and the area under the curve (AUC). RESULTS: Seven studies were included in our systematic review. Of the 182 patients included, 108 patients had PEs. The pooled analysis showed an overall sensitivity and specificity of 88.9% (95% confidence interval [CI]: 81.4%-94.1%) and 94.6% (95% CI: 86.7%-98.5%), respectively. The pooled PLR was 8.186 (95% CI: 3.726-17.986), while the pooled NLR was 0.159 (95% CI: 0.093-0.270). Cochran-Q was 0.8712, and AUC was 0.935 in the sROC curve. CONCLUSION: Dual-energy computed tomography shows high sensitivity, specificity, and diagnostic accuracy in the detection of acute PE. The high PLR highlights the high clinical importance of DECT as a prevalence-independent, rule-in test. Studies with a larger sample size with standardized reference tests are still needed to increase the statistical power of the study and support these findings.
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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 | 0.022 | 0.058 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.050 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".