Quantitative Lesion Water Uptake as Stroke Imaging Biomarker: A Tool for Treatment Selection in the Extended Time Window?
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Patients presenting in the extended time window may benefit from mechanical thrombectomy. However, selection for mechanical thrombectomy in this patient group has only been performed using specialized image processing platforms, which are not widely available. We hypothesized that quantitative lesion water uptake calculated in acute stroke computed tomography (CT) may serve as imaging biomarker to estimate ischemic lesion progression and predict clinical outcome in patients undergoing mechanical thrombectomy in the extended time window. METHODS: All patients with ischemic anterior circulation stroke presenting within 4.5 to 24 hours after symptom onset who received initial multimodal CT between August 2014 and March 2020 and underwent mechanical thrombectomy were analyzed. Quantitative lesion net water uptake was calculated from the admission CT. Prediction of clinical outcome was assessed using univariable receiver operating characteristic curve analysis and logistic regression analyses. RESULTS: =0.03) were significantly and independently associated with favorable outcome (modified Rankin Scale score ≤1), adjusted for degree of recanalization and Alberta Stroke Program Early CT Score. A multivariable predictive model including the above parameters yielded the highest diagnostic ability in the classification of functional outcome, with an area under the curve of 0.88 (sensitivity 92.3%, specificity 82.9%). CONCLUSIONS: The implementation of quantitative lesion water uptake as imaging biomarker in the diagnosis of patients with ischemic stroke presenting in the extended time window might improve clinical prognosis. Future studies could test this biomarker as complementary or even alternative tool to CT perfusion.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".