Radiomics Analysis of Diffusion-Weighted Imaging and Long-Term Unfavorable Outcomes Risk for Acute Stroke
Bibliographic record
Abstract
BACKGROUND: Diffusion-weighted imaging radiomics could be used as prognostic biomarkers in acute ischemic stroke. We aimed to identify a clinical and diffusion-weighted imaging radiomics model for individual unfavorable outcomes risk assessment in acute ischemic stroke. METHODS: A total of 1716 patients with acute ischemic stroke from 2 centers were divided into a training cohort and a validation cohort. Patient outcomes were measured with the modified Rankin Scale score. An unfavorable outcome was defined as a modified Rankin Scale score greater than 2. The primary end point was all-cause mortality or outcomes 1 year after stroke. The MRI-DRAGON score was calculated based on previous publications. We extracted and selected the infarct features on diffusion-weighted imaging to construct a radiomic signature. The clinic-radiomics signature was built by measuring the Cox proportional risk regression score (CrrScore) and compared with the MRI-DRAGON score and the ClinicScore. CrrScore model performance was estimated by 1-year unfavorable outcomes prediction. RESULTS: <0.0001) cohorts. The diffusion-weighted imaging Alberta Stroke Program Early CT Score, age, glucose level before therapy, National Institutes of Health Stroke Scale score on admission, glycated hemoglobin' radiomic signature, hemorrhagic infarction, and malignant cerebral edema were associated with an unfavorable outcomes risk after multivariable adjustment. A CrrScore nomogram was developed to predict outcomes and had the best performance in the training (area under the curve, 0.862) and validation cohorts (area under the curve, 0.858). The CrrScore model time-dependent areas under the curve of the probability of unfavorable outcomes at 1 year in the training and validation cohorts were 0.811 and 0.801, respectively. CONCLUSIONS: The CrrScore model allows the accurate prediction of patients with acute ischemic stroke outcomes and can potentially guide rehabilitation therapies for patients with different risks of unfavorable outcomes.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".