Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1‐weighted MR images
Bibliographic record
Abstract
PURPOSE: To test the hypothesis that texture analysis of postcontrast T1-weighted MR images will predict hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) with better accuracy than visual evidence of contrast-enhancement (VE). MATERIALS AND METHODS: Thirty-four AIS patients were examined within 3.5 +/- 1.5 h after stroke. T1-weighted MR images were acquired 19 +/- 7 min postcontrast injection. HT was determined by follow-up imaging at 24-72 h. Postcontrast images were evaluated for VE. Four second-order textural features were extracted (f1, f2, f3, and f9) for each patient. Receiver operating characteristic (ROC) curves were constructed for VE and for textural features, with HT as the outcome measure. RESULTS: The f2 for HT patients (n = 12) was significantly lower than in non-HT patients (1058 +/- 356 versus 1568 +/- 527; P = 0.005); the converse was true for f3 (0.67 +/- 0.12 versus 0.54 +/- 0.13; P = 0.007). ROC analysis indicated that the f2 and f3 textural features were the only two significant predictors of HT (P = 0.0018 and P = 0.0042). The addition of VE to either f2 or f3 did not result in a significant improvement in accuracy. CONCLUSION: Texture analysis of postcontrast T1-weighted images may be superior to visual evidence of enhancement for the prediction of HT.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".