Penumbra Pattern Assessment in Acute Stroke Patients: Comparison of Quantitative and Non-Quantitative Methods in Whole Brain CT Perfusion
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: While penumbra assessment has become an important part of the clinical decision making for acute stroke patients, there is a lack of studies measuring the reliability and reproducibility of defined assessment techniques in the clinical setting. Our aim was to determine reliability and reproducibility of different types of three-dimensional penumbra assessment methods in stroke patients who underwent whole brain CT perfusion imaging (WB-CTP). MATERIALS AND METHODS: We included 29 patients with a confirmed MCA infarction who underwent initial WB-CTP with a scan coverage of 100 mm in the z-axis. Two blinded and experienced readers assessed the flow-volume-mismatch twice and in two quantitative ways: Performing a volumetric mismatch analysis using OsiriX imaging software (MM(VOL)) and visual estimation of mismatch (MM(EST)). Complementarily, the semiquantitative Alberta Stroke Programme Early CT Score for CT perfusion was used to define mismatch (MM(ASPECTS)). A favorable penumbral pattern was defined by a mismatch of ≥ 30% in combination with a cerebral blood flow deficit of ≤ 90 ml and an MM(ASPECTS) score of ≥ 1, respectively. Inter- and intrareader agreement was determined by Kappa-values and ICCs. RESULTS: Overall, MM(VOL) showed considerably higher inter-/intrareader agreement (ICCs: 0.751/0.843) compared to MM(EST) (0.292/0.749). In the subgroup of large (≥ 50 mL) perfusion deficits, inter- and intrareader agreement of MM(VOL) was excellent (ICCs: 0.961/0.942), while MM(EST )interreader agreement was poor (0.415) and intrareader agreement was good (0.919). With respect to penumbra classification, MM(VOL) showed the highest agreement (interreader agreement: 25 agreements/4 non-agreements/κ: 0.595; intrareader agreement 27/2/0.833), followed by MM(EST) (22/7/0.471; 23/6/0.577), and MM(ASPECTS) (18/11/0.133; 21/8/0.340). CONCLUSION: The evaluated approach of volumetric mismatch assessment is superior to pure visual and ASPECTS penumbra pattern assessment in WB-CTP and helps to precisely judge the extent of 3-dimensional mismatch in acute stroke patients.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".