Abstract TP45: One Threshold Does not Fit All: Hounsfield Unit Thresholds to Segment Clot on NCCT are Patient Specific
Bibliographic record
Abstract
Introduction: The hyperdense sign is a marker of thrombus on non-contrast computed tomography (NCCT). Currently, pre-specified Hounsfield unit (HU) threshold are used arbitrarily for clot segmentation. We aimed to test if HU thresholds best discriminating clot on thin slice NCCT are patient specific and dependent on factors such as age, hematocrit, and NCCT slice thickness. Methods: Data are from the ESCAPE randomized controlled trial. Only patients with thin slice baseline NCCT (<2.5mm) and M1-MCA occlusion on baseline CT-Angiography (CTA) were included. CTA was co-registered to NCCT using in-house software. Proximal and distal clot interface was identified on CTA and super-imposed onto the NCCT. Three Regions of Interest (ROIs) (36- 100 voxels) were placed on the NCCT in the 1) clot as defined on co-registered CTA, 2) contralateral brain tissue, and 3) contralateral patent M1 MCA artery. Optimal patient specific HU thresholds differentiating “clot” from “normal vessel” and “brain tissue” voxels were calculated using receiver operating characteristic (ROC) analysis. Results: 70 patients were included. ROC analysis showed that the optimal HU threshold discriminating clot on NCCT varies considerably between patients (Figure 1A). Hematocrit was associated with contralateral artery HU (p<0.001); no significant correlation was found with age (p=0.156) and slice thickness (p=0.473). Predictive ability of contralateral artery and brain tissue HU in determining optimal HU threshold discriminating clot was similar (p<0.001, Figure 1B). Model comparison using Bayesian Information Criterion suggests that we can reasonably use contralateral brain tissue HU or contralateral artery HU to derive patient-specific optimal HU thresholds that best discriminates clot. Conclusion: HU thresholds on NCCT that best discriminates clot are patient specific. Signal from brain tissue or patent artery silhouette can be used to derive these patient specific thresholds.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".