Abstract 39: Ability Of Radiomics Versus Humans In Predicting First-pass Effect After Endovascular Treatment In The Escape-na1 Trial
Bibliographic record
Abstract
Introduction: First-pass effect (FPE), i.e., achieving reperfusion with a single thrombectomy device pass, is associated with better clinical outcomes in patients with acute stroke. FPE is therefore increasingly being used as a marker of device and procedural efficacy. We evaluated the ability of thrombus-based radiomics models to predict FPE in patients undergoing endovascular thrombectomy (EVT) and compare performance to experts and non-radiomics thrombus characteristics. Methods: Patients with thin-slice non-contrast CT and CT angiography from The Efficacy and Safety of Nerinetide for the Treatment of Acute Ischemic Stroke (ESCAPE-NA1) trial were included. Thrombi were manually segmented on all images. Data was randomly split into a derivation set that included a training and a validation subset and an independent test set. Radiomics features were extracted from the derivation data set. Three expert stroke physicians reviewed baseline imaging and clinical data for the test set. The machine learning (ML) models were compared to the three experts in predicting the primary outcome (FPE) in the test set using area under the receiver operating characteristic curves (AUC-ROC). Results: A total of 554 patients with available thin-slice images comprised of a derivation set (training subset [n=388, 70%]), validation subset [n=55, 10%]), and a test set (n=111, 20%). FPE was seen in 31.8% in the derivation set and 31.5 % in the test set. AUC of the best radiomics model was 0.74 (95% CI: 0.64, 0.84), which was higher than the mean AUC of the three experts 0.60 (95% CI: 0.50, 0.71) ( P =0.009). Specificity of radiomics was better than the mean specificity of the three experts, 46 of 76 (60%) vs. 35 of 76 (46.4%), P =0.004, whereas sensitivity was not significantly different between radiomics (28 of 35 [79%]) and experts (27 of 35 [77%]). Moreover, radiomics features performed better than non-radiomics features such as thrombus volume and permeability measurements in predicting FPE ( P <0.05). Conclusion: A radiomics-based ML model of thrombus characteristics on non-contrast CT and CT angiography performs better than experts and non-radiomics image characteristics in predicting FPE in patients with acute stroke treated with EVT.
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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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".