Utilizing Quantitative Analysis of CSF Volume from Clinical T1‐Weighted MRI to Predict Thrombectomy Outcomes
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Endovascular thrombectomy (EVT) is the standard for acute ischemic stroke from large vessel occlusion, but post-EVT functional independence varies. Brain atrophy, linked to higher cerebrospinal fluid volume (CSFV), may affect outcomes. Baseline CSFV could predict EVT benefit by assessing brain health. We aimed to quantify total CSFV from clinical T1-weighted (w) magnetic resonance imaging (MRI) to assess global brain atrophy and its association with functional outcomes following successful EVT. METHODS: We performed a retrospective analysis of patients achieving thrombolysis-in-cerebral-infarction ≥2b revascularization via prospectively maintained single-institution stroke thrombectomy registry (n = 432) between 2015 and 2021. We included 214 patients (mean age 67.5 ± 14.6, 49% female) with acceptable quality MRI within 14 days of EVT and available modified Rankin-scale (mRS) at 90 days post EVT. Clinical T1w images were transformed into high-resolution images using the convolutional neural-network SynthSR. FreeSurfer software was then used to estimate total cranial CSFV. To correct for head size, percentage of CSFV to intracranial volume was used. RESULTS: Baseline CSFV% significantly predicted 90-day mRS in an ordinal regression model adjusted for baseline mRS (p < 0.001). Further modeling was performed to account for age, sex, 24-h National-Institutes-Health-Stroke-Scale (NIHSS), smoking history, prior stroke, hypertension, congestive heart failure, hemoglobin-A1c, atrial fibrillation, and Alberta-Stroke-Program-Early-CT-Score (ASPECTS). Total CSFV% remained an independent predictor of 90-day mRS (p = 0.012). CSFV% did not significantly predict the occurrence of any type of hemorrhagic transformation in a logistic regression model. CONCLUSIONS: Increased CSFV% correlates with poorer functional outcomes post EVT. Total CSFV% may serve as a useful imaging biomarker for clinicians determining patient prognostication prior to EVT.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".