Bibliographic record
Abstract
Cerebral Infarct Volume Change Over Time In Ischemic Stroke Mark Krongold 1,2,4 , Armin Eilaghi 1,2,3,4 , Mohammed Almekhalfi 1,2 , Andrew Demchuk 1,2 , Richard Frayne 1,2,3,4 1 Department of Radiology and Clinical Neuroscience, University of Calgary, 2 Hotchkiss Brain Institute, University of Calgary, 3 Department of Electrical and Computer Engineering, University of Calgary. 4 Seaman Family MR Research Centre markkrongold@gmail.com INTRODUCTION Stroke is the second leading cause of death worldwide[1]. Ischemic strokes account for 80% of stroke events and occur due to blood clots which interrupt the flow of blood into the brain. Interruption of blood flow causes a lack of oxygen and nutrients in the brain which leads to a loss of brain function and the build up of infarct tissue[1]. This build up is a dynamic process in which stroke volume changes over time. Stroke evolution is characterized by two types of edema. Cytotoxic edema (imaged using DWI[2]) occurs acutely and causes the build up of fluid intracellularly [3] while vasogenic edema (imaged using FLAIR[4]) occurs due to the breakdown of the blood brain barrier or CSF barrier and is prolonged [5]. Imaging of subjects in clinical trials of stroke is done over periods of time of up to 90 days. Long time periods not only lead to a decrease of patients who follow up in the studies but allows for events such as trauma, secondary stroke, or even death to confound the data acquired. Evidence has shown that stroke volume 90 days post infarct is not significantly different than 30 days post infarct suggesting that stroke volume plateaus at the 30 day mark[6]. The purpose of this research was to study infarct volume evolution and determine if MR imaging at early time points can be used to predict final infarct volume. This would not only increase the number of patients that can be analyzed in clinical trials but help in earlier stroke management and treatment decision making. METHODS This is a retrospective study of patients who had strokes with DWI done at baseline and 2 or more FLAIR imaging sessions post baseline (either around 12 hours, 24 hours, 5 days, 30 days, or 90 days). Infarct tissue was traced using Cerebra and MIPAV software and confirmed by a neuroradiologist. Statistical analysis was done using one way ANOVA and correlation coefficients. RESULTS It was determined that infarct volumes at the 24 hour and 5-day time points were significantly different than volumes at baseline. Significant difference were also found between the 5-day and final (30+90day) time points. Correlation analysis indicated a strong positive correlation between stroke volume at the 5-day vs final time points in patients (Figure 1). Figure 1. Regression lines of final lesion volume (mL) plotted against the 5-day volume (mL) in subjects. Analysis determined a correlation coefficient of 0.884 (n=51). DISCUSSION AND CONCLUSIONS The results of this study suggest that vasogenic edema affects patients significantly between the acute and prolonged stage, increasing lesion volume until a peak is reached at around 5 days. A strong correlation between the 5-day and final volumes in patients suggests that more vasogenic edema at 5 days correlates to increased overall neuronal damage in the patient. The research shows that approximations of final outcome can be determined at earlier time points leading to a reduced need of subjects coming back in clinical trials, inclusion of more subjects in trial analysis, and quicker decision making in the stroke management process. REFERENCES Donnan GA, et al. Lancet. 371:1612-1623, 2008. Schaefer PW, et al. Stroke. 28:1082-1085, 1997. Liang D, et al. Neurosurg Focus. 22:E2, 2007. Brant-Zawadzki M, et al. Stroke. 27:1187-1191, 1996. Rosenberg GA & Yang Y. Neurosurg Focus. 22:1-9, 2007. Gaudinski MR, et al. Stroke. 39:2765-2768, 2008.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".