Abstract 2572: Dual Imaging with MRI and <sup>18</sup> F-FDG PET Can Highly Predict Lipid and Hematoma in Carotid Plaque
Bibliographic record
Abstract
Background and Purpose - Recent studies have disclosed that inflamed and vulnerable plaques in the carotid artery are at high risk for subsequent ischemic stroke, suggesting the importance of non-invasive diagnostic modalities with high sensitivity and specificity to detect them in patients with carotid artery stenosis. Although many investigators have reported that MR imaging is a useful tool to predict the components of carotid plaque, its validity is not established. On the other hand, 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET) may be an alternative modality to directly identify the inflamed plaque in carotid artery stenosis. Therefore, this study was aimed to evaluate the validity of MR imaging and 18 F-FDG PET to predict the components of carotid plaques. Methods - Totally 19 patients were included in this study. Prior to carotid endarterectomy (CEA), 18 F-FDG PET, black-blood fat-suppressed T1-weighted (FS-T1) imaging, and 3-dimensional time-of-flight (TOF) imaging were performed in all of them. During CEA, macroscopic observation of carotid plaque was performed under surgical microscope. The specimens were stained with primary antibodies against CD68 and MMP9. Results - 18 F-FDG PET revealed that 11 of 19 patients had the carotid plaque with significantly high 18 F-FDG uptake (SUVmax >2.0). All of them had lipid-rich soft plaque with strong immunoreactivity against CD68 and MMP9. Its sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) to identify lipid-rich soft plaque were all 100%. On the other hands, 6 out of 19 patients had the carotid plaque with high signal intensity on both FS-T1 and TOF imaging. Carotid plaque had a large intraplaque hematoma in these 6 patients. Their sensitivity, specificity, PPV, and NPV to identify intraplaque hematoma were 86%, 100%, 100%, and 92%, respectively. Conclusions - These findings strongly suggest that MRI and 18 F-FDG PET are complementary to predict the components of carotid plaque. The former would be useful to detect vulnerable plaque with subintimal hemorrhage, and the latter would be sensitive to identify vulnerable plaque with lipid-rich component. Therefore, combination of these two modalities may be quite valuable to non-invasively predict the carotid plaque at higher risk for subsequent ischemic stroke.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".