PET/MRI Assessment of Acute Cardiac Inflammation 1 Month After Left-Sided Breast Cancer Radiation Therapy
Bibliographic record
Abstract
Our purpose was to investigate the utility of 18F-FDG PET/MRI and serial blood work to detect early inflammatory responses and cardiac functionality changes at 1 mo after radiation therapy (RT) in patients with left-sided breast cancer. Methods: Fifteen left-sided breast cancer patients who enrolled in the RICT-BREAST study underwent cardiac PET/MRI at baseline and 1 mo after standard RT. Eleven patients received deep-inspiration breath-hold RT, whereas the others received free-breathing RT. A list-mode 18F-FDG PET scan with glucose suppression was acquired. Myocardial inflammation was quantified by the change in 18F-FDG SUVmean (based on body weight) and analyzed on the basis of the myocardial tissue associated with the left anterior descending, left circumflex, or right coronary artery territories. MRI assessments, including left ventricular functional and extracellular volumes (ECVs), were extracted from T1 (before and during a constant infusion of gadolinium) and cine images, respectively, acquired simultaneously during the PET acquisition. Cardiac injury and inflammation biomarker measurements of high-sensitivity troponin T, high-sensitivity C-reactive protein, and erythrocyte sedimentation rate were measured at the 1-mo follow-up and compared with preirradiation values. Results: At the 1-mo follow-up, a significant increase (10%) in myocardial SUVmean in left anterior descending segments (P = 0.04) and ECVs in slices at the apex (6%) and base (5%) was detected (P ≤ 0.02). Further, a significant reduction in left ventricular stroke volume (−7%) was seen (P < 0.02). No significant changes in any circulating biomarkers were seen at follow-up. Conclusion: Myocardial 18F-FDG uptake and functional MRI, including stroke volume and ECVs, were sensitive to changes at 1 mo after breast cancer RT, with findings suggesting an acute cardiac inflammatory response to RT.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".