Role of Nuclear Imaging in Cardiac Stereotactic Body Radiotherapy for Ablation of Ventricular Tachycardia
Bibliographic record
Abstract
Ventricular tachycardia (VT) is a life-threatening arrhythmia common in patients with structural heart disease or nonischemic cardiomyopathy. Many VTs originate from regions of fibrotic scar tissue, where delayed electrical signals exit scar and re-enter viable myocardium. Cardiac stereotactic body radiotherapy (SBRT) has emerged as a completely noninvasive alternative to catheter ablation for the treatment of recurrent or refractory ventricular tachycardia. While there is no common consensus on the ideal imaging workflow, therapy planning for cardiac SBRT often combines information from a plurality of imaging modalities including MRI, CT, electroanatomic mapping and nuclear imaging. MRI and CT provide detailed anatomic information, and late enhancement contrast imaging can indicate regions of fibrosis. Electroanatomic maps indicate regions of heterogenous conduction voltage or early activation which are indicative of arrhythmogenic tissue. Some early clinical adopters performing cardiac SBRT report the use of myocardial perfusion and viability nuclear imaging to identify regions of scar. Nuclear imaging of hibernating myocardium, inflammation and sympathetic innervation have been studied for ventricular arrhythmia prognosis and in research relating to catheter ablation of VT but have yet to be studied in their potential applications for cardiac SBRT. The integration of information from these many imaging modalities to identify a target for ablation can be challenging. Multimodality image registration and dedicated therapy planning tools may enable higher target accuracy, accelerate therapy planning workflows and improve patient outcomes. Understanding the pathophysiology of ventricular arrhythmias, and localizing the arrhythmogenic tissues, is vital for successful ablation with cardiac SBRT. Nuclear imaging provides an arsenal of imaging strategies to identify regional scar, hibernation, inflammation, and sympathetic denervation with some advantages over alternative imaging strategies.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".