The integration of electroanatomic maps into cardiac radioablation treatment planning: A systematic review
Bibliographic record
Abstract
BACKGROUND: Ventricular tachycardia is a life-threatening cardiac arrhythmia for which radiation therapy is an emerging therapeutic option. Electroanatomic maps (EAMs) are used to define clinical target volumes (CTVs) in cardiac radioablation (CRA) treatment planning. Treatment planning systems are unable to integrate EAM data, thus many different workflows have been developed to guide clinicians in CTV creation. PURPOSE: To provide a review of existing CTV definition protocols involving EAM integration for CRA. METHODS: PubMed was searched on January 11, 2024, using appropriate search terms. Results were filtered according to inclusion and exclusion criteria following PRISMA guidelines. Results were manually sorted based on their workflow. RESULTS: The original literature search resulted in 271 search results, to which two hand-selected articles were added. 85 of the resulting articles met inclusion criteria and did not meet exclusion criteria. The reviewed protocols included side-by-side approaches for EAM integration into the treatment planning workflow as well as software-based protocols. Software-based protocols were further subcategorized based on whether the workflows used commercial or non-commercial software to aid in CTV definition. CONCLUSIONS: There is a strong desire to provide solutions for EAM integration into CRA CTV definition protocols. Although single center-specific approaches exist, there is no standardized workflow to address this problem. As the field of CRA grows, standardized workflows and guidelines will be necessary to perform meaningful analyses and comparisons of data between small data sets and to make recommendations for both technical and therapeutic indications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".