Clinical implementation of <scp>AXB</scp> from <scp>AAA</scp> for breast: Plan quality and subvolume analysis
Bibliographic record
Abstract
Abstract Purpose Two dose calculation algorithms are available in Varian Eclipse software: Anisotropic Analytical Algorithm (AAA) and Acuros External Beam (AXB). Many Varian Eclipse‐based centers have access to AXB; however, a thorough understanding of how it will affect plan characteristics and, subsequently, clinical practice is necessary prior to implementation. We characterized the difference in breast plan quality between AXB and AAA for dissemination to clinicians during implementation. Methods Locoregional irradiation plans were created with AAA for 30 breast cancer patients with a prescription dose of 50 Gy to the breast and 45 Gy to the regional node, in 25 fractions. The internal mammary chain (IMCCTV) nodes were covered by 80% of the breast dose. AXB, both dose‐to‐water and dose‐to‐medium reporting, was used to recalculate plans while maintaining constant monitor units. Target coverage and organ‐at‐risk doses were compared between the two algorithms using dose–volume parameters. An analysis to assess location‐specific changes was performed by dividing the breast into nine subvolumes in the superior–inferior and left–right directions. Results There were minimal differences found between the AXB and AAA calculated plans. The median difference between AXB and AAA for breastCTV V95%, was <2.5%. For IMCCTV, the median differences V95%, and V80% were <5% and 0%, respectively; indicating IMCCTV coverage only decreased when marginally covered. Mean superficial dose increased by a median of 3.2 Gy. In the subvolume analysis, the medial subvolumes were “hotter” when recalculated with AXB and the lateral subvolumes “cooler” with AXB; however, all differences were within 2 Gy. Conclusion We observed minimal difference in magnitude and spatial distribution of dose when comparing the two algorithms. The largest observable differences occurred in superficial dose regions. Therefore, clinical implementation of AXB from AAA for breast radiotherapy is not expected to result in changes in clinical practice for prescribing or planning breast radiotherapy.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".