Poster ‐ 34: Clinical Implementation of Prone Breast Treatment
Bibliographic record
Abstract
Purpose: Prone breast treatment is used to reduce acute and late toxicities for large or pendulous breast patients. This study developed and implemented the clinical workflow of prone breast radiotherapy treatment. Methods: Varian kVue Access360™ Prone Breast Couchtop was used as prone breast board. The treatment planning (TP)is performed in Eclipse TP system. TP comparisons between supine deep inspiration breathing hold (DIBH) and prone breast; prone forward field‐in‐field (FinF) planning and inverse IMRT planning were performed and discussed. For the daily setup, breast coverage was assessed in the room using light field and MV imaging was used at day 1 and weekly. Results: The first ten patients are CT scanned and planned both supine and prone. The coverage was all excellent for supine DIBH plan and prone breast plan. The plan in the prone position demonstrated improvements in lung sparing comparing to the DIBH plan. Both forward FinF plan and inverse IMRT plan achieved acceptable coverage of the breast, and heart dose is comparable. Considering the daily setup variations and MLC leakage, forward FinF plan was recommended for routine clinical use. The procedure has been tested in phantom and patients were treated clinically. Conclusions: Prone breast irradiation has been advocated for women with large pendulous breasts in order to decrease acute and late toxicities. The workflow for prone breast radiation therapy has been developed and the technique is ready to treat patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".