Prospective Trial on the Impact of Weekly Cone Beam Computed Tomography-Guided Correction on Mean Heart Dose in Breast Cancer Breath-Hold Radiation Therapy
Bibliographic record
Abstract
Purpose: Surface guided radiation therapy (SGRT) in breast cancer radiation therapy (RT) may decrease the need for image guidance such as cone beam computed tomography (CBCT). The goal of this study was to evaluate the impact of CBCT image guidance on the cumulative and interfractional variation of mean heart dose (MHD) during breath-hold RT in patients with breast cancer. We hypothesized that weekly CBCT is not necessary for SGRT-assisted breath-hold but is still needed in patients treated with voluntary deep inspiration breath-hold (vDIBH) and active breathing control (ABC) to maintain a stable MHD. Methods and Materials: This was a prospective, single-center trial that sequentially assigned breast cancer patients to adjuvant RT 40 to 50 Gy in 15 to 25 fractions using vDIBH, ABC, or SGRT to reproduce the breath-hold. The MHD was estimated on each of the weekly CBCT images before and after online correction. The cumulative and interfractional variation of MHD, which were represented by the average and SD of MHD in each patient, were compared in the series of CBCT before and after online correction to evaluate whether online CBCT-guided correction could lead to a more reproducible MHD. Results: = .2272). The CBCT-guided online correction had no impact on the cumulative MHD in all 3 groups. Conclusions: This study demonstrated that CBCT-guided online correction could reduce the interfractional variation of MHD in ABC or vDIBH. When SGRT was available, CBCT-guided correction had no impact on the stability of MHD across the treatment fractions. Future studies may explore whether the CBCT frequency could be reduced to less than weekly in SGRT to decrease treatment time and the radiation dose associated with CBCT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".