A Customized 3D-Printed Bolus for High-Risk Breast Cancer with Skin Infiltration: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: In high-risk breast cancer patients with skin infiltration, the administration of a uniform dose to superficial tissues is fundamental in order to reduce local skin relapse. A personalized bolus may prevent the potential inadequate dose distribution of a standard bolus due to air gaps between the bolus and the skin. In this pilot study, we introduced into clinical practice the use of a personalized 3D-printed bolus filled with ultrasound transmission gel. METHODS: Seven patients undergoing radiotherapy after mastectomy were selected. A 3D-printed bolus dosimetric assessment was performed with MOSFET dosimeters on an anthropomorphic phantom and, subsequently, on three selected cases with increasing bolus shape irregularity. Acute/late toxicity and local control were assessed. RESULTS: Overall, for the clinical cases, the percentage median difference between the measured and calculated doses was -2.7% (-7.0-4.9%). The median follow-up was 21 months. After two years, one patient showed G2 pain, one patient manifested G1 telangiectasia, one patient showed G1 hyperpigmentation, and two patients had no relevant toxicity. CONCLUSIONS: A personalized 3D-printed bolus filled with ultrasound gel may easily reproduce the standard bolus' consistency and provide accurate coverage of the target area with tolerable acute/late toxicity grades. This is a pilot study, and further investigations are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".