Intrapatient study comparing 3D printed bolus versus standard vinyl gel sheet bolus for postmastectomy chest wall radiation therapy
Bibliographic record
Abstract
PURPOSE: This patient study evaluated the use of 3-dimensional (3D) printed bolus for chest wall radiation therapy compared with standard sheet bolus with regard to accuracy of fit, surface dose measured in vivo, and efficiency of patient setup. By alternating bolus type over the course of therapy, each patient served as her own control. METHODS AND MATERIALS: For 16 patients undergoing chest wall radiation therapy, a custom 5.0 mm thick bolus was designed based on the treatment planning computed tomography scan and 3D printed using polylactic acid. Cone beam computed tomography scanning was used to image and quantify the accuracy of fit of the 2 bolus types with regard to air gaps between the bolus and skin. As a quality assurance measure for the 3D printed bolus, optically stimulated luminescent dosimetry provided in vivo comparison of surface dose at 7 points on the chest wall. Durations of patient setup and image guidance were recorded and compared. RESULTS: In 13 of 16 patients, the bolus was printed without user intervention, and the median print time was 12.6 hours. The accuracy of fit of the bolus to the chest wall was improved significantly relative to standard sheet bolus, with the frequency of air gaps 5 mm or greater reduced from 30% to 13% (P < .001) and maximum air gap dimension diminished from 0.5 ± 0.3 to 0.3 ± 0.3 mm on average. Surface dose was within 3% for both standard sheet and 3D printed bolus. On average, the use of 3D printed bolus reduced the setup time from 104 to 76 seconds. CONCLUSIONS: This study demonstrates 3D printed bolus in postmastectomy radiation therapy improves fit of the bolus and reduces patient setup time marginally compared with standard vinyl gel sheet bolus. The time savings on patient setup must be weighed against the considerable time needed for the 3D printing process.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".