A total inverse planning paradigm: Prospective clinical trial evaluating the performance of a novel MR-based 3D-printed head immobilization device
Bibliographic record
Abstract
Background and purpose: Brain radiotherapy (cnsRT) requires reproducible positioning and immobilization, attained through redundant dedicated imaging studies and a bespoke moulding session to create a thermoplastic mask (T-mask). Innovative approaches may improve the value of care. We prospectively deployed and assessed the performance of a patient-specific 3D-printed mask (3Dp-mask), generated solely from MR imaging, to replicate a reproducible positioning and tolerable immobilization for patients undergoing cnsRT. Material and methods: Patients undergoing LINAC-based cnsRT (primary tumors or resected metastases) were enrolled into two arms: control (T-mask) and investigational (3Dp-mask). For the latter, an in-house designed 3Dp-mask was generated from MR images to recreate the head positioning during MR acquisition and allow coupling with the LINAC tabletop. Differences in inter-fraction motion were compared between both arms. Tolerability was assessed using patient-reported questionnaires at various time points. Results: Between January 2020 - July 2022, forty patients were enrolled (20 per arm). All participants completed the prescribed cnsRT and study evaluations. Average 3Dp-mask design and printing completion time was 36 h:50 min (range 12 h:56 min - 42 h:01 min). Inter-fraction motion analyses showed three-axis displacements comparable to the acceptable tolerance for the current standard-of-care. No differences in patient-reported tolerability were seen at baseline. During the last week of cnsRT, 3Dp-mask resulted in significantly lower facial and cervical discomfort and patients subjectively reported less pressure and confinement sensation when compared to the T-mask. No adverse events were observed. Conclusion: The proposed total inverse planning paradigm using a 3D-printed immobilization device is feasible and renders comparable inter-fraction performance while offering a better patient experience, potentially improving cnsRT workflows and its cost-effectiveness.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".