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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".