Technical note: Toward implementation of MR‐guided radiation therapy for laryngeal cancer with healthy volunteer imaging and a custom MR‐CT larynx phantom
Bibliographic record
Abstract
PURPOSE: Internal motion of the larynx can cause normal tissue toxicity and/or tumor underdosage during radiotherapy. MR-guided radiation therapy (MRgRT) provides improved soft-tissue contrast for patient setup and real-time gating of radiation based on cine imaging of tumor motion, potentially making it an advantageous modality for laryngeal treatments. However, there are potential concerns regarding the small target size, proximity to heterogeneous tissue interfaces in the airway that may cause dosimetric errors in the presence of the magnetic field, and uncertainty about the ability of MR-linear accelerator (MR-Linac) systems to visualize and track laryngeal motion. To date, there have been no reports of the use of MRgRT for laryngeal treatments. METHODS: A healthy volunteer was imaged on a ViewRay MRIdian MR-Linac. Organs-at-risk and a laryngeal pseudo target were contoured and used to generate a stereotactic body radiotherapy plan. A custom phantom was created using 3D-printing based on structures delineated on the volunteer images to construct an enclosure containing the target and airway anatomy, with a gap for radiochromic film, and filled with gelatin . The treatment plan was mapped onto the phantom and delivered dose assessed on radiochromic film with global normalization and a 10% dose threshold. A cine MR of the volunteer was acquired to assess the magnitude of larynx motion with speaking and swallowing, and system's ability to gate radiation. RESULTS: A clinically acceptable laryngeal treatment plan and larynx phantom that was MR and computed tomography-visible were successfully created. The delivered dose had good agreement with the treatment plan with a gamma passing rate of 96.5% (3%/2 mm). The MR-Linac was able to visualize, track, and gate larynx motion. CONCLUSIONS: The MRgRT workflow for laryngeal treatments was assessed and performed in preparation for clinical implementation on the MR-Linac, demonstrating that it is feasible to treat laryngeal cancer patients on the MR-Linac.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".