Sci‐Fri PM: Radiation Therapy, Planning, Imaging, and Special Techniques ‐ 07: Transitioning from extended‐distance total body irradiation to optimized VMAT total marrow irradiation
Bibliographic record
Abstract
Purpose: TMI targets only the bone marrow, with the intent of sparing normal tissues. The NSCC has recently implemented a TMI protocol which includes VMAT fields to treat the bone marrow from head to mid‐thigh and extended SSD POP fields to treat the lower legs. This work describes the commissioning and initial clinical results of the first reported VMAT TMI treatments in Canada. Methods: Detailed CT simulation, imaging, planning and treatment procedures were developed by a multi‐disciplinary team. Patients have 1 cm of bolus over the lower legs and 0.5 cm of bolus around the lower arms. The PTV includes all bone, except mandible, facial bones and hands, with the objective of V(12 Gy) > 90%. Detailed analysis of the influence of field overlap was performed to determine optimal field placement and image‐guidance tolerances. Results: PTV coverage was achieved for all cases as V(12 Gy) ranged from 90.4–96.3%. The minimum dose to the PTV, D(99%), ranged from 91.4–97.87% and V(90%Rx=10.8 Gy) ranged from 99.1–100.0%. The lungs, liver and heart had an average Dmean of (7.8±0.3)Gy/(65±2)%, (7.6±0.7)Gy/(63±5)%, and (6.8±0.4)Gy/(56±4)% respectively. Conclusions: Commissioning required input and collaboration from all team members. Transitioning from TBI to TMI requires additional time for contouring, treatment planning, QA, and treatment. Patient benefit can however be seen in the quality of OAR sparing.
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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".