Sci-Fri PM: Delivery - 08: Total Marrow Irradiation Using Helical Tomotherapy in Treating a Multiple Myeloma Patient: A Case Study
Bibliographic record
Abstract
The Ottawa Hospital Cancer Centre has embarked on a phase I/II dose escalation study of IG-IMRT using Helical Tomotherapy (HT) for Total Marrow Irradiation (TMI) of multiple myeloma patients prior to autologous hematopoietic stem cell transplantation. In this work we outline the technical and physical hurdles related to planning and dose delivery and summarize our experience to date. Limitations with the scanning and planning systems required that patients have two CT scans; one of the upper body and one of the lower body with at least a 20 cm overlap. They must also have a separate treatment plan for each region. PTVs and OARs were defined on both CT sets and image fusion using ImageJ software was used to link the two scan sets. The treatment plan for the upper body used a 2.5 cm beam to provide good sup-inf dose conformation, while a 5.0 cm beam was used for the lower body. DQA was planned, delivered and analyzed, showing good agreement between the planned and measured dose distributions in the junction region. We demonstrate the technical feasibility of our method in overcoming the challenges related to the planning system, including junctioning and summing the dose clouds of longitudinally adjacent plans created on different CT data sets. The treatment was well-tolerated by the patient and no severe acute toxicity was noted. Scaling to the QUANTEC data (V20 of 30–35%) for lungs, we estimate that with the present CTV-PTV margins it should be possible to safely deliver 25 Gy TMI.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".