SU‐E‐J‐209: Dosimetric Benefits of Replanning for IMRT Treatment of Head and Neck Cancer
Bibliographic record
Abstract
Purpose: To evaluate the dosimetric benefit of replanning during the course of intensity modulated radiotherapy (IMRT) for head‐and‐neck cancer patients. Methods: The study included 30 patients who had at least one replan due to weight loss, tumor growth, swelling, or positioning issues. They were prescribed to receive between 60 and 70 Gy with simultaneous integrated boost using multiple‐field IMRT or volumetric modulated arc therapy. The original plan of each patient was recalculated on the computed tomography (CT) set acquired for replanning. MIM Software (Cleveland, OH) was used to rigidly align the original and the new CT to the same coordinate system, matching the high‐dose clinical target volume (CTV). These recalculations represent the scenario where the treatment continues with a new setup but the same treatment plan is used. Results: Without replanning, the mean near maximum dose (D2%) to the high‐dose CTV was increased by 2.0%. The mean near minimum (D98%) doses to the high, intermediate, and low‐dose CTVs were decreased by 5.1%, 4.0%, and 3.1% respectively. One patient could not meet the brainstem maximum dose (Dmax) constraint of 54 Gy. The mean parotid dose was increased by 1.8%. There was a weak correlation between the parotid dose and the change in parotid volume (correlation coefficient=−0.323, p=0.016). Setup variations between CT simulations caused 46% of the plans to exceed the spinal cord Dmax constraint of 45 Gy, and 22% of the plans to have hot spots exceeding 120% of the prescription dose. The replans were comparable to the original plans in target dose coverage, homogeneity, and critical structure doses. Replanning reduced the mean parotid dose by <0.7%. Conclusion: Replanning is important when the patient setup is notably different than the original simulation. When done in conjunction with image guidance before treatment delivery, it helps to ensure dosimetric objectives can be maintained.
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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) | 0.000 | 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) | 0.001 | 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 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".