Superficial Parotid Lobe–Sparing Delineation Approach: A Better Method of Dose Optimization to Protect the Parotid Gland in Intensity-Modulated Radiotherapy for Nasopharyngeal Carcinoma
Bibliographic record
Abstract
PURPOSE: We used a superficial parotid lobe-sparing delineation approach for dose optimization with better protection for the parotid glands in intensity-modulated radiotherapy (imrt) for nasopharyngeal carcinoma (npc) patients. METHODS: Compared with traditional contouring of the entire parotid glands as organs at risk (oars) in imrt for npc, we used a superficial parotid lobe-sparing delineation approach of contouring the superficial parotid lobes as oars. Changes in dose to the parotid glands, the targets, and other oars were evaluated. RESULTS: The mean dose to the parotid glands overall decreased by more than 4 Gy in the test plans. Impressively, the mean dose to the superficial parotid lobes in the test plans was not more than 30 Gy, regardless of clinical stage. In T1-3 npc patients, the dose distributions for targets were not significantly different in the control plans and the test plans. However, for some T4 patients, the dose distributions for targets and brainstem in the test plans could not meet clinical requirements. CONCLUSIONS: The superficial parotid lobe-sparing delineation approach can significantly lower the mean dose to the entire parotid and to the superficial parotid lobe in T1-3 npc patients, which would be expected to result in less xerostomia and better quality of life for those patients.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".