Heterogeneous radiotherapy dose-outcomes response in parotid glands
Bibliographic record
Abstract
Abstract Parotid glands are treated clinically as though the distribution of functional burden were homogeneous. Radiotherapy treatments are planned using whole parotid mean dose to predict risk of salivary dysfunction. Recent progress has identified specific parotid non-homogeneities by demonstrating the existence of regional, bath-and-shower, and dose-volume effects. In this work, parotid regional effects and their impact on salivary function are quantified using a non-parametric (model-free) approach. Regional effects have implications for clinical sparing practices. Radiotherapy planning contours, dose profiles, and late clinical outcomes from a single cohort consisting of N = 332 patients was used. Pre-radiotherapy and one year post-radiotherapy whole mouth stimulated saliva were collected for assessment of salivary dysfunction. Organ-at-risk parotid glands were segmented into 2, 3, 4, 18, and 96 equal-volume sub-segments. Sub-segment relative importance was derived from mean dose regressors using random forests and conditional inference trees. Regressor multicollinearity, cohort homogeneity, and overfitting were addressed. Linear and exponential whole parotid mean dose models were also implemented for comparison purposes. Exclusion of caudal-anterior sub-segments negatively impacted prediction the most. The most important sub-segments had importances (on average over all segmentation methods) or (at the finest level of segmentation) that of an equivalent sub-segment in a theoretical homogeneous parotid. In contrast, the least important sub-segments held virtually no importance for prediction. Both random forests and conditional inference trees outperformed parametric (model-based) techniques. Both improved prediction as segmentation was refined. Radiation dose to caudal-anterior aspects of the parotid are the strongest predictors of radiotherapy-induced late stimulated whole mouth saliva, and are thus the most clinically-relevant regions for controlling dysfunction. Cranial and posterior aspects are less important. Shifting dose from regions of high importance to low importance may therefore improve patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".