Complication probability for radiation pneumonitis (RP) after stereotactic body radiotherapy (SBRT).
Bibliographic record
Abstract
PURPOSE/OBJECTIVE: To determine clinically relevant SBRT/stereotactic ablative radiotherapy (SABR) dose tolerance limits for RP based on statistical analysis of outcomes data. MATERIALS AND METHODS: Eighteen consecutive patients who were treated using volumetric modulated arc therapy (RapidArc) for lung tumors exceeding 80cc were assessed. Clinical outcomes have been published elsewhere, and here we present a normal tissue complication probability (NTCP) analysis. The dose volume histogram (DVH) reduction techniques of total lung V20Gy, V15Gy, V10Gy, V5Gy and mean lung dose (MLD) were each analyzed, as well as ipsilateral lung V5Gy and contralateral lung V5Gy, using the DVH Evaluator software tool. The framework of the Lyman Model was used except that each DVH reduction method was analyzed independently instead of using the power-law relationship for volume dependence. Model parameters were fitted using Maximum Likelihood. RESULTS: RP was reported in 5 patients (CTC Grade 2 in 3, and Grade 3 in 2). Total lung V5Gy and contralateral lung V5Gy were the best predictors of RP (p < 0.0001 for both). For V5Gy, the 10% risk level for Grade 2-3 RP was 27.9% for total lung and 21.8% for contralateral lung. For V20Gy, the 25% risk level is 10.5% of total lung. CONCLUSIONS: Analysis of RP endpoints has identified total lung V5Gy and contralateral lung V5Gy as the best predictors of RP following SBRT when delivered with RapidArc. These findings are based on limited clinical data, and longer follow-up in larger patient cohorts is required in order to determine more accurate dose tolerance limits.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".