A new approach to quantifying lung damage after stereotactic body radiation therapy
Bibliographic record
Abstract
Radiological pneumonitis and fibrosis are common after stereotactic body radiotherapy (SBRT) but current scoring systems are qualitative and subjective. We evaluated the use of CT density measurements and a deformable registration tool to quantitatively measure lung changes post-SBRT. Material and methods. Four-dimensional CT datasets from 25 patients were imported into an image analysis program. Deformable registration was done using a B-spline algorithm (VelocityAI) and evaluated by landmark matching. The effects of respiration, contrast, and CT scanner on density measurements were evaluated. The relationship between density and clinician-scored radiological pneumonitis was assessed. Results. Deformable registration resulted in more accurate image matching than rigid registration. CT lung density was maximal at end-expiration, and most deformation with breathing occurred in the lower thorax. Use of contrast increased mean lung density by 18 HU (range 16-20 HU; p = 0.004). Diagnostic scans had a lower mean lung density than planning scans (mean difference 57 HU in lung contralateral to tumor; p = 0.048). Post-treatment CT density measurements correlated strongly with clinician-scored radiological pneumonitis (r = 0.75; p < 0.001). Conclusions. Quantitative analysis of changes in lung density correlated strongly with physician-assigned radiologic pneumonitis scores. Deformable registration and CT density measurements permit objective assessment of treatment toxicity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".