Time course and predictive factors for lung volume reduction following stereotactic ablative radiotherapy (SABR) of lung tumors
Bibliographic record
Abstract
BACKGROUND: Stereotactic ablative volume reduction (SAVR) is a potential alternative to lung-volume reduction surgery in patients with severe emphysema and excessive surgical risk. Having previously observed a dose-volume response for localized lobar volume reduction after stereotactic ablative radiotherapy (SABR) for lung tumors, we investigated the time course and factors associated with volume reduction. METHODS: We retrospectively identified 70 eligible patients receiving lung tumor SABR during 2007-2013. We correlated lobar volume reduction (relative to total, bilateral lung volume [TLV]) with volume receiving high biologically effective doses (VXXBED3) and other pre-treatment factors in all patients, and measured the time course of volume changes on 3-month interval CT scans in patients with large V60BED3 (n = 21, V60BED3 ≥4.1 % TLV). RESULTS: Median CT follow-up was 15 months. Median volume reduction of treated lobes was 4.5 % of TLV (range 0.01-13.0 %), or ~9 % of ipsilateral lung volume (ILV); median expansion of non-target adjacent lobes was 2.2 % TLV (-4.6-9.9 %; ~4 % ILV). Treated lobe volume reduction was significantly greater with larger VXXBED3 (XX = 20-100 Gy, R (2) = 0.52-0.55, p < 0.0001) and smaller with lower pre-treatment FEV1% (R (2) = 0.11, p = 0.005) in a multivariable linear model. Maximum volume reduction was reached by ~12 months and persisted. CONCLUSIONS: We identified a multivariable model for lobar volume reduction after SABR incorporating dose-volume and pre-treatment FEV1% and characterized its time course.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".