Radiographic changes after lung stereotactic ablative radiotherapy (SABR) – Can we distinguish recurrence from fibrosis? A systematic review of the literature
Bibliographic record
Abstract
BACKGROUND: Changes in lung density on computed tomography (CT) are common after stereotactic ablative radiotherapy (SABR) and can confound the early detection of recurrence. We performed a systematic review to describe post-SABR findings on computed tomography (CT) and positron-emission tomography (PET), identify imaging characteristics that predict recurrence and propose a follow-up imaging algorithm. METHODS: A systematic review was conducted of studies providing detailed radiologic descriptions of anatomic and metabolic lung changes after SABR. Our search returned 824 studies; 26 met our inclusion criteria. Data are presented according to PRISMA guidelines. RESULTS: Acute changes post-SABR predominantly appear as consolidation or ground glass opacities. Late changes often demonstrate a modified conventional pattern of fibrosis, evolving beyond 2years after treatment. Several CT features, including an enlarging opacity, correlate with recurrence. Although PET SUVmax may rise immediately post-SABR, an SUVmax⩾5 carries a high predictive value of recurrence. CONCLUSIONS: CT density changes are common post-SABR. The available evidence suggests that recurrent disease should be suspected if high-risk CT changes are seen with SUVmax⩾5 on PET. Further studies are needed to validate the predictive values of such metrics, and for advanced analysis of CT changes to allow early detection of potentially curable local recurrence.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.006 | 0.007 |
| 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".