CT patterns and serial CT Changes in lung Cancer patients post stereotactic body radiotherapy (SBRT)
Bibliographic record
Abstract
BACKGROUND: To evaluate computed tomography (CT) patterns of post-SBRT lung injury in lung cancer and identify time points of serial CT changes. MATERIALS AND METHODS: One hundred eighty-three tumors in 170 patients were evaluated on sequential CTs within 29 months (median). Frequencies of post-SBRT CT patterns and time points of initiation and duration were assessed. Duration of increase of primary lesion or surrounding injury without evidence of local recurrence and time to stabilization or local recurrence were evaluated. RESULTS: Post-SBRT CT patterns could overlap in the same patient and were nodule-like pattern (69%), consolidation with ground glass opacity (GGO) (41%), modified conventional pattern (39%), peribronchial/patchy consolidation (42%), patchy GGO (24%), diffuse consolidation (16%), "orbit sign" (21%), mass-like pattern (19%), scar-like pattern (15%) and diffuse GGO (3%). Patchy GGO started at 4 months post-SBRT. Peribronchial/patchy consolidation and consolidation with GGO started at 4 and 5 months respectively. Diffuse consolidation, diffuse GGO and orbit sign started at 5, 6 and 8 months respectively. Mass-like, modified conventional and scar-like pattern started at 8, 12 and 12 months respectively. Primary lesion (n = 11) or surrounding injury (n = 85) increased up to 13 months. Primary lesion (n = 119) or surrounding injury (n = 115) started to decrease at 4 and 9 months respectively. Time to stabilization was 20 months. The most common CT pattern at stabilization was modified conventional pattern (49%), scar-like pattern (23%) and mass-like pattern (12%). Local recurrence (n = 15) occurred at a median time of 18 months. CONCLUSION: Different CT patterns of lung injury post-SBRT appear in predictable time points and have variable but predictable duration. Familiarity with these patterns and timeframes of appearance helps differentiate them from 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".