Radiological changes on CT after stereotactic body radiation therapy to non-spine bone metastases: a descriptive series
Bibliographic record
Abstract
BACKGROUND: In recent years, stereotactic body radiation therapy (SBRT) has become increasingly used for the management of non-spine bone metastases. Few studies have examined the radiological changes in bone metastases after treatment with SBRT and there is no consensus about what constitutes radiologic response to therapy. This article describes various changes on CT after SBRT to non-spine bone metastases in eight selected cases. METHODS: A retrospective review was conducted for patients treated with SBRT to non-spine bone metastases between November 2011 and April 2014 at Sunnybrook Health Sciences Centre. A musculoskeletal radiologist identified eight illustrative cases of interest and provided a description of the findings. RESULTS: Different radiological changes following SBRT were described, including: remineralization of lytic bone metastases, demineralization of sclerotic bone metastases, pathologic fracture, size progression and response in different lesions, as well as lung fibrosis after SBRT to a rib metastasis. CONCLUSIONS: We reviewed the radiological images of eight selected cases after SBRT to nonspine bone metastases and a number of characteristic findings were highlighted. We recommend future studies to correlate radiologic changes with clinical outcomes including pain relief, toxicity and long-term local control.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 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 | 0.001 |
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".