Stereotactic body radiotherapy for colorectal liver metastases
Bibliographic record
Abstract
BACKGROUND: This study was undertaken to determine outcomes of stereotactic body radiotherapy for colorectal liver metastases in a pooled patient cohort. METHODS: Patients with colorectal liver metastases from 3 institutions were included if they had 1 to 4 lesions, received 1 to 6 fractions of stereotactic body radiotherapy, and had radiologic imaging ≥ 3 months post-treatment. Sixty-five patients with 102 lesions treated from August 2003 to May 2009 were retrospectively analyzed. A tumor control probability (TCP) model was used to estimate the 3-fraction dose required for > 90% local control after converting the schedule into biologically equivalent dose (BED), single-fraction equivalent dose, or linear quadratic model-based single-fraction dose. RESULTS: Forty-seven (72%) patients had ≥ 1 chemotherapy regimen before stereotactic body radiotherapy, and 27 (42%) patients had ≥ 2 regimens. The median follow-up was 1.2 years (range, 0.3-5.2 years). The median dose was 42 gray (Gy; range, 22-60 Gy). When evaluated separately by multivariate analysis, total dose (P = .0015), dose/fraction (P = .003), and BED (P = .004) all correlated with local control by lesion. On multivariate analysis, nonactive extrahepatic disease was associated with overall survival (OS; P = .046), and sustained local control was closely correlated (P = .06). By using single-fraction equivalent dose, BED, or linear quadratic model-based single-fraction dose in the TCP model, the estimated dose range needed for 1-year local control > 90% is 46 to 52 Gy in 3 fractions. CONCLUSIONS: Liver stereotactic body radiotherapy is well tolerated and effective for colorectal liver metastases. The strong correlation between local control and OS supports controlling hepatic disease even for heavily pretreated patients. For a 3-fraction regimen of stereotactic body radiotherapy, a prescription dose of ≥ 48 Gy should be considered, if normal tissue constraints allow.
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 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 | 0.000 | 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) | 0.001 | 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".