Clinical Outcomes of Patients with Metastatic Breast Cancer Treated with Hypo-Fractionated Liver Radiotherapy
Bibliographic record
Abstract
Purpose: To retrospectively review the clinical outcomes of patients with metastatic breast cancer (MBCa) following liver directed ablative intent radiotherapy (RT). Methods: Demographics, disease and treatment characteristics of patients with MBCa who received liver metastasis (LM) directed ablative RT between 2004–2020 were analysed. The primary outcome was local control (LC), secondary outcomes included overall survival (OS) and progression-free survival (PFS) analyzed by univariate (UVA) and multi-variable analysis (MVA). Results: Thirty MBCa patients with 50 LM treated with 5–10 fraction RT were identified. Median follow-up was 14.6 (range 0.9–156.2) months. Class of metastatic disease was described as induced (12 patients, 40%), repeat (15 patients, 50%) and de novo (three patients, 10%). Median size of treated LM was 3.1 cm (range 1–8.8 cm) and median biologically effective dose delivered was 122 (Q1–Q3; 98–174) Gy3. One-year LC rate was 100%. One year and two-year survival was 89% and 63%, respectively, with size of treated LM predictive of OS (HR 1.35, p = 0.023) on UVA. Patients with induced OMD had a significantly higher rate of progression (HR 4.77, p = 0.01) on UVA, trending to significance on MVA (HR 3.23, p = 0.051). Conclusions: Hypo-fractionated ablative liver RT in patients with MBCa provides safe, tolerable treatment with excellent LC.
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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 | 0.000 | 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".