Stereotactic Body Radiation Therapy for Lung and Liver Oligometastases from Breast Cancer: Toxicity Data of a Prospective Non-Randomized Phase II Trial
Bibliographic record
Abstract
AIMS: We report the mature toxicity data of a phase II non-randomized trial on the use of SBRT for lung and liver oligometastases. METHODS: Oligometastatic patients from breast cancer were treated with SBRT for up to five lung and/or liver lesions. Inclusion criteria were: age > 18 years, ECOG 0-2, diagnosis of breast cancer, less than five lung/liver lesions (with a maximum diameter <5 cm), metastatic disease confined to the lungs and liver or extrapulmonary or extrahepatic disease stable or responding to systemic therapy. Various dose-fractionation schedules were used. Then, a 4D-CT scan and FDG-CTPET were acquired for simulation and fused for target definition. RESULTS: From 2015 to 2021, 64 patients and a total of 90 lesions were irradiated. Treatment was well tolerated, with no G 3-4 toxicities. No grade ≥3 toxicities were registered and the coprimary endpoint of the study was met. Median follow-up was 19.4 months (range 2.6-73.1). CONCLUSIONS: The co-primary endpoint of this phase II trial was met, showing excellent tolerability of SBRT for lung and liver oligometastatic in breast cancer patients. Until efficacy data will mature with longer follow-up, SBRT should be regarded as an opportunity for oligometastatic breast cancer patients.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".