Repeated Multimodality Ablative Therapies for Oligorecurrent Pulmonary Metastatic Disease
Bibliographic record
Abstract
Stereotactic body radiotherapy (SBRT) and percutaneous thermal ablation (TA) are alternatives to surgery for the management of pulmonary oligometastases. In this collaborative work, we retrospectively analyzed patients who had undergone iterative focal ablative treatments of pulmonary oligometastases. We hypothesized that repeated ablative therapies could benefit patients with consecutive oligometastatic relapses. Patients treated with SBRT and/or TA for pulmonary oligometastases in two French academic centers between October 2011 and November 2016 were included. A total of 102 patients with 198 lesions were included; 45 patients (44.1%) received repeated focal treatments at the pulmonary site for an oligorecurrent disease (the “multiple courses” group). Median follow-up was 22.5 months. The 3-year overall survival rates of patients who had a single treatment sequence (the “single course” group) versus the “multiple courses” were 73.9% and 78.8%, respectively, which was not a statistically significant difference (p = 0.860). The 3-year systemic therapy-free survival tended to be longer in the “multiple courses” group (50.4%) than in the “single course” group (44.7%) (p = 0.081). Tolerance of repeated treatments was excellent with only one grade 4 toxicity. Thereby, multimodality repeated ablative therapy is effective in patients with pulmonary oligorecurrent metastases. This strategy may delay the use of more toxic systemic therapy.
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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".