Impact of time to relapse (TTR) and metastasectomy (MTS) on survival in leiomyosarcoma (LMS): A CanSaRCC study.
Bibliographic record
Abstract
11567 Background: Approximately 40% of LMS patients (pts) experience relapse despite standard care of surgical resection ± radiotherapy. Upon relapse, pts with advanced disease receive palliative systemic therapy. Often reserved for pts with oligometastatic disease, the role of MTS remains unclear due to a lack of randomized trials. Longer time from curative surgery to relapse (TTR) is associated with better outcomes in LMS pts who undergo MTS but its prognostic value has not been established in those treated without MTS. This study evaluates the effect of TTR on outcomes in LMS pts with metachronous metastases treated with systemic therapy ± surgery. Methods: This real-world study included advanced LMS pts treated at 4 Canadian sarcoma centers (2010–2022) who underwent curative resection, subsequent relapse, and systemic treatment ± MTS. Data were retrieved from the ethics-approved Canadian Sarcoma Research and Clinical Collaboration (CanSaRCC) database. Primary and secondary endpoints were overall survival (OS) stratified by TTR (< 6 vs ≥6 months from the completion of curative resection); and MTS, respectively. Exploratory analysis evaluated the impact of MTS in pts with low disease burden at relapse ( < 2 sites). Kaplan-Meier survival analysis and log-rank tests were used to compare OS, and Cox proportional hazards models identified independent predictors, with p < 0.05 considered significant. Results: A total of 113 pts (median age 56y) were included. Median follow-up was 38.4 months (mo). Majority (n = 93, 82%) were female and 38 (34%) had uterine leiomyosarcoma (uLMS). Relapse occurred in 109 pts (96%) with 73/109 pts (70%) having < 2 metastatic sites at relapse. TTR was < 6 months (TTR < 6) in 31/109 pts (28%) and 43/109 pts (39%) underwent MTS. Pts with TTR ≥6 months (TTR≥6) had significantly longer median OS (mOS) than those with TTR < 6 (32.9 vs 15.6mo, p = 0.006). Metastasectomy was associated with a significantly longer mOS in the whole cohort (50.4 vs 17.6mo, p < 0.0001) as well as the subgroup of patients with < 2 metastatic sites at relapse (50.6 vs 15.6mo, p < 0.0001). The mOS was 50.4 vs 24.7mo in pts with TTR≥6 who underwent MTS and those who did not, respectively (p < 0.0001). Among pts with TTR < 6 who had MTS, median OS was 44.8mo compared with 9.4mo in pts without surgery (p = 0.005). No other variables (e.g., tumor grade, primary site, metastatic burden, gender, or age) impacted OS. Both MTS (HR 0.26, 95% CI 0.16-0.49, p = 0.000) and TTR ≥ 6 (HR 0.55, 95% CI 0.33-0.91, p = 0.02) remained independent predictors of favorable OS in multivariate analysis. Conclusions: TTR≥6 was associated with longer OS in LMS pts. OS improved significantly with MTS, particularly in pts with longer TTR, though those with TTR < 6 also benefited to a lesser extent. These results underscore the role of MTS as part of an individualized treatment strategy to optimize outcomes in advanced LMS.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".