Trabectedin for L-Type Sarcoma: A Retrospective Multicenter Study
Bibliographic record
Abstract
(1) Background: Metastatic L-type sarcomas (liposarcoma and leiomyosarcoma) are rare and have a poor prognosis. Trabectedin is an effective agent that can be used after anthracyclines. This study was designed to evaluate the real-life effectiveness and safety of trabectedin. (2) Methods: A retrospective multicenter study was conducted on patients who were treated with trabectedin for metastatic L-type sarcomas at ten tertiary oncology centers between 2015 and 2023. The objective response rate (ORR), disease control rate (DCR), time to treatment failure (TTF), and overall survival (OS) were evaluated in the cohort. Cox regression analysis was used to determine prognostic factors for survival. (3) Results: A total of 98 patients (52% liposarcoma and 48% leiomyosarcoma) were included in the study. The median treatment line was three (range: 1 to 6). Thirteen patients (13.3%) underwent local treatment due to oligoprogression, and dose reduction was required in seventeen patients (17.3%) due to toxicity. The ORR and DCR were 16% and 42%, respectively. The median TTF was 3 months, and the median OS was 10 months. In univariate analysis, a significantly longer median TTF was observed in patients who underwent local treatment (p = 0.008), obtained objective responses (p < 0.001), and underwent dose reduction (p = 0.002). No statistical differences were observed according to the histologic subtype and metastatic site. In the multivariate analysis for OS, it was found that obtaining an objective response was a good prognostic factor (p = 0.003), while the presence of liver metastases was associated with a poor prognosis (p = 0.016). (4) Conclusion: Trabectedin is a suitable option for L-type sarcoma after doxorubicin-based treatments. Survival was not worse in patients who underwent dose reduction. The use of local therapies simultaneously with trabectedin can be effective.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".