Doxorubicin and trabectedin for recurrent leiomyosarcoma – A case report
Bibliographic record
Abstract
Uterine leiomyosarcoma (LMS) represents a rare yet highly aggressive tumor, comprising approximately 1% of uterine malignancies. First-line regimens involving doxorubicin or gemcitabine and docetaxel demonstrate modest response rates. Notably, the combination of doxorubicin plus trabectedin has emerged as a preferred first-line option following the LMS-04 study, showing superior progression-free survival compared to doxorubicin alone. Second-line therapy for recurrent LMS poses greater challenges, with single-agent treatments exhibiting limited efficacy. Herein, we present a case of a 65-year-old woman with stage 1B uterine leiomyosarcoma, previously treated with surgical resection and adjuvant gemcitabine/docetaxel, due to surgical morcellation. Despite initially achieving disease-free status, she experienced a first recurrence 5 years later, treated with surgery and radiation, and a second recurrence 4 years after, necessitating second-line therapy with doxorubicin and trabectedin. The patient exhibited a remarkable response to this regimen, achieving partial response after 6 cycles of doxorubicin and trabectedin chemotherapy. She maintained stable disease over 13 cycles of maintenance trabectedin and 6 months off treatment, for a total of 16 months of progression-free survival. This case underscores the potential efficacy of combination chemotherapy with doxorubicin and trabectedin as a second-line treatment option for recurrent uterine leiomyosarcoma.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".