Update on Systemic Therapy for Advanced Soft-Tissue Sarcoma
Bibliographic record
Abstract
Background: Soft-tissue sarcoma (sts) represents a rare group of mesenchymal neoplasms comprising more than 50 heterogeneous subtypes. Great efforts have been made to increase the understanding of the treatment of advanced sts (unresectable or metastatic disease). We set out to determine whether outcomes for patients with advanced sts have improved over time and to assess the current evidence for systemic therapy. Methods: In a scoping review, we evaluated the contemporary evidence for systemic treatment of advanced sts in adults (>18 years of age). Phase i, ii, and iii studies of systemic therapy for advanced sts published in the English language were included. After abstract and full-text review of seventy-seven studies, sixty-two trials met the inclusion criteria. Results: The number of clinical trials conducted and published in advanced sts has increased over the last 30 years. Although median overall survival has increased, attempts at improving first-line therapy through dose intensification, doublet chemotherapy, or alternative backbones have not been successful. The optimal therapy beyond anthracyclines remains a challenge, especially given the heterogeneity that grouping multiple sts subtypes within clinical trials creates. However, increasing numbers of agents are being studied, and several studies had shown isolated benefit in progression-free or overall survival. Summary: First-line systemic therapy with an anthracycline remains the standard of care for advanced sts. However, choice of subsequent therapy beyond anthracyclines remains challenging. Novel systemic therapies, use of molecular diagnostics to direct therapy, subtype-specific trials, and learnings from real-world retrospective data are all important for improving outcomes in patients with advanced sts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".