Defining the role of neoadjuvant systemic therapy in high‐risk retroperitoneal sarcoma: A multi‐institutional study from the Transatlantic Australasian Retroperitoneal Sarcoma Working Group
Bibliographic record
Abstract
BACKGROUND: In patients with retroperitoneal sarcoma (RPS), the incidence of recurrence after surgery remains high. Novel treatment approaches are needed. This retrospective study evaluated patients with primary, high-risk RPS who received neoadjuvant systemic therapy followed by surgery to 1) determine the frequency and potential predictors of radiologic tumor responses and 2) assess clinical outcomes. METHODS: Clinicopathologic data were collected for eligible patients treated at 13 sarcoma referral centers from 2008 to 2018. Univariable and multivariable logistic models were performed to assess the association between clinical predictors and response. Overall survival (OS) and crude cumulative incidences of local recurrence and distant metastasis were compared. RESULTS: Data on 158 patients were analyzed. A median of 3 cycles of neoadjuvant systemic therapy (interquartile range, 2-4 cycles) were given. The regimens were mostly anthracycline based; however, there was significant heterogeneity. No patients demonstrated a complete response, 37 (23%) demonstrated a partial response (PR), 88 (56%) demonstrated stable disease, and 33 (21%) demonstrated progressive disease (PD) according to the Response Evaluation Criteria in Solid Tumors, version 1.1. Only a higher number of cycles given was positively associated with PR (P = .005). All patients underwent complete resection, regardless of the tumor response. Overall, patients whose tumors demonstrated PD before surgery showed markedly worse OS (P = .005). An indication of a better clinical outcome was seen in specific regimens given for grade 3 dedifferentiated liposarcoma and leiomyosarcoma. CONCLUSIONS: In patients with high-risk RPS, the response to neoadjuvant systemic therapy is fair overall. Disease progression on therapy may be used to predict survival after surgery. Subtype-specific regimens should be further validated.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".