The Effect of Surgical Resection on Cancer-Specific Mortality in Pelvic Soft Tissue Sarcoma According to Histologic Subtype and Stage
Bibliographic record
Abstract
Background/Objectives: The impact of surgical resection versus non-resection on cancer-specific mortality (CSM) in soft tissue pelvic sarcoma remains largely unclear, particularly when considering histologic subtypes such as liposarcoma, leiomyosarcoma, and sarcoma NOS. The objective of the present study was to first report data regarding the association between surgical resection status and CSM in soft tissue pelvic sarcoma. Methods: Using data from the Surveillance, Epidemiology, and End Results (SEER) database from 2000 to 2019, we identified 2491 patients diagnosed with pelvic soft tissue sarcoma. Cumulative incidence plots were used to illustrate CSM and other-cause mortality rates based on the histologic subtype and surgical resection status. Competing risk regression models were employed to assess whether surgical resection was an independent predictor of CSM in both non-metastatic and metastatic patients. Results: Among the 2491 patients with soft tissue pelvic sarcoma, liposarcoma was the most common subtype (41%), followed by leiomyosarcoma (39%) and sarcoma NOS (20%). Surgical resection rates were 92% for liposarcoma, 91% for leiomyosarcoma, and 58% for sarcoma NOS in non-metastatic patients, while for metastatic patients, the rates were 55%, 49%, and 23%, respectively. In non-metastatic patients who underwent surgical resection, five-year CSM rates by histologic subtype were 10% for liposarcoma, 32% for leiomyosarcoma, and 27% for sarcoma NOS. The multivariable competing risk regression analysis showed that surgical resection provided a protective effect across all histologic subtypes in non-metastatic patients (liposarcoma HR: 0.2, leiomyosarcoma HR: 0.5, sarcoma NOS HR: 0.4). In metastatic patients, surgical resection had a protective effect for those with leiomyosarcoma (HR: 0.6) but not for those with sarcoma NOS. An analysis for metastatic liposarcoma was not possible due to insufficient data. Conclusions: In non-metastatic soft tissue pelvic sarcoma, surgical resection may be linked to a reduction in CSM. However, in metastatic patients, this protective effect appears to be limited primarily to those with leiomyosarcoma.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".