Perioperative Radiotherapy and Survival After Surgical Treatment of Nonmetastatic Retroperitoneal Sarcoma
Bibliographic record
Abstract
BACKGROUND: Among patients with nonmetastatic retroperitoneal sarcoma, the use of radiotherapy (RT) use and its association with survival following surgical treatment remain unknown. PATIENTS AND METHODS: Patients with surgically treated, nonmetastatic retroperitoneal well-differentiated, dedifferentiated liposarcoma, and leiomyosarcoma were identified within the SEER database (2004-2020). We used estimated annual percentage change (EAPC) analyses, multivariable logistic regression (LR) models, Kaplan-Meier survival plots, and multivariable Cox regression (MCR) models to evaluate treatment patterns and outcomes. RESULTS: Perioperative radiotherapy (RT) was delivered to 96 (16%) of 582 patients with well-differentiated liposarcoma, 211 (29%) of 739 patients with dedifferentiated liposarcoma, and 247 (35%) of 703 patients with leiomyosarcoma. Over time, the rate of perioperative RT use decreased from 27.3% to 5.4% for well-differentiated liposarcoma (EAPC, -3.5%; P=.02), but remained stable for dedifferentiated liposarcoma (P=.9) and leiomyosarcoma (P=.055). In multivariable LR, dedifferentiated liposarcoma (odds ratio [OR], 1.99; P<.001) and leiomyosarcoma (OR, 2.83; P<.001) independently predicted higher RT rates. In all 3 retroperitoneal sarcoma histologic subtypes, perioperative RT was not associated with lower cancer-specific mortality in either the Kaplan-Meier plots or the MCR models (all P>.05). CONCLUSIONS: The rate of perioperative RT use was lowest in well-differentiated retroperitoneal liposarcoma. Conversely, perioperative RT was used at approximately twice that rate in dedifferentiated liposarcoma and leiomyosarcoma. These findings are discordant with the most recent guideline recommendations but should be interpreted in the context of the historical nature of the investigated cohort, which predates the publication of those guidelines. Perioperative RT was not associated with differences in cancer-specific mortality across any of the 3 examined retroperitoneal sarcoma histologic subtypes. Unfortunately, local recurrence, surgical margin status, and metastatic progression rates could not be addressed.
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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 | 0.000 | 0.000 |
| 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".