Preoperative IMRT for soft-tissue sarcoma of the extremities and trunk: low rate of wound complications
Bibliographic record
Abstract
Background: The Canadian prospective randomized NCIC SR2 trial tested the sequence of radiation and surgery for extremity soft-tissue sarcoma. The trial was conducted in the era before intensity-modulated radiation therapy (IMRT) was clinically available. Similar disease control after preoperative and postoperative non-IMRT was found. However, the preoperative non-IMRT arm showed significantly less (persisting) late-term effects but increased (transient) wound complication rates compared to the postoperative non-IMRT arm (35% vs. 17%, P = 0.01). Consequently based on these results, preoperative radiation therapy was considered the preferred approach. Currently IMRT, with its option for highly conformal dose distribution that translates into better normal tissue sparing, is used as the general standard for sarcoma radiation therapy in most patients. Our hypothesis was that a lower wound complication rate after preoperative radiation therapy might be achievable in the IMRT era. Methods: We prospectively assessed our preoperative IMRT cohort (n=67 consecutive patients) treated between March 2008 and March 2016 with respect to wound complication rates. Results: Fourteen of 67 (21%) externally referred patients with recurrent (n=1) or incompletely resected disease (n=13), and 53 treatment-naive patients underwent planned preoperative radiation after core biopsy. After mean/median 7.3/7 wk (3-12 wk), complete tumor resection was performed. Secondary revision was required in five of 67 (i.e., wound complication rate of 7%). Two local failures were observed so far. Conclusions: The presented results support our hypothesis that preoperative IMRT may lead to a reduced wound complication rate compared to that after postoperative and mainly preoperative non-IMRT techniques.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".