A Comprehensive Review of Margin Identification Methods in Soft Tissue Sarcoma
Bibliographic record
Abstract
Soft tissue sarcomas (STS) are rare and heterogeneous tumors for which achieving complete tumor resection with negative surgical margins remains the cornerstone of curative treatment and a key predictor of survival. Current intraoperative resection margin status assessment techniques remain limited, as traditional intraoperative frozen section analysis is of limited accuracy for most STS histological subtypes. This comprehensive review evaluates current and emerging margin assessment techniques used intra-operatively during STS resection. A systematic search of PubMed and PubMed Central databases from 2000 to 2025 identified studies using fluorescence imaging, spectroscopy, and ultrasound-based modalities. Indocyanine green (ICG) fluorescence-guided surgery appeared to be the closest to widespread use, with the most clinical evidence showing potential to reduce positive margins. Use of acridine orange (AO) as a fluorescent dye also showed potential in decreasing local recurrences, but it remains in the experimental stage of research with little clinical data available. Raman spectroscopy has recently shown high accuracy in identifying STS from healthy tissue, but the impact of its use on patient outcomes has not been studied yet. Other techniques, such as diffuse reflectance spectroscopy (DRS), rapid evaporative ionization mass spectrometry (REIMS), optical coherence tomography (OCT), and intraoperative ultrasound (IOUS) yielded encouraging results but still require further prospective studies to validate their safety, reproducibility, and clinical utility in improving surgical precision and patient outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".