Accuracy of Histology and Malignancy Grade Between Preoperative Biopsy and Surgical Specimens in Primary Retroperitoneal Sarcoma. A Study From the Prospective Retroperitoneal Sarcoma Registry (RESAR)
Bibliographic record
Abstract
OBJECTIVE: This study aimed to prospectively assess the accuracy of preoperative biopsy in primary retroperitoneal sarcoma (RPS) across sarcoma referral centers. BACKGROUND: Histologic subtype and malignancy grade are key for guiding RPS treatment strategies. However, the accuracy of preoperative biopsy remains uncertain. METHODS: Data on adult patients with primary localized RPS who underwent preoperative biopsy followed by curative-intent surgery (2017-2020) were collected from the Retroperitoneal Sarcoma Registry. The study aimed to assess concordance between biopsy and surgical specimen histology and grade, using the Cohen kappa statistic. Concordance was also analyzed by center volume (high ≥13 vs. low <13 cases/year). RESULTS: Of 894 enrolled patients, histologic concordance was observed in 87.7% of cases (unweighted κ=0.814; 95% CI: 0.773-0.854). Among 172 tumors initially diagnosed as well-differentiated liposarcomas, 44 (25.6%) were reclassified as dedifferentiated liposarcomas. Grade concordance was observed in 232 of 346 cases (76.1%; weighted κ=0.652; 95% CI: 0.589-0.715), with no difference between computed tomography-guided and ultrasound-guided biopsies. Concordance by tumor grade was 98.9% (grade 1), 62.1% (grade 2), and 40.2% (grade 3). In dedifferentiated liposarcomas, grade concordance was 59.7% (weighted κ=0.385; 95% CI: 0.292-0.479). High-volume centers showed higher concordance for both histology (κ=0.780) and grade (κ=0.680) compared with low-volume centers (κ=0.622 and 0.564, respectively). CONCLUSIONS: Although preoperative biopsy for RPS provides satisfactory histologic accuracy, tumor grade is frequently underestimated. This diagnostic inaccuracy may impact treatment decisions, particularly regarding preoperative therapies. Incorporating additional diagnostic factors may improve the accuracy of preoperative assessment.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".