Rare bone sarcomas: A retrospective analysis of 145 adult patients from the French Sarcoma Group
Bibliographic record
Abstract
The benefit of chemotherapy (CT) in rare bone sarcomas is not documented in prospective studies. Our retrospective study from the French sarcoma network for bone tumors ResOs was performed in adult patients (pts) from 1976 to 2014, with histologically verified diagnosis of leiomyosarcomas (LMS), undifferentiated pleomorphic sarcoma (UPS) or radiation-associated sarcomas of bone. The median follow-up was 4.7 years (95% CI: 3.7-6.5). Clinical features, treatment modalities and outcomes were recorded and analyzed from 145 pts (median age 53 years [range 20-87]). Site of disease was extremities (66%) or axial skeleton (34%), 111 (77%) presented with localized and potentially resectable disease. The most common histological subtypes were UPS (58%) and LMS (33%); 58% were high-grade tumors. Surgery was performed in 127 pts. In the 111 localized pts, 28 pts (25%) underwent upfront surgery or exclusive radiotherapy (RT; >50 Gy) without CT, whereas 83 pts (75%) received either neoadjuvant (n = 26) or adjuvant CT (n = 13) or both (n = 44). Neoadjuvant and adjuvant CT was mostly doxorubicin-based (95%/86%) and cisplatin-based (67%/63%). R0 resection was achieved in 59 pts, and a good histological response in 15 patients (25%). Adjuvant RT was performed in 24 (22%) pts. For the whole cohort (n = 145), the 5-year overall survival (OS) rate was 53% [42; 62]. In univariate analysis, age ≤ 60 was associated with a longer disease-free survival (DFS) (P = .0436). Neoadjuvant and adjuvant CT tended to be associated with better DFS (P = .056) with no significant impact on OS in this retrospective series.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".