Clinical outcomes of non‐osteogenic, non‐Ewing soft‐tissue sarcoma of bone––experience of the Toronto Sarcoma Program
Bibliographic record
Abstract
Non-osteogenic, non-Ewing soft-tissue sarcoma (NONE-STS) of bone is a rare presentation of primary bone cancers. Optimal treatments and outcomes for this heterogenous group are poorly described. We evaluated the factors associated with long-term outcomes in patients with this disease. Patients with localized NONE-STS of bone treated at the Toronto Sarcoma Program from 1987 to 2017 were identified. Clinical characteristics, treatment, and survival information were collected. Kaplan-Meier (log-rank) survival estimates from the time of definitive surgery, with uni-/multivariate analyses (Cox) of sarcoma-specific survival were performed. A total of 106 patients (60.4% male; median age 46 years) with NONE-STS of bone were identified. Common histologies included undifferentiated pleomorphic sarcoma [UPS]/malignant fibrous histiocytoma [MFH] (UPS/MFH, 41.5%), leiomyosarcoma (LMS, 20.8%), and fibrosarcoma (FS, 11.3%). Tumors were often high grade (59.4%) and involved the extremities (88.7%), with most receiving chemotherapy (67.9%) with cisplatin/doxorubicin-based regimens (73.6%). In the full cohort, 10-year DFS (45.7%, [95%CI: 35.7-55.8%]), OS (53.4%, [95%CI: 41.7-62.2%]), and SSS (63.9%, [95%CI: 53.9-72.5%]) were moderate. Histology specific, 10-year SSS was 70.7% [95%CI: 56.1-85.5%] for UPS/MFH, 51.8% [95%CI: 29.8-73.8%] for LMS, and 72.2% [95%CI: 45.1-99.2%] for FS. Only UPS/MFH (n = 4) showed sarcoma-related death >10 years. Multivariate analysis identified axial location (HR = 35.5, [95%CI: 3.4-369.6]), high grade (HR = 16.9, [95%CI: 1.6-185.1]), and disease relapse (HR = 485.1, [95%CI: 36.3-6482.6]) as risk factors for death (p < 0.05). Treatment with chemotherapy (HR = 0.1, [95%CI: 0.01-0.86]) and necrosis ≥85% (HR = 0.2, [95%CI: 0.04-0.99]) showed improved survival (p < 0.05). NONE-STS of bone has favorable long-term survival similar to osteosarcoma. Patients receiving chemotherapy derive benefit in retrospective analyses. UPS/MFH histologies show sarcoma-related death beyond 10 years. Further data on histologic subgroups are needed.
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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".