Clinical characteristics of nonosteogenic, non-Ewing’s sarcoma of the bone: Experience at the Toronto Sarcoma Program.
Bibliographic record
Abstract
11029 Background: Non-osteogenic sarcoma of the bone is a rare entity comprising a heterogenous group of malignant tumors. Clinical characteristics and outcome data are sparse in the literature. We evaluated the characteristics and long-term outcomes of patients (pts) with this disease. Methods: Pts with non-osteogenic sarcoma of the bone treated at the Toronto Sarcoma Program from 1987-2017 were identified from our institutional sarcoma database. Patient characteristics (ie: age, gender, tumor size, histology, grade, necrosis, tumor location), treatment modality (ie: surgical management, chemotherapy, radiotherapy), and survival information were collected. Survival was estimated by Kaplan-Meier (log-rank). Multi-variate analysis (MVA) was used to evaluate characteristics for sarcoma specific survival. Results: Of 130 pts identified, 106 had non-metastatic disease with a median age of 46 (range 18-89). Male-to-female predominance was 1.5:1. Common histologies were undifferentiated pleomorphic sarcoma (UPS; 42%), leiomyosarcoma (21%), and fibrosarcoma (11%). Tumors were generally high grade (59%) and > 5 cm in size (73%). The majority of pts received chemotherapy (68%), with Cisplatin/Doxorubicin based regimens (95%). R0 resection was achieved in 85% of cases. Survival for the entire cohort, showed a median (m)DFS of 8.13 years (95%CI:2.52-18.02), and a mOS of 11.72 (95%CI:7.00-not reached [NR]). Median sarcoma specific survival was NR, however 15- and 25-year survivals were 60.4% and 52.6% respectively. MVA demonstrated axial tumor location (HR = 13.03; p = 0.005), no chemotherapy (HR = 4.50; p = 0.017) and tumor grade (G2: HR = 36.21; p = 0.012; G3: HR = 20.30; p = 0.015) as risk factors for sarcoma specific death. Tumor size > 10cm (p = 0.085) and necrosis > 90% (p = 0.082) trended towards significance. Conclusions: Non-osteogenic sarcoma of the bone is a rare tumor entity, with a predominant UPS histology. Patient outcomes are reasonable, with measurable long-term survival. Axial tumor location, absence of chemotherapy, and high-grade disease predict for worse survival outcome. Further evaluation with larger data series is warranted to more fully understand this disease.
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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 | 0.001 |
| 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".