Clinical and molecular characterization of primary sclerosing epithelioid fibrosarcoma of bone and review of the literature
Bibliographic record
Abstract
Sclerosing epithelioid fibrosarcoma (SEF) is a rare sarcoma subtype characterized by monomorphic epithelioid cells embedded in a densely sclerotic collagenous matrix. The overwhelming majority of tumors arise in soft tissues; however, rare cases have been documented to occur primarily in bone. The hallmarks of soft tissue SEF include MUC4 immunoreactivity and the presence of an EWSR1-CREB3L1 fusion. Rare cases with alternative fusions have also been reported such as EWSR1-CREB3L2 and FUS-CREB3L2 transcripts. The molecular alterations of skeletal SEF have not been well-defined, with only rare cases analyzed to date. In this study we investigated the clinicopathologic and molecular features of seven patients presenting with primary osseous SEF. There were 3 males and 4 females, with a mean age at diagnosis of 38 years. All cases had microscopic features within the histologic spectrum of SEF and showed strong and diffuse MUC4 positivity, while lacking SATB2 expression. However, due to its unusual presentation within bone, four cases were initially misinterpreted as either osteosarcoma, Ewing sarcoma or chondroblastoma. Half of the patients with follow-up data developed metastasis. The cases were tested by targeted RNA sequencing, MSK-IMPACT, and/or fluorescence in situ hybridization, showing EWSR1-CREB3L1 in six cases and EWSR1-CREB3L2 in one case. The fusion transcripts were composed of EWSR1 exon 11 to either exon 6 of CREB3L1 or CREB3L2. In summary, due to their rarity in the bone, skeletal SEF are often misdiagnosed, resulting in inadequate treatment modalities. Similar to their soft tissue counterpart, bone SEF follow an aggressive clinical behavior and show similar EWSR1-CREB3L1/CREB3L2 fusions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".