A Humeral Osteosarcoma Mimicking Osseous Leiomyosarcoma: A Case Report
Bibliographic record
Abstract
Osteosarcoma stands as one of the primary mesenchymal bone neoplasms commonly encountered in clinical practice. This malignancy often presents with a wide range of distinctive imaging characteristics. Here, we present a unique case wherein a delayed diagnosis of high-grade osteosarcoma occurred due to the absence of an osteoid matrix in the initial imaging studies. A 61-year-old female, initially presented with a left humeral fracture. As the healing of the fractured bone was delayed and the possibility of a pathologic fracture was considered, a CT-guided biopsy was performed. Histological examination of the biopsy sample initially suggested an osseous leiomyosarcoma. The lack of osteoid matrix on radiographs including aggressive intra-medullary mass seen on MRI, combined with the patient's age, appeared consistent with a diagnosis of leiomyosarcoma of bone. As a result, the initial diagnosis was not called into question. Due to neurovascular involvement, this led to a forequarter amputation. However, upon microscopic examination of the amputation specimen, certain areas exhibited features indicative of malignant osteoid deposition, ultimately supporting a revised diagnosis of high-grade osteosarcoma. This case underscores the critical importance of considering the limitations of core biopsy samples, especially when dealing with suspected limb masses associated with pathological fractures. Radiographs and CT scans can prove invaluable in ruling out subtle adjacent osteoid, and ultimately a multidisciplinary approach to the diagnosis of osteosarcoma is imperative to ensure accurate identification.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".