Giant cell tumors of the spine: has denosumab changed the treatment paradigm?
Bibliographic record
Abstract
OBJECT Giant cell tumors (GCTs) of the spine are rare and complex to treat. They have a propensity for local recurrence and the potential to metastasize. Treatment is currently surgical and presents unique challenges due to the proximity of neural structures and the need for reconstruction. Denosumab has been shown in clinical trials to be an effective treatment for GCT, but has not yet been studied specifically in GCT of the spine or as a surgical adjunct. To the authors' knowledge this is the first such reported series. METHODS A multicenter, prospective series of 5 patients with GCT of the spine treated with denosumab were included. Patient demographic data, oncological history, neurological status, tumor staging, treatment details and adverse events, surgical procedure, complications, radiological and histological responses, and patient outcome were analyzed. RESULTS All patients were women, with a mean age of 38 years, and presented with pain; 2 patients had additional neurological signs and symptoms. The mean duration of symptoms was 62 weeks. No patient had a prior tumor or metastatic disease at presentation. All patients had Enneking Stage III tumors and were treated with monthly cycles of 120 mg of denosumab, with initial additional loading doses on Days 8 and 15. Patients were given daily supplements of calcium (500 mg) and vitamin D (400 IU). There were no denosumab-related adverse events. All patients had a radiological response to denosumab. One patient failed to have a histological response to denosumab, with > 90% of tumor cells found to be viable on histological investigation. CONCLUSIONS This study reports the early experience of using denosumab in the treatment of spinal GCT. The results demonstrate a clinically beneficial radiological response and an impressive histological response in most but not all patients. Further experience with denosumab and longer patient follow-up is required. Denosumab has the potential to change the treatment paradigm for spinal GCT.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".