Brown tumor of the iliac crest initially misdiagnosed as a giant cell tumor of the bone
Bibliographic record
Abstract
SUMMARY: Brown tumors (BTs) are expansile osteolytic lesions complicating severe primary hyperparathyroidism (PHPT). Clinical, radiological and histological features of BTs share many similarities with other giant cell-containing lesions of the bone, which can make their diagnosis challenging. We report the case of a 32-year-old man in whom an aggressive osteolytic lesion of the iliac crest was initially diagnosed as a giant cell tumor by biopsy. The patient was scheduled for surgical curettage, with a course of neoadjuvant denosumab. Routine biochemical workup prior to denosumab administration incidentally revealed high serum calcium levels. The patient was diagnosed with PHPT and a parathyroid adenoma was identified. In light of these findings, histological slices of the iliac lesion were reviewed and diagnosis of a BT was confirmed. Follow-up CT-scans performed 2 and 7 months after parathyroidectomy showed regression and re-ossification of the bone lesion. The aim of this case report is to underline the importance of distinguishing BTs from other giant cell-containing lesions of the bone and to highlight the relevance of measuring serum calcium as part of the initial evaluation of osteolytic bone lesions. This can have a major impact on patients' management and can prevent unnecessary invasive surgical interventions. LEARNING POINTS: Although rare, brown tumors should always be considered in the differential diagnosis of osteolytic giant cell-containing bone lesions. Among giant cell-containing lesions of the bone, the main differential diagnoses of brown tumors are giant cell tumors and aneurysmal bone cysts. Clinical, radiological and histological characteristics can be non-discriminating between brown tumors and giant cell tumors. One of the best ways to distinguish these two diagnoses appears to be through biochemical workup. Differentiating brown tumors from giant cell tumors and aneurysmal bone cysts is crucial in order to ensure better patient care and prevent unnecessary morbid surgical interventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".