Clinical, Radiological, and Pathological Features of Intraosseous Hibernoma: A Systematic Review of Case Reports and Case Series
Bibliographic record
Abstract
Intraosseous hibernoma (IOH) is a rare benign tumor composed of brown adipose tissue within the bone, frequently mimicking metastatic lesions and leading to diagnostic challenges. This systematic review aimed to consolidate and analyze all published IOH cases to improve recognition and inform management. A comprehensive literature search was conducted in PubMed, Web of Science, Scopus, Google Scholar, and the Cochrane Library from database inception to March 2025. Studies were eligible for inclusion if they reported histopathologically confirmed cases of intraosseous hibernoma (IOH) in human patients. A total of 62 cases from 30 studies were included. The mean age was 59.2 years, with a female predominance. Lesions were most frequently located in the pelvis and spine and were typically identified incidentally during cancer staging or imaging performed for unrelated indications. Imaging often revealed sclerotic patterns on computed tomography (CT), hyperintense signals on magnetic resonance imaging (MRI) T2-weighted and short tau inversion recovery (STIR) sequences, and mild to moderate uptake on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT). Immunohistochemistry consistently showed S100 protein positivity. Most patients underwent biopsy and were managed conservatively, with no cases of malignant transformation reported. IOH is a benign entity with distinctive radiologic and immunohistochemical features that may mimic malignancy. Awareness of its presentation can reduce misdiagnosis and unnecessary interventions, supporting biopsy-based confirmation and conservative management in most cases.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".