Improving Diagnosis of Tumor-Induced Osteomalacia With Gallium-68 DOTATATE PET/CT
Bibliographic record
Abstract
CONTEXT: Tumor-induced osteomalacia (TIO) is a rarely diagnosed disorder presenting with bone pain, fractures, muscle weakness, and moderate-to-severe hypophosphatemia resulting from fibroblast growth factor 23-mediated renal phosphate wasting. Tumors secreting fibroblast growth factor 23 are often small and difficult to find with conventional imaging. OBJECTIVE: We studied the utility of (68)Ga-DOTA-octreotate (DOTATATE) somatostatin receptor positron emission tomography (PET)/computed tomography (CT) imaging in the diagnosis of TIO. DESIGN AND SETTING: A multicenter case series was conducted at tertiary referral hospitals. PATIENTS AND METHODS: Six patients with TIO diagnosed between 2003 and 2012 in Australia were referred for DOTATATE PET imaging. We reviewed the clinical history, biochemistry, imaging characteristics, histopathology, and clinical outcome of each patient. RESULTS: Each case demonstrated delayed diagnosis despite severe symptoms. DOTATATE PET/CT imaging demonstrated high uptake and localized the tumor with confidence in each case. After surgical excision, there was resolution of clinical symptoms and serum phosphate, except in one patient who demonstrated residual disease on PET/CT. All tumors demonstrated high somatostatin receptor subtype 2 cell surface receptor expression using immunohistochemistry. CONCLUSIONS: In patients with TIO, DOTATATE PET/CT can successfully localize phosphaturic mesenchymal tumors and may be a practical first step in functional imaging for this disorder. Serum phosphate should be measured routinely in patients with unexplained muscle weakness, bone pain, or stress fractures to allow earlier diagnosis of TIO.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".