Spectrum of FDG PET/CT Findings in Burkitt Lymphoma
Bibliographic record
Abstract
PURPOSE: F-18 fluorodeoxyglucose (FDG) positron emission tomography (PET) is known to be a useful diagnostic tool for staging, restaging, and monitoring therapy for lymphoma. The purpose of this retrospective study is to present a spectrum of FDG PET findings at initial presentation of Burkitt lymphoma and subsequent findings after therapy. METHOD AND MATERIALS: We retrospectively reviewed 48 patients with Burkitt lymphoma referred for a total of 160 FDG PET/computed tomography (CT) scans at our institution. We characterized the disease distribution of Burkitt lymphoma in all patients and measured representative FDG activity from initial staging scans. Therapeutic response and disease remission were assessed in patients with PET/CT and clinical follow-up studies. RESULTS: Of the 48 patients diagnosed with Burkitt lymphoma, 25 patients had FDG PET/CT scans for initial staging. All untreated lesions of Burkitt lymphoma were highly FDG avid. The mean maximum standardized uptake value of 54 representative lesions is 16.5 (range: 6-54). Twelve patients were immune compromised. The majority of patients had disease localized to the abdomen and the pelvis. Extranodal involvement was identified in more than half of the patients studied. CONCLUSION: The American (or sporadic) form of Burkitt lymphoma presented with intense hypermetabolic lesions when untreated. The information is useful in evaluating post-treatment studies in the absence of a pretreatment scan.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".