18F-FDG PET or PET/CT in detecting high-grade transformation of chronic lymphocytic leukaemia and indolent lymphomas: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: To evaluate the diagnostic accuracy of 18F-FDG positron emission tomography (PET) or PET/computed tomography (CT) in detecting histological transformation (HT) of indolent lymphomas. METHODS: A systematic search of articles up to July 2024 was performed in Embase and Medline. Eligible studies included adults with histologically proven indolent lymphoma, 18F-FDG PET or PET/CT as the index test, and sufficient data to assess diagnostic performance. Summary receiver operating characteristic curves were plotted using a bivariate model to estimate diagnostic accuracy with area under the curve (AUC). RESULTS: Fifteen studies with 1307 participants were included. Ten studies assessed PET ability to detect Richter's transformation, and 5 studies focused on HT in follicular lymphoma and other subtypes. A meta-analysis of the former showed pooled sensitivity of 0.90 (95% CI, 0.84-0.93) and specificity of 0.54 (95% CI, 0.28-0.77) when using a maximum standardized uptake value (SUVmax) threshold of around 5. AUC was 0.89. Pooled sensitivity was 0.74 (95% CI, 0.54-0.87), and specificity was 0.84 (95% CI, 0.67-0.93) when using an SUVmax threshold of around 10. Area under the curve was 0.84. For detecting HT in follicular lymphoma, thresholds were found higher than those for Richter's transformation. CONCLUSIONS: 18F-FDG PET or PET/CT demonstrates good diagnostic accuracy to detect Richter's transformation, best when employing SUVmax ≥ 5. SUVmax thresholds may be limited in discriminating follicular lymphoma from HT, and alternatives should be sought. ADVANCES IN KNOWLEDGE: If biopsy is feasible, SUVmax ≥ 5 can guide biopsy in patients with clinically suspicious Richter's transformation. If biopsy is infeasible, SUVmax ≥ 10 can better identify HT and guide patient management.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".