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 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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.028 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".