CXCR4-Targeted PET Imaging in Hematologic Malignancies
Bibliographic record
Abstract
PURPOSE: The aims of this study were to perform a comprehensive review and meta-analyses and to report pooled diagnostic results on CXCR4-targeted PET, particularly considering detection, visualization, and prognostication. PATIENTS AND METHODS: This study followed PRISMA-DTA. A systematic search was conducted on major medical literature databases up to March 1, 2024. The search strategy was designed to include CXCR4 PET studies in hematologic malignancies. A random-effects model combined sensitivity values derived from 2-by-2 contingency tables. Pooled means for SUV max were computed. Analyses were performed by R software. RESULTS: The initial search resulted in a total of 1428 studies. Ultimately, 18 were eligible for systematic review and meta-analytic calculations. Twelve studies (320 patients) included B-cell lymphoma. The pooled detection rate of CXCR4 PET was 99.4% (95% confidence interval [CI]: 88.3%-100%). Marginal zone lymphoma was investigated in 5 studies (209 patients), with a pooled sensitivity of 97.6% (95% CI: 79.7%-99.8%). In studies on central nervous system lymphoma, CXCR4 PET demonstrated 100% accuracy at both patient and lesion levels. Also, it demonstrated a significantly higher tumor-to-background ratio than 18 F-FDG PET. For multiple myeloma, 5 studies (116 patients) showed a patient-level pooled sensitivity of 77.8% (95% CI: 64.4%-87.2%), whereas 18 F-FDG PET had 65.0% (95% CI: 55.2%-73.7%). The pooled SUV max for CXCR4 PET was 13.6 (95% CI: 9.3-17.8) versus 9.0 (95% CI: 6.3-11.7) for 18 F-FDG PET. Additionally, CXCR4 PET-derived parameters were significant predictors of survival in multiple myeloma. CONCLUSIONS: CXCR4 PET can be a helpful imaging tool for evaluating hematologic malignancies, particularly in B-cell lymphoma and multiple myeloma patients. In specific clinical scenarios, it appears to be superior compared with the current standard-of-care imaging.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.019 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".