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 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.003 |
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; both teacher heads agree on what is shown here.
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".