Single-Center Comparison of [64Cu]-DOTAGA-PSMA and [18F]-PSMA PET–CT for Imaging Prostate Cancer
Bibliographic record
Abstract
Introduction: the diagnostic performance of [64Cu]-DOTAGA-PSMA PET–CT imaging was compared retrospectively to [18F]-PSMA PET–CT in prostate cancer patients with recurrent disease and in the primary staging of selected patients with advanced local and possible metastatic disease. Methods: We retrospectively selected a total of 100 patients, who were consecutively examined in our department, with biochemical recurrence after radical prostatectomy or who had progressive local and possible metastatic disease in the last 3 months prior to this investigation. All patients were examined with a dedicated PET–CT scanner (Biograph; Siemens Healthineers). A total of 250 MBq (3.5 MBq per kg bodyweight, range 230–290 MBq) of [64Cu]-DOTAGA-PSMA or [18-F]-PSMA was applied intravenously. PET images were performed 1 h post-injection (skull base to mid-thigh). The maximum standardized uptake values (SUVmax) of PSMA-positive lesions and the mean standardized uptake value (SUVmean) of the right liver lobe were measured. Results: All but 9/50 of the patients (18%; PSA range: 0.01–0.7 µg/L) studied with [64Cu]-DOTAGA-PSMA and 6/50 of the ones (12%; PSA range: 0.01–4.2) studied with [18F]-PSMA had at least one positive PSMA lesion shown by PET–CT. The total number of lesions was higher with [64Cu]-DOTAGA-PSMA (209 vs. 191); however, the median number of lesions was one for [64Cu]-DOTAGA-PSMA and two for [18F]-PSMA. Interestingly, the median SUVmean of the right liver lobe was slightly higher for [18F]-PSMA (11.8 vs. 8.9). Conclusions: [64Cu]-DOTAGA-PSMA and [18F]-PSMA have comparable detection rates for the assessment of residual disease in patients with recurrent or primary progressive prostate cancer. The uptake in the liver is moderately different, and therefore at least the SUVs of the lesions in both studies would not be comparable.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".