<sup>64</sup>Cu-DOTATATE somatostatin receptor imaging in neuroendocrine tumors:experience from 500 patients at Copenhagen ENETS Center of Excellence
Bibliographic record
Abstract
Background: In 2012 we introduced the somatostatin receptor imaging ligand 64Cu-DOTATATE. A potential benefit compared to SPECT tracers and 68Ga-labeled PET tracers included a better spatial image resolution. In addition, when compared to 68Ga-labeled tracers, the longer half-life of 64Cu (13h) compared to 68Ga (1h) could potentially make logistics easier, in particular in high-throughput centers. Here we present our experience having scanned more than 500 neuroendocrine tumor patients.<br/><br/>Methods: Description of performance and practical workflow based on the first 500 patients. Data summarized from both results obtained as part of our routine as well as from the clinical protocols for evaluation of diagnostic performance we have performed until now.<br/><br/>Results: The PET tracer 64Cu-DOTATATE is produced in batches for up to ten patient doses. These batches are released in the morning and the product has an approved shelf life of 24h. Accordingly, for practical purposes the patients may be scanned during the day and evening on the day of tracer production. Due to the long half-life, patients showing up late are no longer a major concern with regard to PET tracer use. Compared to 68Ga-labeled tracers, which we used previously and that typically are produced for 1-2 patients at a time, we have freed up radiochemist time at our department. Imaging is typically performed 1h after injection of approximately 200 MBq of 64Cu-DOTATATE but based on our first-in-human study, we have documented that image acquisition may be performed any time between 1 and 3h post injection. With regard to diagnostic performance, we have undertaken two head-to-head comparison studies with 111In-DTPA-octreotide and 68Ga-DOTATOC, respectively. On a lesion basis, 64Cu-DOTATATE was superior to both 111In-DTPA-octreotide and 68Ga-DOTATOC . Based on the first 112 patients, the sensitivity and specificity when using a composite standard of truth (CT only, follow up on imaging/biopsy) were 97% (CI: 91-99%) and 100% (CI: 96-100%), respectively. No major side-effects have been observed in the first 500 patients at our Center.<br/><br/>Conclusions: 64Cu-DOTATATE is a sensitive and convenient somatostatin receptor imaging tracer for routine use in a NET center.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.342 | 0.040 |
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".