Molecular Imaging of Neuroendocrine Tumors by Somatostatin-Receptor Scintigraphy (SPECT/CT) with 99mTc-Tektrotyd
Bibliographic record
Abstract
Neuroendocrine tumors (NETs) are a heterogeneous group of neoplasms constituting about 0.5 % of all cancer cases. In recent years, there has been a significant increase in the incidence of NETs, which is primarily due to the active development and improvement of medical imaging technologies. Successful treatment and prognosis for patients with NETs strongly depend on the stage of the disease. One of the effective methods of visualization and staging NETs in nuclear medicine is somatostin receptor scintigraphy (SRS), which is based on the use of partial somatostatin receptor agonists labeled with radioactive isotopes. The article presents an analysis of 55 patients with NETs of various localizations who underwent scintigraphy and SPECT/CT. Radiopharmaceutical was used as a tracer for SRS. It was prepared on the basis of a lyophilisate developed by Polatom (Poland) — Tektrotyd, labeled with 99mTc. According to the results of the study SRS with 99mTc-Tektrotyd is informative in the topical diagnosis of NETs, especially when PET/CT scan with 68Ga-labeled peptides is not available. Sensitivity varies depending on the NET localization. It is necessary to continue researches on the diagnostic value of SRS with 99mTc-Tektrotyd for tumors, in the pathogenesis of which somatostatin receptors play a significant role.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".