6-l-<sup>18</sup>F-Fluorodihydroxyphenylalanine PET in Neuroendocrine Tumors: Basic Aspects and Emerging Clinical Applications
Bibliographic record
Abstract
In recent years, 6-l-18F-fluorodihydroxyphenylalanine (18F-DOPA) PET has emerged as a new diagnostic tool for the imaging of neuroendocrine tumors. This application is based on the unique property of neuroendocrine tumors to produce and secrete various substances, a process that requires the uptake of metabolic precursors, which leads to the uptake of 18F-DOPA. This nonsystematic review first describes basic aspects of 18F-DOPA imaging, including radiosynthesis, factors involved in tracer uptake, and various aspects of metabolism and imaging. Subsequently, this review provides an overview of current clinical applications in neuroendocrine tumors, including carcinoid tumors, pancreatic islet cell tumors, pheochromocytoma, paraganglioma, medullary thyroid cancer, hyperinsulinism, and various other clinical entities. The application of PET/CT in carcinoid tumors has unsurpassed sensitivity. In medullary thyroid cancer, pheochromocytoma, and hyperinsulinism, results are also excellent and contribute significantly to clinical management. In the remaining conditions, the initial experience with 18F-DOPA PET indicates that it seems to be less valuable, but further study is required.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".