A review of pediatric neuroendocrine tumors, their detection, and treatment by radioisotopes
Bibliographic record
Abstract
Neuroendocrine tumors (NETs) are rare in childhood. Neuroblastoma is the most common pediatric extracranial solid tumor, occurring >90% in children younger than 5 years of age. Pheochromocytoma and paraganglioma are rare NETs, causing hypertension in 0.5-2% of hypertensive children. Gastroenteropancreatic NETs can occur in children and are classified into carcinoids and pancreatic tumors. Nuclear medicine procedures have an essential role both in the diagnosis and treatment of NETs. Metaiodobenzylguanidine (MIBG) labeled with radioiodine has a well-established role in diagnosis as well as therapeutic management of the neuroblastoma group of diseases. During recent decades, establishing the abundant expression of somatostatin receptors by NETs first led to scintigraphy with somatostatin analogs (i.e. Tc/In-octreotide) and, later, with the emergence of positron-emitting labeled agents (i.e. Ga-DOTATATE/DOTATOC/DOTANOC) PET scans with significantly higher detection efficiency became available. Therapy with somatostatin analogs labeled with beta emitters such as Lu-177 and Y-90, known as peptide receptor radionuclide therapy, is a promising new option in the management of patients with inoperable or metastasized NETs. In this article, pediatric NETs are briefly reviewed and the role of radioactive agents in the detection and treatment of these tumors is discussed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".