Optimising Radioligand Therapy for Patients with Gastro-Entero-Pancreatic Neuroendocrine Tumours: Expert Opinion from an Italian Multidisciplinary Group
Bibliographic record
Abstract
Lu) oxodotreotide is an approved therapy in combination with somatostatin analogues (SSAs) for patients with advanced, well-differentiated G1-G2, gastro-entero-pancreatic neuroendocrine tumours (GEP-NETs) that progress on SSAs. We conducted a series of round table meetings throughout Italy to identify issues related to RLT delivery to patients with GEP-NETs. Four key issues were identified: (1) the proper definition of tumour progression prior to RLT initiation; (2) the impact of RLT in patients with bone metastases and/or high hepatic tumour burden; (3) the optimal follow-up protocol after RLT; and (4) organisational issues related to RLT use and managerial implications. This article reviews the literature relating to the aforementioned issues and makes recommendations based on available evidence and Italian NET experts' opinions. In particular, the group recommends the development of a diagnostic-therapeutic care pathway (DTCP) for patients undergoing RLT which provides systematic guidance but can still be individualised for each patient's clinical and psychosocial needs. A DTCP may clarify the diagnostic, therapeutic and post-treatment monitoring process, and improve communication and the coordination of care between hub and spoke centres. The DTCP may also contribute to changes in the care process related to the 2013/59/EURATOM Directive and to the definition of costs when planning for future or updated reimbursement of RLT in Italy.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| 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".