Neoadjuvant 177Lu-DOTATATE for non-functioning pancreatic neuroendocrine tumours (NEOLUPANET): multicentre phase II study
Bibliographic record
Abstract
BACKGROUND: Resection of non-functioning pancreatic neuroendocrine tumours (NF-PanNETs) is curative in most patients. The potential benefits of neoadjuvant treatments have, however, never been explored. The primary aim of this study was to evaluate the safety of neoadjuvant 177Lu-labelled DOTA0-octreotate (177Lu-DOTATATE) followed by surgery in patients with NF-PanNETs. METHODS: NEOLUPANET was a multicentre, single-arm, phase II trial of patients with sporadic, resectable or potentially resectable NF-PanNETs at high-risk of recurrence; those with positive 68Ga-labelled DOTA PET were eligible. All patients were candidates for neoadjuvant 177Lu-DOTATATE followed by surgery. A sample size of 30 patients was calculated to test postoperative complication rates against predefined cut-offs. The primary endpoint was safety, reflected by postoperative morbidity and mortality within 90 days. Secondary endpoints included rate of objective radiological response and quality of life. RESULTS: From March 2020 to February 2023, 31 patients were enrolled, of whom 26 completed 4 cycles of 177Lu-DOTATATE. A partial radiological response was observed in 18 of 31 patients, and 13 patients had stable disease. Disease progression was not observed. Twenty-four R0 resections and 4 R1 resections were performed in 29 patients who underwent surgery. One tumour was unresectable owing to vascular involvement. There was no postoperative death. Postoperative complications occurred in 21 of 29 patients. Severe complications were observed in seven patients. Quality of life remained stable after 177Lu-DOTATATE and decreased after surgery. CONCLUSION: Neoadjuvant treatment with 177Lu-DOTATATE is safe and effective for patients with NF-PanNETs.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".