Factors predicting outcome of G1/2 GEP NET after PRRT with Lu177-octreotate.
Bibliographic record
Abstract
e14565 Background: Outcome analyses of G1/2 NEN stage IV after peptide receptor radionuclide therapy (PRRT) are still limited, especially with regard to the impact of the Ki-67 index. This study aims to establish predictors of survival. Methods: Retrospective analysis of 74 consecutive GEP NET patients undergoing PRRT with 177Lu-octreotate. Patients had unresectable metastatic disease and a G1/2 grading (33 pancreatic, 41 non-pancreatic GEP- NET), documented morphologic or clinical progression within < 12 months and/or uncontrolled disease. Response (modified SWOG criteria) was correlated with potential impact factors: origin, function, burden, and uptake of tumor, age, Ki-67-index, Karnofsky score, baseline tumor marker levels. Predictors for survival were analyzed with Kaplan-Meier curves (log-rank test) and multivariate analysis (p<0.05). Results: The response rates were 36.5% PR, 17.6% MR, 35.1% SD, and 10.8% PD for the entire cohort, 54.5% PR, 18.2% MR, 18.2% SD, and 9.1% PD for pancreatic NET, and 22.0% PR, 17.1% MR, 48.8% SD, and 12.2% PD for non-pancreatic GEP-NET. The median progression-free (PFS) and overall (OS) survival was 26 months (95% CI, 18.3 - 33.7) and 55 months (95% CI, 48.8–61.2), respectively. The only factor associated with decreased PFS was a Ki-67 index >10% (p=0.002). For OS, besides the Ki-67 index, a Karnofsky score ≤70% and a baseline NSE of >15 ng/ml independently predicted shorter survival (each p<0.005, HR 3.0 - 3.4). Patients with a Ki-67 index >10% still had a median PFS and OS of 19 and 34 months, respectively. Conclusions: This study confirms the favorable outcome of G1/2 NET after PRRT. Independent predictors of survival are the Ki-67 index, the patient‘s performance status and the baseline NSE level. Nevertheless, patients with a Ki-67 >10% may still benefit from PRRT as demonstrated by the long-term outcome.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".