Efficacy and Prognostic Factors for Y-90 Radioembolization (Y-90) in Metastatic Neuroendocrine Tumors with Liver Metastases
Bibliographic record
Abstract
Background. Yttrium-90 (Y-90) can be an effective liver-directed therapy for patients with metastatic neuroendocrine tumors (NETs), but population-based data are limited. We characterized the use of Y-90 in NET patients and identified factors associated with response. Methods. We identified 49 patients with metastatic liver-dominant NETs across BC Cancer’s six regional centres who received Y-90 between June 2011 and January 2017 in British Columbia, Canada. Baseline characteristics, radiographic responses, and outcomes were summarized. Results. Of the 49 patients who received Y-90, the median age was 56 years (range 21–78), 49% were male, and 94% had an ECOG performance status of 0–1. The primary location of the NET included pancreas (31%), small bowel (41%), large bowel (6%), unknown (14%), and others (12%). 69% of these patients had liver metastases alone, and tumors were graded as G1 (61%), G2 (25%), G3 (2%), and unknown (12%). Prior therapies included surgery (63%), local ablative therapy (25%), somatostatin analogue (69%), and systemic therapy (35%). The median Y-90 dose was 2.2 GBq (range 0.8–3.6), as SIR-spheres (69%) or TheraSpheres (29%). Median time to Y-90 from diagnosis of metastases measured 1.54 years. 88% received segmental Y-90, with 1 (69%), 2 (29%), and 3 (2%) treatments. Y-90 resulted in partial response (53%), stable disease (33%), and progressive disease (12%). Y-90 was well-tolerated, with infrequent grade 3-4 biochemical toxicities (2%) and grade 3 abdominal pain (6%). Longer overall survival (OS) was associated with resection of primary tumor, well-differentiated histology, and low Ki-67. Median OS was 27.2 months (95% CI 8.0–46.5).Conclusions. In our population-based cohort, Y-90 was well-tolerated in patients with metastatic liver-dominant NETs. Prior surgical resection was an important predictor of OS.
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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 | 0.001 | 0.002 |
| 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".