Stereotactic Ablative Radiotherapy for the Management of Liver Metastases from Neuroendocrine Neoplasms: A Preliminary Study
Bibliographic record
Abstract
INTRODUCTION: Liver metastases are common in patients with neuroendocrine neoplasms. The role of stereotactic ablative radiotherapy (SABR) is not well understood in this population. OBJECTIVE: The objective of this study was to evaluate the safety and efficacy of SABR in treating well-differentiated neuroendocrine liver metastases (WD-NELM). METHODS: A retrospective review of patients with WD-NELM treated with SABR was conducted between January 2015 and July 2019. Demographic, treatment, and clinical/radiographic follow-up data were abstracted. RECIST 1.1 criteria were applied to each individual target to evaluate the response to treatment. Local control (LC) and progression-free survival (PFS) were determined using the Kaplan-Meier methodology. Toxicity was reported according to the CTCAE v5.0. RESULTS: Twenty-five patients with a total of 53 liver metastases treated with SABR were identified. Most patients (68%) had midgut tumors, were grade 2 (80%), and had high-volume intrahepatic and/or extrahepatic disease (76%). The median number of liver metastases treated was 2, with a median size of 2.5 cm. The median radiation dose delivered was 50 Gy/5 fractions. The median follow-up was 14 months; 24 of the 25 patients were alive at the time of analysis. The objective response rate was 32%, with improvement or stability in 96% of lesions treated. The median time to best response was 9 months. The 1-year LC and PFS were 92 and 44%, respectively. No grade 3/4 acute or late toxicity was identified. CONCLUSIONS: Liver SABR is a safe and promising means of providing LC for WD-NELM. This treatment modality should be evaluated in selected patients in concert with strategies to manage systemic disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".