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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".