Dynamic Contrast-enhanced CT to Evaluate Early Response in Neuroendocrine Liver Metastases Treated With Everolimus and Radiation
Bibliographic record
Abstract
BACKGROUND/AIM: The optimal method to evaluate response of neuroendocrine liver metastases (NELM) to radiation treatment (RT) is unknown; tumor perfusion parameters were evaluated by using dynamic contrast-enhanced computed tomography (DCE-CT) to correlate with efficacy in a prospective pilot study utilizing everolimus with radiotherapy for NELM. PATIENTS AND METHODS: Fourteen patients with progressive NELM received everolimus for 28 days prior to, concurrent with, and 14 days following radiation. Patients had a DCE-CT at baseline (t0), prior to radiation (t1) and 7 days after radiation (t2). Per lesion response was evaluated per standard response evaluation criteria (RECIST v1.1). Median statistics of the perfusion parameters were tabulated and included: blood flow (BF), blood volume (BV), and permeability (PS). Correlations between the parameters and the maximum percent change in size of the NELM at 12-months were explored. NELM not treated with radiation served as an internal control. RESULTS: Twenty-one treated NELM in 10 patients were evaluable. Compared to t0, BV increased at t1 (median 11%, range -15 to +37%, p=0.59), and then decreased significantly at t2 (median -8.4%, range -29 to +5.4%, p<0.03). A trend of increased BV in internal controls at each time point supports that the observed effect is due to radiation. Conventional objective response rate was 33%; no progression was seen within 12-months. CONCLUSION: Changes in DCE-CT were observed in patients receiving everolimus and radiation for NELM, with BV decreasing significantly following radiotherapy. Given the challenges in assessing response in NELM using traditional response evaluation criteria in any context, DCE-CT appears to be a promising modality.
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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) | 0.000 | 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".