Sequential FDG-PET/CT as a Biomarker of Response to Sunitinib in Metastatic Clear Cell Renal Cancer
Bibliographic record
Abstract
PURPOSE: To test the hypothesis that sequential (18)F-fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) is a correlative marker in metastatic clear cell renal cancer (mRCC), patients were treated with sunitinib. Three sequential scans were conducted to determine whether the timing of the investigation was relevant. EXPERIMENTAL DESIGN: Forty-four untreated mRCC patients were enrolled into this prospective phase II study. (18)F-FDG-PET/CT scans were conducted before (n = 44) and after 4 weeks (n = 43) and 16 weeks (n = 40) of sunitinib given at standard doses. The primary endpoint was to correlate FDG-PET/CT response (20% reduction in SUV(max)) at 4 and 16 weeks with overall survival (OS). RESULTS: Forty-three (98%) patients had FDG-PET/CT avid lesions at diagnosis (median SUV(max) = 6.8, range: <2.5-18.4). In multivariate analysis, a high SUV(max) and an increased number of PET-positive lesions correlated with shorter OS [HR: 3.30 (95% CI: 1.36-8.45) and 3.67 (95% CI: 1.43-9.39), respectively]. After 4 weeks of sunitinib, a metabolic response occurred in 24 (57%) patients, but this did not correlate with progression-free survival (HR for responders = 0.87; 95% CI: 0.40-1.99) or OS (HR for responders = 0.80; 95% CI: 0.34-1.85). After 16 weeks of treatment, disease progression on FDG-PET/CT occurred in 28% (n = 12) patients which correlated with a decreased OS and PFS [HR = 5.96 (95% CI: 2.43-19.02) and HR = 12.13 (95% CI: 3.72-46.45), respectively]. CONCLUSIONS: Baseline FDG-PET/CT yields prognostic significant data. FDG-PET/CT responses occur in the majority of patients after 4 weeks of therapy; however, it is not until 16 weeks when the results become prognostically significant.
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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".