Contrasting the vascular response to sunitinib as measured by DCE-CT, DCE-MRI, and DCE-US.
Bibliographic record
Abstract
378 Background: Medical imaging (DCE-MRI, DCE-CT, DCE-US) provides localized information about the integrity and hemodynamics of the tumor microvasculature. Methods: 34 treatment (Rx) naive pts with mRCC and an abdominal tumor suitable for imaging received Sunitinib 50 mg on a 4wk-on/2wk-off schedule. DCE-US, DCE-CT, and DCE-MRI were done at baseline, during the first course of Rx and after 2wks off Rx. Imaging parameters obtained included vessel permeability (Ktrans), extracellular volume fraction (ve) and Ktrans/ve=Kep (by DCE-MRI), permeability surface product (PS, by DCE-CT), blood volume (BV, by DCE-CT and DCE-US), and blood flow (by DCE-US). We also developed a morphology parameter (MP) that relates the flow kinetics of an intravascular microbubble contrast agent to tumor vascular morphology using DCE-US. Results: A range of imaging parameters that predicted for progression free survival (PFS) were identified in responding pts (N = 26). Baseline imaging parameters correlated with PFS: Ktrans by DCE-MR (r = 0.53, p = 0.01, N = 24), BV by DCE-CT (r = 0.48, p = 0.02, N = 25) and disorganized vessel morphology (large MP) by DCE-US (Spearman r = 0.-0.45, p = 0.02, N = 24). Changes from baseline imaging parameters correlated with PFS: BV by DCE-US at 2 wks (r = -0.46, p = 0.02, N = 24), Kep by DCE-MR at 2 wks (r = -0.45, p = 0.03, N = 24) and MP at 1wk (r = 0.67, p = 0.02, N = 12). There was a correlation between imaging methods: BV measured by DCE-CT correlated with BV by DCE-US (r = 0.46, p = 0.03, N = 23) and Kep by DCE-MR (r = 0.59, p = 0.003, N = 22); Permeability measured by DCE-MR (Ktrans) and DCE-CT (PS) correlated at 2wks (r = 0.56, p = 0.01, N = 21). Conclusions: This is the first study to contrast DCE-US, DCE-CT, and DCE-MRI imaging in pts receiving antiangiogenic therapy. Baseline parameters for all three methods can predict for PFS. Changes from baseline in DCE-US and DCE-MRI parameters also predict for PFS. There is a correlation between imaging methods in parameters that measure BV and permeability. A novel DCE-US parameter was developed (MP) that quantifies the degree of tumor vascular disorganization. Baseline values in MP and changes during Rx correlate with PFS. Clinical trial information: OCT1205, SU Timing RCC.
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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 | 0.000 | 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".