Quantifying Vascular Heterogeneity Using Microbubble Disruption-Replenishment Kinetics in Patients With Renal Cell Cancer
Bibliographic record
Abstract
PURPOSE: The purposes of this study were to establish the physiological interpretation of the shape parameter of the dynamic contrast-enhanced ultrasound (DCE-US) lognormal perfusion model and to evaluate the clinical significance of the parameter in a sample of patients undergoing antiangiogenic therapy for metastatic renal cell carcinoma (mRCC). MATERIALS AND METHODS: The physiological interpretation of the lognormal shape parameter was explored using computer simulations of disruption-replenishment in fractal models of the microcirculation generated by a piecewise iterative algorithm in MATLAB. Architectural variety was accomplished by introducing random perturbations to the diameter, length, and branching angles to the growing vascular tree. The shape parameter was extracted from the time-intensity curves and compared with the transit time distributions calculated directly from the simulations. Dynamic contrast-enhanced ultrasound data were obtained from 31 consenting patients with mRCC being treated with antiangiogenic therapy. Lognormal parameters related to the blood volume, mean flow speed, and vascular morphology/heterogeneity extracted before, during, and after therapy were correlated with progression-free survival (PFS). Cox proportional hazard ratios were calculated alongside receiver operator characteristics for different combinations of the vascular parameters to determine their ability to distinguish patients who would progress early (less than the median PFS) versus late (greater than the median PFS). RESULTS: The lognormal shape parameter correlated strongly to the width of the transit time distribution calculated directly from the simulations, and by extension, to the morphology/heterogeneity of the microvascular network (Spearman r = 0.80, P < 0.001, n = 28). Shorter time to progression was predicted by higher baseline heterogeneity (P = 0.003) and a reduction in tumor blood volume less than 43% (P = 0.002) after 2 weeks of treatment. Combining baseline parameters with changes that occur shortly after starting treatment increased the sensitivity and specificity of DCE-US to identify which patients would progress/resist therapy early versus late compared with when the vascular parameters were considered in isolation. CONCLUSIONS: The DCE-US shape parameter from the lognormal perfusion model is representative of microvascular morphology/heterogeneity and may be used to noninvasively characterize the vascular architecture of cancer lesions. A more abnormal flow distribution at baseline predicts for poorer outcome for patients treated with antiangiogenic therapy for metastatic renal cell cancer. Combining pretreatment and on-treatment measurements of vascularity can improve the performance of DCE-US to predict which patients will progress earlier versus later when on antiangiogenic therapy for mRCC.
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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 | 0.002 |
| 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) | 0.000 | 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 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".