Quantifying Vascular Heterogeneity Using Microbubble Disruption-Replenishment Kinetics in Patients With Renal Cell Cancer
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».