Quantifying vascular heterogeneity using microbubble disruption-replenishment kinetics in patients with renal cell cancer.
OBJECTIVE
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).


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.
From the *Department of Medical Biophysics, University of Toronto; †Imaging Research, Sunnybrook Research Institute; ‡Department of Physics, Ryerson University; §Medical Imaging, Sunnybrook Health Sciences Centre; ∥Department of Medical Imaging, Toronto General Hospital; and ¶Medical Oncology, Sunnybrook Odette Cancer Centre, Toronto, Ontario, Canada.
John M
JM
Hudson
Ross
R
Williams
Raffi
R
Karshafian
Laurent
L
Milot
Mostafa
M
Atri
Peter N
PN
Burns
Georg A
GA
Bjarnason
D000328:Adult; D000368:Aged; D020533:Angiogenesis Inhibitors / Q000627:therapeutic use; D002292:Carcinoma, Renal Cell / Q000000981:diagnostic imaging* / Q000188:drug therapy / Q000378:metabolism*; D003287:Contrast Media / Q000493:pharmacokinetics; D005260:Female; D005466:Fluorocarbons / Q000493:pharmacokinetics*; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D007211:Indoles / Q000627:therapeutic use; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging* / Q000188:drug therapy / Q000378:metabolism*; D007700:Kinetics; D008297:Male; D008657:Metabolic Clearance Rate; D008875:Middle Aged; D011758:Pyrroles / Q000627:therapeutic use; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D000077210:Sunitinib; D014463:Ultrasonography
D020533:Angiogenesis Inhibitors; D003287:Contrast Media; D005466:Fluorocarbons; D007211:Indoles; D011758:Pyrroles; D000077210:Sunitinib; C042852:perflutren