Quantitative Assessment of Cancer Vascular Architecture by Skeletonization of High-resolution 3-D Contrast-enhanced Ultrasound Images

The accurate characterization and description of the vascular network of a cancer lesion is of paramount importance in clinical practice and cancer research in order to improve diagnostic accuracy or to assess the effectiveness of a treatment. The aim of this study was to show the effectiveness of liposomes as an ultrasound contrast agent to describe the 3-D vascular architecture of a tumor. Eight C57BL/6 mice grafted with syngeneic B16-F10 murine melanoma cells were injected with a bolus of 1,2-Distearoyl-sn-glycero-3-phosphocoline (DSPC)-based non-targeted liposomes and with a bolus of microbubbles. 3-D contrast-enhanced images of the tumor lesions were acquired in three conditions: pre-contrast, after the injection of microbubbles, and after the injection of liposomes. By using a previously developed reconstruction and characterization image processing technique, we obtained the 3-D representation of the vascular architecture in these three conditions. Six descriptive parameters of these networks were also computed: the number of vascular trees (NT), the vascular density (VD), the number of branches, the 2-D curvature measure, the number of vascular flexes of the vessels, and the 3-D curvature. Results showed that all the vascular descriptors obtained by liposome-based images were statistically equal to those obtained by using microbubbles, except the VD which was found to be lower for liposome images. All the six descriptors computed in pre-contrast conditions had values that were statistically lower than those computed in presence of contrast, both for liposomes and microbubbles. Liposomes have already been used in cancer therapy for the selective ultrasound-mediated delivery of drugs. This work demonstrated their effectiveness also as vascular diagnostic contrast agents, therefore proving that liposomes can be used as efficient “theranostic” (i.e. therapeutic + diagnostic) ultrasound probes.

[1]  R. Pozzi Mucelli,et al.  Contrast-enhanced ultrasonography of the pancreas. , 2007, JOP : Journal of the pancreas.

[2]  U. Rajendra Acharya,et al.  Ovarian Tumor Characterization and Classification Using Ultrasound—A New Online Paradigm , 2013, Journal of Digital Imaging.

[3]  V. Patravale,et al.  The upcoming field of theranostic nanomedicine: an overview. , 2012, Journal of biomedical nanotechnology.

[4]  Fabian Kiessling,et al.  Liver dysplasia: US molecular imaging with targeted contrast agent enables early assessment. , 2013, Radiology.

[5]  K. Maruyama,et al.  Size-dependent extravasation and interstitial localization of polyethyleneglycol liposomes in solid tumor-bearing mice. , 1999, International journal of pharmaceutics.

[6]  J. Suri,et al.  Characterization of thyroid nodules by 3-D contrast-enhanced ultrasound imaging , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  D. Stippel,et al.  The laparoscopic approach for radiofrequency ablation of hepatocellular carcinoma—indication, technique and results , 2012, Langenbeck's Archives of Surgery.

[8]  Filippo Molinari,et al.  Contribution CHARACTERIZATION OF SINGLE THYROID NODULES BY CONTRAST-ENHANCED 3-D ULTRASOUND , 2010 .

[9]  A. Brunetti,et al.  Mice anesthesia, analgesia, and care, Part II: anesthetic considerations in preclinical imaging studies. , 2012, ILAR journal.

[10]  H. Hahn,et al.  Breast image registration and deformation modeling. , 2012, Critical reviews in biomedical engineering.

[11]  E. Jung,et al.  Evaluation of the microvascularization of pathologic parathyroid glands in patients with primary hyperparathyroidism using conventional ultrasound and contrast-enhanced ultrasound. , 2011, Clinical hemorheology and microcirculation.

[12]  Stephen Farrell,et al.  US Features of thyroid malignancy: pearls and pitfalls. , 2007, Radiographics : a review publication of the Radiological Society of North America, Inc.

[13]  Michel Schneider,et al.  Characteristics of SonoVue™ , 1999 .

[14]  U Rajendra Acharya,et al.  Ovarian Tumor Characterization using 3D Ultrasound , 2012, Technology in cancer research & treatment.

[15]  Hatem Fessi,et al.  Theranostic applications of nanoparticles in cancer. , 2012, Drug discovery today.

[16]  Milan Sonka,et al.  A Fully Parallel 3D Thinning Algorithm and Its Applications , 1996, Comput. Vis. Image Underst..

[17]  M. Mason,et al.  Drivers for change in the management of prostate cancer – Guidelines and new treatment techniques , 2012, BJU international.

[18]  M. Varras.  Benefits and limitations of ultrasonographic evaluation of uterine adnexal lesions in early detection of ovarian cancer. , 2004, Clinical and experimental obstetrics & gynecology.

[19]  U. Rajendra Acharya,et al.  ThyroScreen system: High resolution ultrasound thyroid image characterization into benign and malignant classes using novel combination of texture and discrete wavelet transform , 2012, Comput. Methods Programs Biomed..

[20]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[21]  Michel Schneider.  Characteristics of SonoVuetrade mark. , 1999, Echocardiography.

[22]  Fabian Kiessling,et al.  Molecular and functional ultrasound imaging in differently aggressive breast cancer xenografts using two novel ultrasound contrast agents (BR55 and BR38) , 2011, European Radiology.

[23]  Weimin Fan,et al.  Nanoparticles for tumor targeted therapies and their pharmacokinetics. , 2010, Current drug metabolism.

[24]  Shaoling Huang,et al.  Acoustic characterization of echogenic liposomes: frequency-dependent attenuation and backscatter. , 2011, The Journal of the Acoustical Society of America.

[25]  Jasjit S. Suri,et al.  Analysis of Tumor Vascularity Using Three-Dimensional Power Doppler Ultrasound Images , 2008, IEEE Transactions on Medical Imaging.

[26]  Gabriella Sanniti di Baja,et al.  Distance-Driven Skeletonization in Voxel Images , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[27]  U. Roelcke.  Imaging brain tumors with PET, SPECT, and ultrasonography. , 2012, Handbook of clinical neurology.

[28]  Attila Kuba,et al.  A 3D 6-subiteration thinning algorithm for extracting medial lines , 1998, Pattern Recognit. Lett..

[29]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[30]  Enzo Terreno,et al.  Image guided therapy: the advent of theranostic agents. , 2012, Journal of controlled release : official journal of the Controlled Release Society.

[31]  Jeon-Hor Chen,et al.  Diagnosis of Solid Breast Tumors Using Vessel Analysis in Three-Dimensional Power Doppler Ultrasound Images , 2013, Journal of Digital Imaging.

[32]  U Rajendra Acharya,et al.  Effect of complex wavelet transform filter on thyroid tumor classification in three-dimensional ultrasound , 2013, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[33]  J. Gorce,et al.  Influence of Bubble Size Distribution on the Echogenicity of Ultrasound Contrast Agents: A Study of SonoVue™ , 2000, Investigative radiology.

[34]  P. Giustetto,et al.  Release of a Paramagnetic Magnetic Resonance Imaging Agent from Liposomes Triggered by Low Intensity Non-Focused Ultrasound , 2013 .

[35]  J. Suri,et al.  Cost-Effective and Non-Invasive Automated Benign & Malignant Thyroid Lesion Classification in 3D Contrast-Enhanced Ultrasound Using Combination of Wavelets and Textures: A Class of ThyroScan™ Algorithms , 2011, Technology in cancer research & treatment.

[36]  Javier Portillo,et al.  Breadth-first search and its application to image processing problems , 2001, IEEE Trans. Image Process..

[37]  E. Terreno,et al.  In vivo MRI multicontrast kinetic analysis of the uptake and intracellular trafficking of paramagnetically labeled liposomes. , 2010, Journal of controlled release : official journal of the Controlled Release Society.