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IEEE Trans Biomed Eng ; 64(3): 661-670, 2017 03.
Article in English | MEDLINE | ID: mdl-28113214

ABSTRACT

OBJECTIVE: The role of angiogenesis in cancer growth has stimulated research aimed at noninvasive cancer detection by blood perfusion imaging. Recently, contrast ultrasound dispersion imaging was proposed as an alternative method for angiogenesis imaging. After the intravenous injection of an ultrasound-contrast-agent bolus, dispersion can be indirectly estimated from the local similarity between neighboring time-intensity curves (TICs) measured by ultrasound imaging. Up until now, only linear similarity measures have been investigated. Motivated by the promising results of this approach in prostate cancer (PCa), we developed a novel dispersion estimation method based on mutual information, thus including nonlinear similarity, to further improve its ability to localize PCa. METHODS: First, a simulation study was performed to establish the theoretical link between dispersion and mutual information. Next, the method's ability to localize PCa was validated in vivo in 23 patients (58 datasets) referred for radical prostatectomy by comparison with histology. RESULTS: A monotonic relationship between dispersion and mutual information was demonstrated. The in vivo study resulted in a receiver operating characteristic (ROC) curve area equal to 0.77, which was superior (p = 0.21-0.24) to that obtained by linear similarity measures (0.74-0.75) and (p <; 0.05) to that by conventional perfusion parameters (≤0.70). CONCLUSION: Mutual information between neighboring time-intensity curves can be used to indirectly estimate contrast dispersion and can lead to more accurate PCa localization. SIGNIFICANCE: An improved PCa localization method can possibly lead to better grading and staging of tumors, and support focal-treatment guidance. Moreover, future employment of the method in other types of angiogenic cancer can be considered.


Subject(s)
Image Interpretation, Computer-Assisted/methods , Neovascularization, Pathologic/diagnostic imaging , Neovascularization, Pathologic/pathology , Prostatic Neoplasms/diagnostic imaging , Prostatic Neoplasms/pathology , Ultrasonography/methods , Algorithms , Contrast Media , Humans , Male , Neovascularization, Pathologic/complications , Pattern Recognition, Automated/methods , Perfusion Imaging/methods , Prostatic Neoplasms/complications , Reproducibility of Results , Sensitivity and Specificity
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