Computer algorithm for analysing breast tumor angiogenesis using 3-D power Doppler ultrasound

Chang, Ruey-Feng; Huang, Sheng; Moon, Wookyung; Lee, Yu Hau; Chen, Dar Ren

doi:10.1016/j.ultrasmedbio.2006.05.029

Cited by 17 publications

(10 citation statements)

References 19 publications

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“…, neovascular thinning), which is a necessary process for extracting morphologic characteristics. Conversely, if T R is set too high, there is a risk that true low-intensity neovasculature information is filtered out (Chang, Huang 2006, Huang, Chang 2008). Based on this criterion a T R value of 50 was manually selected by an experienced researcher and applied to all image datasets used for this study.…”

Section: Methodsmentioning

confidence: 99%

“…The ability to visualize MB contrast agents flowing through tumorous tissue using dynamic contrast-enhanced US (DCE-US) yields an opportunity to quantify select image biomarkers associated with tumor angiogenesis (Saini and Hoyt 2014). While the evaluation of tumor perfusion is well detailed in the literature, quantitative analysis of neovascular morphology from DCE-US imaging data is a relatively new development (Chang, Huang 2006, Chang, Huang 2012, Chen, Wang 2014, Eisenbrey, Joshi 2011, Gessner, Aylward 2012, Huang, Chang 2008, Lai, Huang 2013, Molinari, Mantovani 2010). Early reports have indicated that neovascular features measured from US images ( e.g.…”

Section: Introductionmentioning

confidence: 99%

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Ultrasound Imaging of Breast Tumor Perfusion and Neovascular Morphology

Hoyt

Umphrey

Lockhart

et al. 2015

Ultrasound in Medicine & Biology

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In this paper, a novel image processing strategy is detailed for simultaneous measurement of tumor perfusion and neovascular morphology parameters from a sequence of dynamic contrast-enhanced ultrasound (DCE-US) images. After normalization and tumor segmentation, a global time-intensity curve describing contrast agent flow was analyzed to derive surrogate measures of tumor perfusion (i.e., peak intensity, time-to-peak intensity, area under the curve, wash-in rate, and wash-out rate). Further, a maximum intensity image was generated from these same segmented image sequences and each vascular component was skeletonized via a thinning algorithm. This skeletonized dataset and collection of vessel segments were then investigated to extract parameters related to the neovascular network and physical architecture (i.e., vessel-to-tissue ratio, number of bifurcations, vessel count, and average vessel length and tortuosity). An efficient computation of local perfusion parameters is also introduced and operates by averaging time-intensity curve data over each individual neovascular segment. Each skeletonized neovascular segment is then color-coded by these local measures to produce a parametric map detailing spatial properties of tumor perfusion. Longitudinal DCE-US image datasets were collected in 6 patients diagnosed with invasive breast cancer using a Philips iU22 ultrasound (US) system equipped with a L9-3 MHz transducer and Definity contrast agent. Patients were imaged using US before and after contrast agent dosing at baseline and again at weeks 6, 12, 18, and 24 after treatment started. Preliminary clinical results suggest that breast tumor response to neoadjuvant chemotherapy may be associated with temporal and spatial changes in DCE-US derived parametric measures of tumor perfusion. Moreover, changes in neovascular morphology parametric measures may also help identify any breast tumor response (or lack thereof) to systemic treatment. Breast cancer management from early detection to therapeutic monitoring is currently undergoing profound changes. The development of novel imaging techniques that are sensitive to the unique biological conditions of each individual tumor represent valuable tools in the pursuit of personalized medicine.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ultrasound Imaging of Breast Tumor Perfusion and Neovascular Morphology

Hoyt

Umphrey

Lockhart

et al. 2015

Ultrasound in Medicine & Biology

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“…Being disorganized, excessively branched, tumor-induced angiogenesis show different morphology from healthy vessel network [25]. Many morphological features, such as shape, diameter, branching pattern, and tortuosity, are statically proven to be useful features of vessels within malignant tumors [26,27].…”

Section: Methodsmentioning

confidence: 99%

Automatic identification of angiogenesis in double stained images of liver tissue

et al. 2009

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BackgroundTo grow beyond certain size and reach oxygen and other essential nutrients, solid tumors trigger angiogenesis (neovascularization) by secreting various growth factors. Based on this fact, several researches proposed that density of newly formed vessels correlate with tumor malignancy. Vessel density is known as a true prognostic indicator for several types of cancer. However, automated quantification of angiogenesis is still in its primitive stage, and deserves more intelligent methods by taking advantages accruing from novel computer algorithms.ResultsThe newly introduced characteristics of subimages performed well in identification of region-of-angiogenesis. The proposed technique was tested on 522 samples collected from two high-resolution tissues. Having 0.90 overall f-measure, the results obtained with Support Vector Machines show significant agreement between automated framework and manual assessment of microvessels.ConclusionThis study introduces a new framework to identify angiogenesis to measure microvessel density (MVD) in digitalized images of liver cancer tissues. The objective is to recognize all subimages having new vessel formations. In addition to region based characteristics, a set of morphological features are proposed to differentiate positive and negative incidences.

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“…However, vascularity is not usually considered by radiologists, mainly because evaluating and counting vessels using multiple views may be time consuming and a standardized quantification method of breast vessels does not exist [10]. Several studies have tried to address the automatic detection and extraction of the breast vascularity to improve the work-flow of radiologists [1,11,12], but only one of them has been conceived to work on DCE-MRI [13]. However, the last method, proposed by Lin et al and based on wavelet transform and the Hessian matrix, is affected by some limitations.…”

Section: Description Of Purposementioning

confidence: 99%