Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

Sadeghi‐Naini, Ali; Suraweera, Harini; Tran, William T.; Hadizad, Farnoosh; Bruni, Giancarlo; Rastegar, Rashin F.; Curpen, Belinda; Czarnota, Gregory J.

doi:10.1038/s41598-017-13977-x

Cited by 50 publications

(46 citation statements)

References 60 publications

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“…These methods are used to estimate various physical properties of the tissue, such as the attenuation or scattering characteristics [ 41 ]. Quantitative US techniques can be used to create parametric maps that serve as an additional source of information on investigated tissue in comparison with standard B-mode images [ 42 , 43 ]. Those maps may serve as a more proper input to the CNN than regular B-mode images.…”

Section: Discussionmentioning

confidence: 99%

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

et al. 2018

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PurposeThe nonalcoholic fatty liver disease is the most common liver abnormality. Up to date, liver biopsy is the reference standard for direct liver steatosis quantification in hepatic tissue samples. In this paper we propose a neural network-based approach for nonalcoholic fatty liver disease assessment in ultrasound.MethodsWe used the Inception-ResNet-v2 deep convolutional neural network pre-trained on the ImageNet dataset to extract high-level features in liver B-mode ultrasound image sequences. The steatosis level of each liver was graded by wedge biopsy. The proposed approach was compared with the hepatorenal index technique and the gray-level co-occurrence matrix algorithm. After the feature extraction, we applied the support vector machine algorithm to classify images containing fatty liver. Based on liver biopsy, the fatty liver was defined to have more than 5% of hepatocytes with steatosis. Next, we used the features and the Lasso regression method to assess the steatosis level.ResultsThe area under the receiver operating characteristics curve obtained using the proposed approach was equal to 0.977, being higher than the one obtained with the hepatorenal index method, 0.959, and much higher than in the case of the gray-level co-occurrence matrix algorithm, 0.893. For regression the Spearman correlation coefficients between the steatosis level and the proposed approach, the hepatorenal index and the gray-level co-occurrence matrix algorithm were equal to 0.78, 0.80 and 0.39, respectively.ConclusionsThe proposed approach may help the sonographers automatically diagnose the amount of fat in the liver. The presented approach is efficient and in comparison with other methods does not require the sonographers to select the region of interest.

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Section: Discussionmentioning

confidence: 99%

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

et al. 2018

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“…Accurate diagnosis and characterization of breast lesions have an essential role in the management and improved prognosis of breast cancer [2,3]. CE-MRI is used nowadays as a problem-solving tool and as an adjunct to sonomammography in women at high risk or those with extremely dense breasts [4].…”

Section: Introductionmentioning

confidence: 99%

Can contrast-enhanced mammography replace dynamic contrast-enhanced MRI in the assessment of sonomammographic indeterminate breast lesions?

Kamal

Hanafy

Mansour

et al. 2020

Egypt J Radiol Nucl Med

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Background: Dynamic contrast-enhanced MRI of the breast has been used for several years in the assessment of indeterminate mammographic findings. Contrast-enhanced mammography is a relatively novel imaging technique that has shown comparable sensitivity and specificity to MRI. Contrast-enhanced mammography is a relatively easy feasible study with high sensitivity and low cost. Our aim was to assess the feasibility of replacing dynamic contrastenhanced (DCE)-MRI by contrast-enhanced mammography in the assessment of sonomammographic indeterminate lesions (BIRADS 3 and 4). Results: The study included 82 patients with 171 breast lesions. They all performed contrast-enhanced mammography and dynamic contrast-enhanced MRI. DCE-MRI sensitivity and NPV were significantly higher than those of contrast-enhanced mammogram (CEM). The overall accuracy of MRI was better than that of CEM; however, no statistically significant difference could be detected. Conclusion: Contrast-enhanced mammography and dynamic contrast-enhanced MRI improved the characterization of breast lesions. CEM showed slightly lower sensitivity and accuracy compared to MRI; however, because of being relatively easy, available, cheap, and acceptable by women, CEM can replace DC-MRI as a problem-solving tool in the characterization of indeterminate breast lesions.

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“…This method was first described by Haralick et al . 33 and now is often used in ultrasound tumour classification 25,34 .…”

Section: Introductionmentioning

confidence: 99%

Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue

Klimonda

Karwat

Dobruch‐Sobczak

et al. 2019

Sci Rep

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The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions.

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Breast-Lesion Characterization using Textural Features of Quantitative Ultrasound Parametric Maps

Cited by 50 publications

References 60 publications

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

Transfer learning with deep convolutional neural network for liver steatosis assessment in ultrasound images

Can contrast-enhanced mammography replace dynamic contrast-enhanced MRI in the assessment of sonomammographic indeterminate breast lesions?

Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue

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