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Computer-aided assessment of tumor grade for breast cancer in ultrasound images.

Chen DR, Chien CL, Kuo YF - Comput Math Methods Med (2015)

Bottom Line: In this study, 148 3-dimensional US images of malignant breast tumors were obtained.A support vector machine was developed to classify breast tumor grades as either low or high.The proposed CAD system achieved an accuracy of 85.14% (126/148), a sensitivity of 79.31% (23/29), a specificity of 86.55% (103/119), and an A Z of 0.7940.

View Article: PubMed Central - PubMed

Affiliation: Comprehensive Breast Cancer Center, Department of Medical Research, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua 50006, Taiwan.

ABSTRACT
This study involved developing a computer-aided diagnosis (CAD) system for discriminating the grades of breast cancer tumors in ultrasound (US) images. Histological tumor grades of breast cancer lesions are standard prognostic indicators. Tumor grade information enables physicians to determine appropriate treatments for their patients. US imaging is a noninvasive approach to breast cancer examination. In this study, 148 3-dimensional US images of malignant breast tumors were obtained. Textural, morphological, ellipsoid fitting, and posterior acoustic features were quantified to characterize the tumor masses. A support vector machine was developed to classify breast tumor grades as either low or high. The proposed CAD system achieved an accuracy of 85.14% (126/148), a sensitivity of 79.31% (23/29), a specificity of 86.55% (103/119), and an A Z of 0.7940.

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Sonographic A-view and C-view images of a tumor mass and its posterior region. The posterior region is the area under the tumor in the A-view image. The C-view image shows the section contour of the tumor lesion (external curve) and the section contour of the posterior regions (internal curve). The blue line in the C-view image indicates the plane of the A-view image. The green line in the A-view image indicates the plane of the C-view image.
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fig3: Sonographic A-view and C-view images of a tumor mass and its posterior region. The posterior region is the area under the tumor in the A-view image. The C-view image shows the section contour of the tumor lesion (external curve) and the section contour of the posterior regions (internal curve). The blue line in the C-view image indicates the plane of the A-view image. The green line in the A-view image indicates the plane of the C-view image.

Mentions: Posterior acoustic features [26–28] are characterized by the discrepancy in the gray levels of a voxel between a tumor mass and its corresponding posterior region (the region beneath the tumor in the A-view image in Figure 3). When acoustic enhancement occurs, the gray level of the posterior region is greater than the gray level of the lesion in ultrasound images [29]. Five posterior acoustic features were defined: the standard deviation of the gray levels in the posterior region Pstd, the ratio of the mean gray level in the posterior region to that in the tumor region PRm, the ratio of the gray level standard deviation in the posterior region to that in the tumor region PRstd, the difference between the gray level means of the posterior and tumor regions PSm, and the difference between the gray level standard deviations of the posterior and tumor regions PSstd. In this study, the section area (C-view image in Figure 3) of the posterior region was defined as two-thirds of the maximum tumor mass section area to avoid the edge-shadowing effect [26, 28]. The section area of the posterior region was derived using distance transform [30]. The height of the posterior region was defined as the tumor mass height and could not exceed 100 voxels [28].


Computer-aided assessment of tumor grade for breast cancer in ultrasound images.

Chen DR, Chien CL, Kuo YF - Comput Math Methods Med (2015)

Sonographic A-view and C-view images of a tumor mass and its posterior region. The posterior region is the area under the tumor in the A-view image. The C-view image shows the section contour of the tumor lesion (external curve) and the section contour of the posterior regions (internal curve). The blue line in the C-view image indicates the plane of the A-view image. The green line in the A-view image indicates the plane of the C-view image.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4355599&req=5

fig3: Sonographic A-view and C-view images of a tumor mass and its posterior region. The posterior region is the area under the tumor in the A-view image. The C-view image shows the section contour of the tumor lesion (external curve) and the section contour of the posterior regions (internal curve). The blue line in the C-view image indicates the plane of the A-view image. The green line in the A-view image indicates the plane of the C-view image.
Mentions: Posterior acoustic features [26–28] are characterized by the discrepancy in the gray levels of a voxel between a tumor mass and its corresponding posterior region (the region beneath the tumor in the A-view image in Figure 3). When acoustic enhancement occurs, the gray level of the posterior region is greater than the gray level of the lesion in ultrasound images [29]. Five posterior acoustic features were defined: the standard deviation of the gray levels in the posterior region Pstd, the ratio of the mean gray level in the posterior region to that in the tumor region PRm, the ratio of the gray level standard deviation in the posterior region to that in the tumor region PRstd, the difference between the gray level means of the posterior and tumor regions PSm, and the difference between the gray level standard deviations of the posterior and tumor regions PSstd. In this study, the section area (C-view image in Figure 3) of the posterior region was defined as two-thirds of the maximum tumor mass section area to avoid the edge-shadowing effect [26, 28]. The section area of the posterior region was derived using distance transform [30]. The height of the posterior region was defined as the tumor mass height and could not exceed 100 voxels [28].

Bottom Line: In this study, 148 3-dimensional US images of malignant breast tumors were obtained.A support vector machine was developed to classify breast tumor grades as either low or high.The proposed CAD system achieved an accuracy of 85.14% (126/148), a sensitivity of 79.31% (23/29), a specificity of 86.55% (103/119), and an A Z of 0.7940.

View Article: PubMed Central - PubMed

Affiliation: Comprehensive Breast Cancer Center, Department of Medical Research, Changhua Christian Hospital, 135 Nanhsiao Street, Changhua 50006, Taiwan.

ABSTRACT
This study involved developing a computer-aided diagnosis (CAD) system for discriminating the grades of breast cancer tumors in ultrasound (US) images. Histological tumor grades of breast cancer lesions are standard prognostic indicators. Tumor grade information enables physicians to determine appropriate treatments for their patients. US imaging is a noninvasive approach to breast cancer examination. In this study, 148 3-dimensional US images of malignant breast tumors were obtained. Textural, morphological, ellipsoid fitting, and posterior acoustic features were quantified to characterize the tumor masses. A support vector machine was developed to classify breast tumor grades as either low or high. The proposed CAD system achieved an accuracy of 85.14% (126/148), a sensitivity of 79.31% (23/29), a specificity of 86.55% (103/119), and an A Z of 0.7940.

Show MeSH
Related in: MedlinePlus