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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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A tumor mass (gray) and its optimally fitted ellipsoid (red).
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fig2: A tumor mass (gray) and its optimally fitted ellipsoid (red).

Mentions: Ellipsoid fitting features [24] depict the degree of similarity between a tumor mass and its optimally fitted ellipsoid (Figure 2). The optimally fitted ellipsoid can be regarded as the baseline against which the degree of shape irregularity of a tumor mass can be measured. Nine ellipsoid fitting features were quantified: axis ratio EA, surface ratio ES, volume covering ratio EV, number of regions outside the ellipsoid ERO, number of regions inside the ellipsoid ERI, number of total regions ER, number of regions with angularity outside the ellipsoid EROa, number of regions with angularity inside the ellipsoid ERIa, and number of total regions with angularity ERa. The parameter EV was defined as the ratio of the volume of the intersection between the tumor and the ellipsoid volume to the tumor volume; ER is the sum of ERO and ERI; and ERa is the sum of EROa and ERIa.


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

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

A tumor mass (gray) and its optimally fitted ellipsoid (red).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: A tumor mass (gray) and its optimally fitted ellipsoid (red).
Mentions: Ellipsoid fitting features [24] depict the degree of similarity between a tumor mass and its optimally fitted ellipsoid (Figure 2). The optimally fitted ellipsoid can be regarded as the baseline against which the degree of shape irregularity of a tumor mass can be measured. Nine ellipsoid fitting features were quantified: axis ratio EA, surface ratio ES, volume covering ratio EV, number of regions outside the ellipsoid ERO, number of regions inside the ellipsoid ERI, number of total regions ER, number of regions with angularity outside the ellipsoid EROa, number of regions with angularity inside the ellipsoid ERIa, and number of total regions with angularity ERa. The parameter EV was defined as the ratio of the volume of the intersection between the tumor and the ellipsoid volume to the tumor volume; ER is the sum of ERO and ERI; and ERa is the sum of EROa and ERIa.

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