TY - JOUR
T1 - Computer-aided diagnosis of contrast-enhanced spectral mammography
T2 - A feasibility study
AU - Patel, Bhavika K.
AU - Ranjbar, Sara
AU - Wu, Teresa
AU - Pockaj, Barbara A.
AU - Li, Jing
AU - Zhang, Nan
AU - Lobbes, Mark
AU - Zhang, Bin
AU - Mitchell, J. Ross
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2018/1
Y1 - 2018/1
N2 - Objective To evaluate whether the use of a computer-aided diagnosis–contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists. Materials and methods This IRB-approved retrospective study analyzed 50 lesions described on CESM from August 2014 to December 2015. Histopathologic analyses, used as the criterion standard, revealed 24 benign and 26 malignant lesions. An expert breast radiologist manually outlined lesion boundaries on the different views. A set of morphologic and textural features were then extracted from the low-energy and recombined images. Machine-learning algorithms with feature selection were used along with statistical analysis to reduce, select, and combine features. Selected features were then used to construct a predictive model using a support vector machine (SVM) classification method in a leave-one-out–cross-validation approach. The classification performance was compared against the diagnostic predictions of 2 breast radiologists with access to the same CESM cases. Results Based on the SVM classification, CAD-CESM correctly identified 45 of 50 lesions in the cohort, resulting in an overall accuracy of 90%. The detection rate for the malignant group was 88% (3 false-negative cases) and 92% for the benign group (2 false-positive cases). Compared with the model, radiologist 1 had an overall accuracy of 78% and a detection rate of 92% (2 false-negative cases) for the malignant group and 62% (10 false-positive cases) for the benign group. Radiologist 2 had an overall accuracy of 86% and a detection rate of 100% for the malignant group and 71% (8 false-positive cases) for the benign group. Conclusions The results of our feasibility study suggest that a CAD-CESM tool can provide complementary information to radiologists, mainly by reducing the number of false-positive findings.
AB - Objective To evaluate whether the use of a computer-aided diagnosis–contrast-enhanced spectral mammography (CAD-CESM) tool can further increase the diagnostic performance of CESM compared with that of experienced radiologists. Materials and methods This IRB-approved retrospective study analyzed 50 lesions described on CESM from August 2014 to December 2015. Histopathologic analyses, used as the criterion standard, revealed 24 benign and 26 malignant lesions. An expert breast radiologist manually outlined lesion boundaries on the different views. A set of morphologic and textural features were then extracted from the low-energy and recombined images. Machine-learning algorithms with feature selection were used along with statistical analysis to reduce, select, and combine features. Selected features were then used to construct a predictive model using a support vector machine (SVM) classification method in a leave-one-out–cross-validation approach. The classification performance was compared against the diagnostic predictions of 2 breast radiologists with access to the same CESM cases. Results Based on the SVM classification, CAD-CESM correctly identified 45 of 50 lesions in the cohort, resulting in an overall accuracy of 90%. The detection rate for the malignant group was 88% (3 false-negative cases) and 92% for the benign group (2 false-positive cases). Compared with the model, radiologist 1 had an overall accuracy of 78% and a detection rate of 92% (2 false-negative cases) for the malignant group and 62% (10 false-positive cases) for the benign group. Radiologist 2 had an overall accuracy of 86% and a detection rate of 100% for the malignant group and 71% (8 false-positive cases) for the benign group. Conclusions The results of our feasibility study suggest that a CAD-CESM tool can provide complementary information to radiologists, mainly by reducing the number of false-positive findings.
KW - Breast cancer
KW - Computer-aided diagnosis
KW - Contrast-enhanced digital mammography
KW - Contrast-enhanced spectral mammography
KW - Quantitative image analysis
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U2 - 10.1016/j.ejrad.2017.11.024
DO - 10.1016/j.ejrad.2017.11.024
M3 - Article
C2 - 29279165
AN - SCOPUS:85038874041
SN - 0720-048X
VL - 98
SP - 207
EP - 213
JO - European Journal of Radiology
JF - European Journal of Radiology
ER -