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Proceedings Paper

Breast cancer Ki67 expression preoperative discrimination by DCE-MRI radiomics features
Author(s): Wenjuan Ma; Yu Ji; Zhuanping Qin; Xinpeng Guo; Xiqi Jian; Peifang Liu
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Paper Abstract

To investigate whether quantitative radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) are associated with Ki67 expression of breast cancer. In this institutional review board approved retrospective study, we collected 377 cases Chinese women who were diagnosed with invasive breast cancer in 2015. This cohort included 53 low-Ki67 expression (Ki67 proliferation index less than 14%) and 324 cases with high-Ki67 expression (Ki67 proliferation index more than 14%). A binary-classification of low- vs. high- Ki67 expression was performed. A set of 52 quantitative radiomics features, including morphological, gray scale statistic, and texture features, were extracted from the segmented lesion area. Three most common machine learning classification methods, including Naive Bayes, k-Nearest Neighbor and support vector machine with Gaussian kernel, were employed for the classification and the least absolute shrink age and selection operator (LASSO) method was used to select most predictive features set for the classifiers. Classification performance was evaluated by the area under receiver operating characteristic curve (AUC), accuracy, sensitivity and specificity. The model that used Naive Bayes classification method achieved the best performance than the other two methods, yielding 0.773 AUC value, 0.757 accuracy, 0.777 sensitivity and 0.769 specificity. Our study showed that quantitative radiomics imaging features of breast tumor extracted from DCE-MRI are associated with breast cancer Ki67 expression. Future larger studies are needed in order to further evaluate the findings.

Paper Details

Date Published: 13 February 2018
PDF: 6 pages
Proc. SPIE 10487, Multimodal Biomedical Imaging XIII, 104870N (13 February 2018); doi: 10.1117/12.2286609
Show Author Affiliations
Wenjuan Ma, Tianjin Medical Univ. (China)
Tianjin's Clinical Research Ctr. for Cancer (China)
Yu Ji, Tianjin Medical Univ. (China)
Tianjin's Clinical Research Ctr. for Cancer (China)
Zhuanping Qin, Tianjin Univ. of Technology and Education (China)
Xinpeng Guo, Tianjin Medical Univ. (China)
Tianjin's Clinical Research Ctr. for Cancer (China)
Xiqi Jian, Tianjin Medical Univ. (China)
Peifang Liu, Tianjin Medical Univ. (China)
Tianjin's Clinical Research Ctr. for Cancer (China)


Published in SPIE Proceedings Vol. 10487:
Multimodal Biomedical Imaging XIII
Fred S. Azar; Xavier Intes, Editor(s)

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