Association of high proliferation marker Ki-67 expression with DCE-MR imaging features of breast: A large scale evaluation

被引:1
作者
Saha, Ashirbani [1 ,2 ]
Harowicz, Michael R. [1 ,2 ]
Grimm, Lars J. [1 ]
Kim, Connie E. [1 ]
Ghate, Sujata V. [1 ]
Walsh, Ruth [1 ]
Mazurowski, Maciej A. [1 ,2 ,3 ,4 ]
机构
[1] Duke Univ, Dept Radiol, Sch Med, Durham, NC 27710 USA
[2] Carl E Ravin Adv Imaging Labs, Durham, NC 27705 USA
[3] Duke Univ, Dept Elect & Comp Engn, Durham, NC USA
[4] Duke Univ, Med Phys Program, Durham, NC USA
来源
MEDICAL IMAGING 2018: COMPUTER-AIDED DIAGNOSIS | 2018年 / 10575卷
基金
美国国家卫生研究院;
关键词
Ki-67; expression; proliferation marker; MR imaging features; FREE SURVIVAL; CANCER; RADIOMICS; PREDICTION; RECURRENCE; TUMORS;
D O I
10.1117/12.2293207
中图分类号
O43 [光学];
学科分类号
070207 ; 0803 ;
摘要
One of the methods widely used to measure the proliferative activity of cells in breast cancer patients is the immunohistochemical (IHC) measurement of the percentage of cells stained for nuclear antigen Ki-67. Use of Ki-67 expression as a prognostic marker is still under investigation. However, numerous clinical studies have reported an association between a high Ki-67 and overall survival (OS) and disease free survival (DFS). On the other hand, to offer non-invasive alternative in determining Ki-67 expression, researchers have made recent attempts to study the association of Ki-67 expression with magnetic resonance (MR) imaging features of breast cancer in small cohorts (<30). Here, we present a large scale evaluation of the relationship between imaging features and Ki-67 score as: (a) we used a set of 450 invasive breast cancer patients, (b) we extracted a set of 529 imaging features of shape and enhancement from breast, tumor and fibroglandular tissue of the patients, (c) used a subset of patients as the training set to select features and trained a multivariate logistic regression model to predict high versus low Ki-67 values, and (d) we validated the performance of the trained model in an independent test set using the area-under the receiver operating characteristics (ROC) curve (AUC) of the values predicted. Our model was able to predict high versus low Ki-67 in the test set with an AUC of 0.67 (95% CI: 0.58-0.75, p<1.1e-04). Thus, a moderate strength of association of Ki-67 values and MR extracted imaging features was demonstrated in our experiments.
引用
收藏
页数:6
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