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Association of high proliferation marker Ki-67 expression with DCEMR imaging features of breast: a large scale evaluation
Author(s): Ashirbani Saha; Michael R. Harowicz; Lars J. Grimm; Connie E. Kim; Sujata V. Ghate; Ruth Walsh; Maciej A. Mazurowski
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Paper Abstract

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 MRextracted imaging features was demonstrated in our experiments.

Paper Details

Date Published: 27 February 2018
PDF: 6 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 1057507 (27 February 2018); doi: 10.1117/12.2293207
Show Author Affiliations
Ashirbani Saha, Duke Univ. School of Medicine (United States)
Carl E. Ravin Advanced Imaging Labs. (United States)
Michael R. Harowicz, Duke Univ. School of Medicine (United States)
Carl E. Ravin Advanced Imaging Labs. (United States)
Lars J. Grimm, Duke Univ. School of Medicine (United States)
Connie E. Kim, Duke Univ. School of Medicine (United States)
Sujata V. Ghate, Duke Univ. School of Medicine (United States)
Ruth Walsh, Duke Univ. School of Medicine (United States)
Maciej A. Mazurowski, Duke Univ. School of Medicine (United States)
Carl E. Ravin Advanced Imaging Labs. (United States)
Duke Univ. Medical Physics Program (United States)


Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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