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Proceedings Paper

Predicting Ki67% expression from DCE-MR images of breast tumors using textural kinetic features in tumor habitats
Author(s): Baishali Chaudhury; Mu Zhou; Hamidreza Farhidzadeh; Dmitry B. Goldgof; Lawrence O. Hall; Robert A. Gatenby; Robert J. Gillies; Robert J. Weinfurtner; Jennifer S. Drukteinis
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Paper Abstract

The use of Ki67% expression, a cell proliferation marker, as a predictive and prognostic factor has been widely studied in the literature. Yet its usefulness is limited due to inconsistent cut off scores for Ki67% expression, subjective differences in its assessment in various studies, and spatial variation in expression, which makes it difficult to reproduce as a reliable independent prognostic factor. Previous studies have shown that there are significant spatial variations in Ki67% expression, which may limit its clinical prognostic utility after core biopsy. These variations are most evident when examining the periphery of the tumor vs. the core. To date, prediction of Ki67% expression from quantitative image analysis of DCE-MRI is very limited. This work presents a novel computer aided diagnosis framework to use textural kinetics to (i) predict the ratio of periphery Ki67% expression to core Ki67% expression, and (ii) predict Ki67% expression from individual tumor habitats. The pilot cohort consists of T1 weighted fat saturated DCE-MR images from 17 patients. Support vector regression with a radial basis function was used for predicting the Ki67% expression and ratios. The initial results show that texture features from individual tumor habitats are more predictive of the Ki67% expression ratio and spatial Ki67% expression than features from the whole tumor. The Ki67% expression ratio could be predicted with a root mean square error (RMSE) of 1.67%. Quantitative image analysis of DCE-MRI using textural kinetic habitats, has the potential to be used as a non-invasive method for predicting Ki67 percentage and ratio, thus more accurately reporting high KI-67 expression for patient prognosis.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97850T (24 March 2016); doi: 10.1117/12.2216075
Show Author Affiliations
Baishali Chaudhury, Univ. of South Florida (United States)
Mu Zhou, Univ. of South Florida (United States)
Hamidreza Farhidzadeh, Univ. of South Florida (United States)
Dmitry B. Goldgof, Univ. of South Florida (United States)
Lawrence O. Hall, Univ. of South Florida (United States)
Robert A. Gatenby, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert J. Gillies, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Robert J. Weinfurtner, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)
Jennifer S. Drukteinis, H. Lee Moffitt Cancer Ctr. & Research Institute (United States)


Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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